From 8c76ec1471cd6bca35d4d01b9c0cbecf3b701e6e Mon Sep 17 00:00:00 2001
From: Bingumalla Likith <138435877+binguliki@users.noreply.github.com>
Date: Mon, 3 Jun 2024 17:32:30 +0530
Subject: [PATCH 1/3] Added Saved Model

---
 .../GradientBoostingRegressor.pkl             | Bin 0 -> 1815717 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 Hedging with Real Estate/New_Upgrade_Bengaluru_House_Price_Predition/GradientBoostingRegressor.pkl

diff --git a/Hedging with Real Estate/New_Upgrade_Bengaluru_House_Price_Predition/GradientBoostingRegressor.pkl b/Hedging with Real Estate/New_Upgrade_Bengaluru_House_Price_Predition/GradientBoostingRegressor.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..6ce6ae822efab52d37dd3cbdc64b28216c73e63a
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HcmV?d00001


From e69cb6ce53db9b46a49da9e3976bd5555d6035a2 Mon Sep 17 00:00:00 2001
From: Bingumalla Likith <138435877+binguliki@users.noreply.github.com>
Date: Mon, 3 Jun 2024 17:33:08 +0530
Subject: [PATCH 2/3] Add Improved Models

---
 .../MoreModels.ipynb                          | 8630 +++++++++++++++++
 1 file changed, 8630 insertions(+)
 create mode 100644 Hedging with Real Estate/New_Upgrade_Bengaluru_House_Price_Predition/MoreModels.ipynb

diff --git a/Hedging with Real Estate/New_Upgrade_Bengaluru_House_Price_Predition/MoreModels.ipynb b/Hedging with Real Estate/New_Upgrade_Bengaluru_House_Price_Predition/MoreModels.ipynb
new file mode 100644
index 00000000..29964f9f
--- /dev/null
+++ b/Hedging with Real Estate/New_Upgrade_Bengaluru_House_Price_Predition/MoreModels.ipynb	
@@ -0,0 +1,8630 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.pipeline import Pipeline\n",
+    "import pickle\n",
+    "import numpy as np\n",
+    "\n",
+    "# Scraping the data preprocessing pipeline from the existing Pipeline\n",
+    "with open('PIPELINE.pkl' , 'rb') as file:\n",
+    "    pipeline = pickle.load(file)\n",
+    "with open('dataframe.pkl' , 'rb') as file:\n",
+    "    data = pickle.load(file)\n",
+    "\n",
+    "data_pipeline = Pipeline(pipeline.steps[:-1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;step1&#x27;,\n",
+       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                                   transformers=[(&#x27;col_tnf&#x27;,\n",
+       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
+       "                                                                sparse_output=False),\n",
+       "                                                  [0, 2])])),\n",
+       "                (&#x27;step2&#x27;, StandardScaler())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;step1&#x27;,\n",
+       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                                   transformers=[(&#x27;col_tnf&#x27;,\n",
+       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
+       "                                                                sparse_output=False),\n",
+       "                                                  [0, 2])])),\n",
+       "                (&#x27;step2&#x27;, StandardScaler())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;step1: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for step1: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                  transformers=[(&#x27;col_tnf&#x27;,\n",
+       "                                 OneHotEncoder(drop=&#x27;first&#x27;,\n",
+       "                                               sparse_output=False),\n",
+       "                                 [0, 2])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">col_tnf</label><div class=\"sk-toggleable__content fitted\"><pre>[0, 2]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, sparse_output=False)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">remainder</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;availability&#x27;, &#x27;total_sqft&#x27;, &#x27;bath&#x27;, &#x27;bhk&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">passthrough</label><div class=\"sk-toggleable__content fitted\"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('step1',\n",
+       "                 ColumnTransformer(remainder='passthrough',\n",
+       "                                   transformers=[('col_tnf',\n",
+       "                                                  OneHotEncoder(drop='first',\n",
+       "                                                                sparse_output=False),\n",
+       "                                                  [0, 2])])),\n",
+       "                ('step2', StandardScaler())])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 7651 entries, 0 to 10664\n",
+      "Data columns (total 7 columns):\n",
+      " #   Column        Non-Null Count  Dtype  \n",
+      "---  ------        --------------  -----  \n",
+      " 0   area_type     7651 non-null   object \n",
+      " 1   availability  7651 non-null   int64  \n",
+      " 2   location      7651 non-null   object \n",
+      " 3   total_sqft    7651 non-null   float64\n",
+      " 4   bath          7651 non-null   float64\n",
+      " 5   price         7651 non-null   float64\n",
+      " 6   bhk           7651 non-null   int32  \n",
+      "dtypes: float64(3), int32(1), int64(1), object(2)\n",
+      "memory usage: 448.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = data.drop(columns = ['price'])\n",
+    "Y = data['price']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import GradientBoostingRegressor , AdaBoostRegressor , StackingRegressor\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "from sklearn.model_selection import train_test_split , GridSearchCV , RandomizedSearchCV\n",
+    "\n",
+    "x_train , x_test , y_train , y_test = train_test_split(X , Y , shuffle = True , test_size = 0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 1176 candidates, totalling 5880 fits\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-47.027 total time=   0.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-47.426 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-48.581 total time=   0.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-49.476 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-51.104 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-47.331 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-47.979 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-49.854 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-48.490 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-47.679 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-48.269 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-50.282 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-49.113 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-47.874 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-48.998 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-50.646 total time=   0.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-47.737 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-47.932 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-48.804 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-46.554 total time=   0.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-47.384 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-51.388 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-47.792 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-48.987 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-47.376 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-47.600 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-50.936 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-48.515 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-48.145 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-47.335 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-47.629 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-49.033 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-51.758 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-47.643 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-46.991 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-43.134 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-44.723 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-47.921 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-45.516 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-43.048 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-45.768 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-43.109 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-48.420 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-46.118 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-44.608 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-45.248 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-46.878 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-45.033 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-45.867 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-45.036 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-43.724 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-46.721 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-48.213 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-44.819 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-44.943 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-46.269 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-44.929 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-48.054 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-46.757 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-44.384 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-44.637 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-45.367 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-43.408 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-45.974 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-45.522 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-44.389 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-44.358 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-47.715 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-45.476 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-44.775 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-42.292 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-40.214 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-43.914 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-42.545 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-42.712 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-42.079 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-43.719 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-44.275 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-40.960 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-43.431 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-43.453 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-44.939 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-43.028 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-43.192 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-43.354 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-43.500 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-45.802 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-42.777 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-41.519 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-41.497 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-41.530 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-43.517 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-43.521 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-42.250 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-45.237 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-42.514 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-44.287 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-43.913 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-43.856 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-43.117 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-42.329 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-43.407 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-42.672 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-44.286 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-42.309 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-38.506 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-39.499 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-41.835 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-40.497 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-42.928 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-40.710 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-40.324 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-42.019 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-40.445 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-39.040 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-39.290 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-39.584 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-41.582 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-39.474 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-40.175 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-39.998 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-39.135 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-42.849 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-39.986 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-38.788 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-37.408 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-41.330 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-42.653 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-41.051 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-37.679 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-38.798 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-40.407 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-39.850 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-41.750 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-41.049 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-42.316 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-40.928 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-46.847 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-44.840 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-47.150 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-46.159 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-45.009 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-45.310 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-47.040 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-42.413 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-41.787 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-48.243 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-45.953 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-38.908 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-44.477 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-45.223 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-45.954 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-48.669 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-47.135 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-46.065 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-44.547 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-47.838 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-49.400 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-45.807 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-44.746 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-45.970 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-46.234 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-48.149 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-45.446 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-45.300 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-43.881 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-46.597 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-48.193 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-45.715 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-45.672 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-47.385 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-43.958 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-47.410 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-45.077 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-45.287 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-40.306 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-42.056 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-40.712 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-40.468 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-42.520 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-39.983 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-40.622 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-43.673 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-41.245 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-41.127 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-40.778 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-40.608 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-41.602 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-42.659 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-41.863 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-40.389 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-41.722 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-44.767 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-41.092 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-40.953 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-41.823 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-41.055 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-42.963 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-39.943 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-40.593 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-39.454 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-40.509 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-41.306 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-40.557 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-42.891 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-39.212 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-40.891 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-43.363 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-40.398 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-39.566 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-37.665 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-36.710 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-39.068 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-39.139 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-36.586 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-38.428 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-37.951 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-39.686 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-36.351 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-35.356 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-36.285 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-36.896 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-39.296 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-37.909 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-35.915 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-35.890 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-36.770 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-37.648 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-39.054 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-36.641 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-35.612 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-39.401 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-36.963 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-35.993 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-36.519 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-38.676 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-37.474 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-38.508 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-37.521 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-36.552 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-36.746 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-39.044 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-40.368 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-36.409 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-36.942 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-34.232 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-35.038 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-36.936 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-35.160 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-33.710 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-33.990 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-37.210 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-35.313 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-33.978 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-33.894 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-32.286 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-35.236 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-37.003 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-34.544 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-35.178 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-35.362 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-32.768 total time=   0.9s[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-35.979 total time=   0.7s\n",
+      "\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-34.140 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-33.816 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-33.129 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-35.616 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-35.089 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-32.960 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-35.670 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-32.661 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-34.863 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-37.140 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-33.833 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-33.867 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-44.322 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-48.501 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-33.845 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-34.881 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-35.030 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-34.708 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-49.634 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-47.455 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-46.792 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-45.531 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-46.271 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-46.361 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-47.122 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-49.186 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-32.611 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-46.190 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-47.383 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-49.765 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-47.245 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-45.177 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-46.723 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-44.827 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-49.081 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-46.868 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-46.426 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-47.829 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-46.138 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-46.484 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-46.128 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-50.297 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-45.842 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-47.458 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-47.525 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-48.942 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-46.366 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-46.638 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-44.982 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-46.493 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-46.770 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-45.657 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-40.476 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-43.007 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-41.755 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-43.071 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-40.510 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-42.057 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-42.869 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-44.877 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-42.496 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-42.147 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-42.619 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-41.944 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-41.782 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-44.221 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-41.271 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-40.603 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-43.450 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-43.585 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-41.485 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-41.687 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-43.335 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-43.037 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-42.673 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-42.041 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-40.449 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-43.580 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-40.757 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-41.732 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-44.234 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-41.869 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-41.928 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-41.979 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-42.186 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-42.609 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-42.279 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-39.534 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-37.932 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-40.305 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-37.917 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-38.107 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-39.196 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-36.822 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-40.073 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-37.238 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-37.387 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-36.900 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-39.445 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-40.411 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-37.669 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-39.009 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-37.604 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-39.931 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-40.942 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-37.773 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-36.229 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-38.277 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-38.693 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-38.659 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-39.943 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-37.080 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-38.130 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-39.969 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-39.417 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-37.414 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-39.068 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-37.225 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-40.808 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-39.945 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-40.314 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-36.419 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-34.234 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-35.145 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-36.084 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-34.258 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-37.056 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-33.760 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-38.561 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-36.584 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-35.347 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-34.176 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-34.180 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-37.165 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-36.502 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-36.969 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-35.393 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-33.367 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-37.975 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-37.018 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-36.870 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-33.863 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-34.438 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-35.577 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-36.950 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-35.303 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-33.213 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-34.773 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-36.620 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-37.963 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-35.876 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-33.533 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-42.963 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-33.421 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-43.913 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-36.273 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-46.092 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-37.844 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-43.138 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-42.673 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-36.592 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-43.532 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-42.327 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-33.660 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-44.914 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-43.511 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-42.416 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-42.718 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-43.586 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-45.313 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-43.283 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-42.691 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-41.527 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-44.961 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-43.771 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-43.080 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-42.672 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-41.814 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-44.318 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-42.414 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-44.726 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-44.583 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-45.328 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-41.903 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-43.601 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-42.404 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-41.772 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-44.408 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-42.675 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-42.996 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-46.357 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-42.856 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-36.142 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-37.431 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-37.219 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-37.578 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-35.828 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-36.649 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-37.174 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-39.400 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-37.908 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-36.122 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-35.709 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-36.821 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-37.553 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-36.878 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-36.304 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-36.599 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-38.422 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-38.319 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-36.357 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-37.378 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-37.710 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-35.335 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-38.362 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-36.285 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-36.897 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-35.977 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-35.877 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-39.940 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-36.373 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-36.631 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-37.274 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-37.572 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-40.333 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-38.052 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-35.349 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-31.944 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-33.362 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-35.106 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-33.669 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-32.294 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-34.193 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-32.493 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-32.636 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-32.707 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-32.856 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-31.443 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-33.730 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-34.492 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-32.926 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-32.660 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-31.598 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-34.438 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-34.174 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-33.580 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-32.929 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-32.687 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-33.222 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-32.979 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-32.810 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-34.367 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-33.781 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-32.982 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-34.350 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-33.270 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-32.254 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-31.623 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-33.590 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-34.363 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-32.740 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-32.529 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-29.618 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-30.224 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-29.472 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-31.753 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-30.392 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-29.854 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-30.014 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-31.227 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-31.638 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-29.784 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-31.424 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-29.970 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-30.541 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-29.245 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-30.502 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-30.597 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-29.819 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-31.776 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-30.212 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-29.389 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-30.567 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-30.410 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-31.776 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-30.308 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-29.346 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-28.891 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-30.010 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-31.717 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-29.964 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-44.189 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-29.303 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-45.218 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-46.359 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-29.020 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-30.435 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-44.008 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-44.141 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-43.251 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-30.029 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-31.464 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-45.970 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-46.475 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-44.127 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-46.000 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-43.115 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-45.779 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-44.712 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-28.805 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-44.311 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-42.828 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-44.719 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-48.539 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-43.742 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-43.080 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-45.110 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-43.780 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-45.588 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-46.554 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-44.492 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-42.435 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-45.036 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-43.010 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-45.991 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-43.820 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-43.454 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-42.353 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-44.010 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-46.561 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-45.113 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-42.752 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-37.893 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-39.008 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-40.721 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-38.787 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-37.742 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-40.922 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-38.152 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-41.009 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-38.513 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-37.474 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-38.091 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-35.885 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-39.231 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-39.037 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-40.809 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-37.891 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-38.068 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-38.544 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-40.744 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-37.816 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-39.993 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-37.387 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-39.029 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-39.845 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-37.468 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-37.009 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-39.488 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-42.164 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-36.965 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-39.162 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-37.895 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-38.795 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-40.118 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-39.623 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-37.451 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-33.481 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-34.605 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-35.946 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-33.280 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-35.195 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-33.919 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-33.790 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-34.286 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-33.247 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-35.803 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-33.694 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-33.717 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-36.361 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-35.020 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-33.305 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-33.926 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-33.145 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-34.856 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-34.136 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-33.263 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-33.654 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-34.724 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-37.513 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-36.040 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-33.297 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-33.122 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-34.531 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-35.536 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-35.004 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-33.648 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-33.492 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-35.159 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-37.313 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-35.003 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-30.008 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-32.808 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-32.932 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-32.147 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-31.285 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-33.168 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-31.394 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-31.625 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-30.695 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-30.350 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-31.516 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-30.425 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-31.795 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-32.907 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-30.400 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-30.900 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-30.673 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-31.249 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-32.935 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-30.836 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-31.035 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-31.545 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-32.930 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-31.865 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-31.320 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-29.392 total time=   1.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-30.211 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-31.701 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-32.141 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-39.613 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-29.815 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-31.324 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-30.552 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-40.918 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-32.291 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-30.261 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-30.962 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-32.084 total time=   1.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-41.530 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-40.805 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-40.487 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-41.437 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-38.917 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-42.800 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-40.716 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-39.913 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-40.274 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-41.048 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-42.138 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-41.080 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-40.035 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-39.566 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-42.729 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-41.458 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-41.692 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-39.436 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-40.158 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-42.237 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-41.116 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-40.073 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-40.132 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-39.814 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-40.595 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-40.558 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-43.808 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-40.732 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-40.340 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-39.859 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-41.432 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-41.818 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-39.611 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-32.364 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-34.117 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-32.913 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-35.230 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-33.610 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-34.005 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-32.792 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-35.979 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-33.889 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-33.723 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-31.545 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-33.455 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-34.985 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-33.669 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-32.928 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-31.738 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-33.824 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-35.677 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-33.919 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-33.022 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-32.099 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-35.316 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-33.812 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-33.418 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-32.629 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-32.287 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-33.934 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-34.775 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-33.349 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-33.889 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-32.804 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-33.430 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-35.382 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-32.736 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-34.092 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-28.056 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-30.119 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-29.426 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-30.634 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-28.215 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-28.759 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-29.789 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-31.209 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-29.233 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-28.689 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-27.815 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-30.018 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-30.494 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-28.822 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-29.136 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-28.061 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-29.902 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-30.299 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-29.542 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-28.649 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-28.637 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-30.875 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-29.649 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-29.107 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-28.530 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-28.257 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-28.927 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-30.092 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-29.546 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-28.452 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-28.135 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-29.873 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-30.019 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-29.704 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-26.746 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-25.569 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-27.815 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-28.345 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-26.459 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-25.723 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-25.416 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-26.181 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-26.307 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-26.960 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-25.637 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-25.466 total time=   1.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-26.196 total time=   1.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-27.876 total time=   1.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-26.824 total time=   1.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-25.290 total time=   1.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-26.085 total time=   1.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-26.339 total time=   1.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-27.596 total time=   1.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-26.672 total time=   1.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-25.507 total time=   1.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-25.123 total time=   1.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-26.379 total time=   1.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-27.516 total time=   1.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-25.752 total time=   1.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-26.133 total time=   1.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-26.101 total time=   1.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-25.338 total time=   1.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-27.278 total time=   1.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-26.362 total time=   1.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-44.431 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-43.793 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-46.142 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-45.680 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-25.994 total time=   1.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-43.195 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-44.094 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-27.036 total time=   1.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-44.015 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-26.691 total time=   1.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-46.589 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-25.254 total time=   1.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-45.004 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-40.542 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-47.164 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-44.350 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-42.804 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-45.703 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-42.777 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-43.292 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-44.018 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-46.408 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-44.501 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-26.204 total time=   1.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-41.795 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-44.063 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-43.669 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-46.990 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-42.826 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-41.826 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-42.722 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.01, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-25.999 total time=   1.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-42.839 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-47.379 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-46.598 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-42.880 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-43.777 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-45.094 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-45.187 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-43.380 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-42.592 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-39.150 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-39.124 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-37.620 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-37.032 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-38.383 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-37.932 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-40.344 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-40.920 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-40.019 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-39.215 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-39.470 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-40.643 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-36.188 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-38.400 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-40.097 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-39.815 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-40.373 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-41.387 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-41.672 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-33.638 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-38.247 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-37.299 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-36.706 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-40.332 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-38.595 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-39.439 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-38.081 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-37.075 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-40.467 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-39.964 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-36.220 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-35.670 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-37.310 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-36.581 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-38.555 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-32.907 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-35.480 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-35.194 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-37.800 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-35.461 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-34.828 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-36.248 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-35.729 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-35.407 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-33.014 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-34.106 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-33.984 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-37.368 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-33.969 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-33.567 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-32.587 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-37.216 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-34.783 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-34.691 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-34.378 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-35.530 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-35.137 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-34.964 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-37.327 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-35.220 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-34.155 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-32.888 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-35.161 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-33.780 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-37.926 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-34.690 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-35.578 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-35.113 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-35.988 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-32.922 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-33.978 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-32.247 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-30.279 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-31.891 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-31.293 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-30.619 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-31.905 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-31.820 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-31.733 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-30.919 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-30.159 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-30.334 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-32.864 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-31.316 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-29.796 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-29.659 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-31.143 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-33.761 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-33.881 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-30.461 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-31.198 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-31.110 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-33.692 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-31.117 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-31.752 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-30.159 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-31.346 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-32.453 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-29.774 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-31.758 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-36.601 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-31.152 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-40.566 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-40.353 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-31.816 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-40.929 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-39.442 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-40.320 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-30.178 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-32.860 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-38.076 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-28.908 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-40.512 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-42.253 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-40.885 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-39.678 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-37.750 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-41.757 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-37.032 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-40.464 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-40.275 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-37.281 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-41.714 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-40.414 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-39.920 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-37.372 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-36.923 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-37.850 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-38.019 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-37.959 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-38.570 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-41.279 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-41.623 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-37.681 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-39.458 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-37.322 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-42.423 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-39.279 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-39.886 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-39.860 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-31.646 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-33.905 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-31.744 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-35.011 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-30.315 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-31.207 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-34.352 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-32.549 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-32.440 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-31.135 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-33.303 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-31.832 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-33.378 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-32.416 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-31.421 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-30.456 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-30.676 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-34.311 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-32.952 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-30.843 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-31.361 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-32.708 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-33.551 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-32.051 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-31.829 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-31.606 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-34.756 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-34.434 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-32.187 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-33.425 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-31.085 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-33.721 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-33.757 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-30.675 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-31.246 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-29.613 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-29.671 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-28.728 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-32.517 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-28.551 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-28.930 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-29.142 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-29.544 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-28.680 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-27.809 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-28.534 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-29.064 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-28.878 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-29.363 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-28.170 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-27.785 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-30.612 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-29.579 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-29.399 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-27.580 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-30.463 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-28.033 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-30.719 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-28.955 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-29.455 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-27.944 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-28.340 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-28.574 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-30.208 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-29.227 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-28.232 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-28.293 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-29.041 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-31.380 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-27.426 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-27.502 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-26.716 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-27.854 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-28.449 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-27.705 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-27.748 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-27.081 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-29.059 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-26.755 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-26.931 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-27.227 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-26.727 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-27.896 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-27.667 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-27.119 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-26.840 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-27.148 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-27.359 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-27.404 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-26.428 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-26.456 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-27.403 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-27.572 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-26.899 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-27.339 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-26.371 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-27.624 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-27.779 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-27.556 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-26.810 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-39.777 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-42.150 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-41.564 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-26.551 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-39.267 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-27.451 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-27.371 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-41.730 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-27.601 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-38.715 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-26.985 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-40.579 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-42.767 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-39.710 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-38.850 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-36.309 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-38.100 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-42.833 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-37.973 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-40.092 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-40.128 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-39.983 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-45.274 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-41.649 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-40.544 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-37.806 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-42.744 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-39.841 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-38.278 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-42.205 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-38.640 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-40.585 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-41.598 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-40.874 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-39.807 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-38.564 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-42.140 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-41.045 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-39.071 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-39.085 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-31.983 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-33.029 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-34.965 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-34.899 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-31.675 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-33.119 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-33.463 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-36.914 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-33.624 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-33.159 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-32.208 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-34.520 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-33.281 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-33.571 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-31.447 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-32.130 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-33.376 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-34.420 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-33.793 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-32.234 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-33.650 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-35.121 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-35.169 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-31.193 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-31.779 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-32.795 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-36.155 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-33.073 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-34.440 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-33.614 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-33.073 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-37.474 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-32.850 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-32.146 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-34.599 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-29.780 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-29.009 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-31.133 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-29.907 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-30.699 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-27.888 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-29.654 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-30.982 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-28.115 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-28.343 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-28.363 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-29.247 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-30.054 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-28.503 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-28.711 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-29.609 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-29.940 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-30.588 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-31.889 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-28.060 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-29.175 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-30.039 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-30.774 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-28.831 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-28.242 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-28.134 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-29.487 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-30.371 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-31.083 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-29.711 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-27.364 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-29.510 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-29.469 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-30.918 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-27.741 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-27.114 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-27.282 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-28.152 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-25.870 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-27.627 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-27.588 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-25.982 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-27.676 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-26.907 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-26.380 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-26.651 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-27.511 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-28.892 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-27.685 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-25.797 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-26.480 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-26.889 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-27.713 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-27.758 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-26.039 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-25.689 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-27.387 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-27.653 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-26.511 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-26.616 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-27.496 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-26.860 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-28.397 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-28.323 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-33.764 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-26.954 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-27.142 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-36.745 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-27.978 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-27.026 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-34.439 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-36.387 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-34.021 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-33.378 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-26.642 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-34.296 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-36.183 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-35.349 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-36.499 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-27.468 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-32.827 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-35.779 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-37.462 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-34.814 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-34.647 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-36.959 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-34.925 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-34.477 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-33.416 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-36.530 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-33.612 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-36.423 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-36.041 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-36.833 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-34.825 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-34.584 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-35.170 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-37.789 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-34.400 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-34.601 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-34.459 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-33.577 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-34.720 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-35.918 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-34.407 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-27.832 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-27.982 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-28.658 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-28.357 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-29.594 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-28.204 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-27.473 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-29.950 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-28.160 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-27.166 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-31.023 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-28.121 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-27.449 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-27.219 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-27.699 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-27.335 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-28.793 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-28.800 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-28.072 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-27.936 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-28.652 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-28.514 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-28.614 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-27.755 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-28.240 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-27.320 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-27.724 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-30.155 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-28.491 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-28.281 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-27.411 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-27.341 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-28.331 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-28.469 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-26.062 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-28.514 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-26.419 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-26.660 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-25.314 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-24.993 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-24.557 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-25.129 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-26.249 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-25.122 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-24.544 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-25.124 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-26.074 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-25.454 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-25.332 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-25.104 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-24.538 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-24.787 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-25.965 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-24.477 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-24.324 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-25.069 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-24.595 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-25.243 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-25.759 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-24.977 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-25.034 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-25.563 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-26.202 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-24.446 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-25.212 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-24.280 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-25.253 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-26.065 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-23.263 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-25.556 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-25.099 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-23.970 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-23.431 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-24.382 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-24.759 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-23.216 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-23.992 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-23.657 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-25.034 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-23.800 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-23.061 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-24.939 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-23.460 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-23.504 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-23.763 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-23.686 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-23.999 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-24.347 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-24.537 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-23.559 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-23.250 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-23.665 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-24.395 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-24.146 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-23.416 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-23.595 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-23.166 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-24.972 total time=   1.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-23.565 total time=   1.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-23.120 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-36.298 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-35.950 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-23.435 total time=   1.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-37.945 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-23.499 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-23.328 total time=   1.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-23.936 total time=   1.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-36.432 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-36.230 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-34.878 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-35.582 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-23.176 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-38.880 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-38.391 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-36.166 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-36.384 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-34.597 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-36.682 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-35.858 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-37.145 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-35.461 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-37.143 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-37.432 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-35.544 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-36.929 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-35.673 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-36.295 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-39.358 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-38.321 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-36.591 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-34.597 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-40.634 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-36.423 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-37.272 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-37.400 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-36.792 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-40.564 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-38.100 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-35.767 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-37.316 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-27.072 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-31.070 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-28.795 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-27.456 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-29.382 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-28.176 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-29.366 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-29.653 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-29.081 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-27.351 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-28.416 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-29.304 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-30.134 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-29.150 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-28.106 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-28.612 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-30.741 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-29.385 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-28.563 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-28.283 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-27.752 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-29.223 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-27.975 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-29.646 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-28.811 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-28.827 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-29.088 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-28.862 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-29.782 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-29.018 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-28.352 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-29.077 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-29.368 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-28.699 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-29.027 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-25.068 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-25.146 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-26.689 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-25.343 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-24.081 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-24.600 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-25.312 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-26.885 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-25.917 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-25.932 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-24.880 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-25.370 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-26.295 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-25.797 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-25.064 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-24.572 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-26.361 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-26.128 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-23.602 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-25.159 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-24.569 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-25.281 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-26.735 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-24.992 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-25.524 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-23.972 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-25.525 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-25.908 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-25.686 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-24.691 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-25.399 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-25.016 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-26.722 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-26.191 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-22.971 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-25.418 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-23.618 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-25.088 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-23.226 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-23.148 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-23.439 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-23.365 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-24.515 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-23.176 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-23.666 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-23.113 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-23.426 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-23.549 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-24.130 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-23.365 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-23.058 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-22.851 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-23.800 total time=   1.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-22.677 total time=   1.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-23.448 total time=   1.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-22.772 total time=   1.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-23.204 total time=   1.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-24.508 total time=   1.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-22.511 total time=   1.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-22.917 total time=   1.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-22.887 total time=   1.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-23.481 total time=   1.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-23.133 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-23.194 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-24.742 total time=   1.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-31.850 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-29.871 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-32.345 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-22.706 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-24.143 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-24.047 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-30.717 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-30.916 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-30.053 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-31.687 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-22.366 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-32.845 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-31.852 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-31.839 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-23.113 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-30.518 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-30.894 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-32.197 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-29.966 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-31.328 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-30.425 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-31.076 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-32.199 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-32.158 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-29.803 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-29.438 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-31.740 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-30.541 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-31.215 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-31.226 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-30.344 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-31.700 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-32.245 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-30.933 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-29.632 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-30.019 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-31.938 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-31.440 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-30.657 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-30.965 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-23.899 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-25.551 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-24.858 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-24.661 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-23.472 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-23.568 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-24.988 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-25.001 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-24.442 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-23.757 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-23.641 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-25.101 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-24.423 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-25.294 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-23.996 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-24.327 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-23.583 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-25.834 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-23.561 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-24.613 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-23.457 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-24.575 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-25.866 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-24.021 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-24.371 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-22.922 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-24.524 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-25.152 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-24.263 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-24.331 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-23.890 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-25.972 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-24.292 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-24.126 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-22.751 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-23.920 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-21.010 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-21.562 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-22.786 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-22.741 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-21.745 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-22.107 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-22.579 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-21.405 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-21.906 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-21.660 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-22.045 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-22.198 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-21.721 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-21.537 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-22.341 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-21.710 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-22.170 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-22.175 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-21.741 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-20.988 total time=   1.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-21.664 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-22.443 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-21.852 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-21.805 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-20.944 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-22.094 total time=   1.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-21.964 total time=   1.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-21.400 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-21.582 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-21.142 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-22.191 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-21.578 total time=   1.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-21.162 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-21.011 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-21.153 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-20.766 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-21.243 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-20.771 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-21.068 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-20.612 total time=   1.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-20.738 total time=   1.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-21.127 total time=   1.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-20.907 total time=   1.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-20.609 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-20.514 total time=   1.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-20.907 total time=   1.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-20.801 total time=   1.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-20.576 total time=   1.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-20.101 total time=   1.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-20.675 total time=   1.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-20.952 total time=   1.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-20.422 total time=   1.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-20.547 total time=   1.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-20.986 total time=   1.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-20.097 total time=   1.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-21.053 total time=   1.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-20.810 total time=   1.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-19.907 total time=   1.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-20.158 total time=   1.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-20.448 total time=   2.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-21.030 total time=   2.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-20.660 total time=   2.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-37.773 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-42.849 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-20.295 total time=   2.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-20.345 total time=   2.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-40.947 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-40.935 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-42.308 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-36.385 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-41.658 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-21.107 total time=   1.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-20.582 total time=   2.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-39.955 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-38.659 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-37.913 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-38.206 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-39.359 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-20.970 total time=   1.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-41.805 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-42.156 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-37.894 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-36.037 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-35.757 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-39.945 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-40.771 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-20.554 total time=   1.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-39.094 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-36.619 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-37.786 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-41.818 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-37.231 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.025, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-19.900 total time=   1.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-37.097 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-38.145 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-40.452 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-39.675 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-35.479 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-38.157 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-36.425 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-39.496 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-41.049 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-36.513 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-34.633 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-31.901 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-29.934 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-30.054 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-33.708 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-32.887 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-30.226 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-29.724 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-32.268 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-31.887 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-29.668 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-33.090 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-30.189 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-35.131 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-33.848 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-30.435 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-30.391 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-32.825 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-31.489 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-31.690 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-31.887 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-30.999 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-32.922 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-32.781 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-33.315 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-31.214 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-32.194 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-30.885 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-31.563 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-30.231 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-30.405 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-32.006 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-31.769 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-32.136 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-30.474 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-33.741 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-28.265 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-27.725 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-27.385 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-31.789 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-28.339 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-30.435 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-26.840 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-28.988 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-29.138 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-28.134 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-27.541 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-28.967 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-29.397 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-27.630 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-28.470 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-28.508 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-29.890 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-28.670 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-28.029 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-27.689 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-27.408 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-29.615 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-29.691 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-28.894 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-26.923 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-27.180 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-29.222 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-28.416 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-29.600 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-28.203 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-28.033 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-30.043 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-28.412 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-28.323 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-29.625 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-26.917 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-26.588 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-26.598 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-27.144 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-27.867 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-25.306 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-26.653 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-28.430 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-26.325 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-27.194 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-26.072 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-26.553 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-27.659 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-26.827 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-27.221 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-26.334 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-26.049 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-27.171 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-25.019 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-27.679 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-25.723 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-26.071 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-26.477 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-27.334 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-25.379 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-25.644 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-26.046 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-26.212 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-26.980 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-25.641 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-34.103 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-25.740 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-30.559 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-26.491 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-34.649 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-32.690 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-27.596 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-31.935 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-25.964 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-31.057 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-25.423 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-33.678 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-34.229 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-33.696 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-29.608 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-32.250 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-32.522 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-35.958 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-31.438 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-34.483 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-32.706 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-35.260 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-34.616 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-30.013 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-31.653 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-31.066 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-34.339 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-31.289 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-31.182 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-32.685 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-30.723 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-31.678 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-31.876 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-32.907 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-32.464 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-35.192 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-30.561 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-31.010 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-31.420 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-31.492 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-28.328 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-26.623 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-27.193 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-27.827 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-26.652 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-27.385 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-27.949 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-28.101 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-26.238 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-26.364 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-27.109 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-27.440 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-27.359 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-26.929 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-26.899 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-26.315 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-27.340 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-27.999 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-27.240 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-25.866 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-26.540 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-26.274 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-28.867 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-24.905 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-26.647 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-26.675 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-26.910 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-27.276 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-27.454 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-26.175 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-25.740 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-27.417 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-28.168 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-27.035 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-26.007 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-24.407 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-24.704 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-25.480 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-24.196 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-23.949 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-25.300 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-24.877 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-25.127 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-25.302 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-25.094 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-25.710 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-25.553 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-24.097 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-25.349 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-25.240 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-23.771 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-25.390 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-25.143 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-25.515 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-25.113 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-24.906 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-25.168 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-24.986 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-25.213 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-24.175 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-24.604 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-25.069 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-24.576 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-25.085 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-24.758 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-25.347 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-25.175 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-24.521 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-24.882 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-24.950 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-23.605 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-22.863 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-24.877 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-22.857 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-24.615 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-24.400 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-23.813 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-23.866 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-24.287 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-23.857 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-22.784 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-23.353 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-24.386 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-24.216 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-24.869 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-23.395 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-23.424 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-24.427 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-23.407 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-24.470 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-23.245 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-24.006 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-24.058 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-23.176 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-23.343 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-24.440 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-23.430 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-24.556 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-23.956 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-23.406 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-31.189 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-23.725 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-36.049 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-35.569 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-33.611 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-36.596 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-23.664 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-36.257 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-32.162 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-34.342 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-24.395 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-24.230 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-32.943 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-33.185 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-33.846 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-23.707 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-31.776 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-34.502 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-35.328 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-31.435 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-31.965 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-33.430 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-32.921 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-33.397 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-30.376 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-33.604 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-33.510 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-36.824 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-35.727 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-32.740 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-32.823 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-31.534 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-35.266 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-33.447 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-34.840 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-30.792 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-33.147 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-34.686 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-33.041 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-33.390 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-25.941 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-26.782 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-28.431 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-27.401 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-28.172 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-25.483 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-28.542 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-25.634 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-28.026 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-28.797 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-27.250 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-26.520 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-27.500 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-27.316 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-26.750 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-27.876 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-28.104 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-28.499 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-26.555 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-28.021 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-27.060 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-30.176 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-26.219 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-27.558 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-26.430 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-25.644 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-29.154 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-27.247 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-28.322 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-26.112 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-27.353 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-26.040 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-27.987 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-27.274 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-27.021 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-23.784 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-24.519 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-24.728 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-25.751 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-25.182 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-24.635 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-25.064 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-24.562 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-25.127 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-23.711 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-25.294 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-26.002 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-23.394 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-24.280 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-24.708 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-23.900 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-25.018 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-25.780 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-22.975 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-25.345 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-24.481 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-24.981 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-25.464 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-23.780 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-24.730 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-25.365 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-23.732 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-24.652 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-24.783 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-24.628 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-25.527 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-24.718 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-23.983 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-23.729 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-23.393 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-23.250 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-23.102 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-24.402 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-22.999 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-22.547 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-22.208 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-23.133 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-23.192 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-23.087 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-22.271 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-23.872 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-23.221 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-22.817 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-23.208 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-22.802 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-22.280 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-22.931 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-23.033 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-22.936 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-22.768 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-23.083 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-22.899 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-23.917 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-22.935 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-22.886 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-22.090 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-22.347 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-23.863 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-21.962 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-22.062 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-30.303 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-23.187 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-28.185 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-22.606 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-24.021 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-27.227 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-29.379 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-26.687 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-22.662 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-22.903 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-27.036 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-28.200 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-29.706 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-30.618 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-26.477 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-28.048 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-30.241 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-27.935 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-28.768 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-27.466 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-27.984 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-27.127 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-28.162 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-27.397 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-26.916 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-25.925 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-27.315 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-28.251 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-26.334 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-29.970 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-27.538 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-27.503 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-28.194 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-29.072 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-27.117 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-27.822 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-30.605 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-28.258 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-28.357 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-27.299 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-23.593 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-23.582 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-25.197 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-24.710 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-23.541 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-23.359 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-24.539 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-23.264 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-24.415 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-23.661 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-23.945 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-23.675 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-23.750 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-23.734 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-23.480 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-24.012 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-23.615 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-22.917 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-23.871 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-23.685 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-23.215 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-24.072 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-24.346 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-24.034 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-24.089 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-23.752 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-23.490 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-24.377 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-24.745 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-23.690 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-24.129 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-23.554 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-23.907 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-24.127 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-23.859 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-21.706 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-22.199 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-23.498 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-21.466 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-22.386 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-21.571 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-21.980 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-22.777 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-22.166 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-23.148 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-22.144 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-22.907 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-22.561 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-22.270 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-21.420 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-21.794 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-21.574 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-22.657 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-21.887 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-22.357 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-22.071 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-22.149 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-21.407 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-23.371 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-21.674 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-21.117 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-22.530 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-22.964 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-22.079 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-21.998 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-21.564 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-21.282 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-22.267 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-21.827 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-22.820 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-20.844 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-20.800 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-21.665 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-21.171 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-21.163 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-20.900 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-20.874 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-21.031 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-20.793 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-21.106 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-20.632 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-21.071 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-21.515 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-19.771 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-21.499 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-20.573 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-21.388 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-20.909 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-20.101 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-21.170 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-20.528 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-20.661 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-21.061 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-20.932 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-21.756 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-20.364 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-21.180 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-20.505 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-21.054 total time=   1.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-21.503 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-21.799 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-21.431 total time=   1.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-28.855 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-20.727 total time=   1.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-27.057 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-29.854 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-28.666 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-28.217 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-27.778 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-33.048 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-28.368 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-20.844 total time=   1.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-28.704 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-26.719 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-30.417 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-26.982 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-20.858 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-28.979 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-29.100 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-28.990 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-26.610 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-29.305 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-27.597 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-28.781 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-30.799 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-28.436 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-31.183 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-28.207 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-28.240 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-26.696 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-27.908 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-27.971 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-29.964 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-27.413 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-28.595 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-27.667 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-29.764 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-29.058 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-29.614 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-26.626 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-23.212 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-23.712 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-24.383 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-23.845 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-23.028 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-22.670 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-23.420 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-23.907 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-24.978 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-23.077 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-22.502 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-23.092 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-23.240 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-22.431 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-23.739 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-22.972 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-23.745 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-23.077 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-22.530 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-24.186 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-23.656 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-24.723 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-23.344 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-22.726 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-24.199 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-23.444 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-22.648 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-23.965 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-22.588 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-22.175 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-22.983 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-22.658 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-23.623 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-23.049 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-21.998 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-21.723 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-21.854 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-21.854 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-21.874 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-21.852 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-21.035 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-21.710 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-21.298 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-21.532 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-21.286 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-21.360 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-21.171 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-21.414 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-20.946 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-21.396 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-21.717 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-21.499 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-22.406 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-20.793 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-21.208 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-21.275 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-21.152 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-21.077 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-21.074 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-21.406 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-19.709 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-21.548 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-21.069 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-20.937 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-20.868 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-20.652 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-21.632 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-21.158 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-20.738 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-20.785 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-19.586 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-20.151 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-20.311 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-19.861 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-19.837 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-20.797 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-20.951 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-19.608 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-19.218 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-20.342 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-20.260 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-20.547 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-20.719 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-19.563 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-19.761 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-19.813 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-19.850 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-20.093 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-19.458 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-20.240 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-20.090 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-19.985 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-20.157 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-19.732 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-19.872 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-18.976 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-19.792 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-19.241 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-20.250 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-23.303 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-25.236 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-20.683 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-19.872 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-19.513 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-20.774 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-25.793 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-20.133 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-24.641 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-24.333 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-20.283 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-25.225 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-24.423 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-25.106 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-23.958 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-23.284 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-23.179 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-24.025 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-25.050 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-24.564 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-23.675 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-25.743 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-24.524 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-24.574 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-25.137 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-24.106 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-23.794 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-25.036 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-24.236 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-24.442 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-25.285 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-23.794 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-25.093 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-25.782 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-24.599 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-24.287 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-24.566 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-23.413 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-24.782 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-24.078 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-24.967 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-21.126 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-20.785 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-21.688 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-19.998 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-20.622 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-20.830 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-21.180 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-20.815 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-20.585 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-20.557 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-20.620 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-20.586 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-20.496 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-20.285 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-21.542 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-20.049 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-21.257 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-20.279 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-21.505 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-19.986 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-20.978 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-20.637 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-19.882 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-19.945 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-20.891 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-20.118 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-21.225 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-21.530 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-19.943 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-19.500 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-20.460 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-20.823 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-20.362 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-19.857 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-20.101 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-18.540 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-19.556 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-20.260 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-19.240 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-19.907 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-18.725 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-19.182 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-19.679 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-19.387 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-19.256 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-18.439 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-19.514 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-19.688 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-18.482 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-18.742 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-19.129 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-18.770 total time=   1.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-19.393 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-19.263 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-18.638 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-18.836 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-19.590 total time=   1.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-19.409 total time=   1.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-18.640 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-19.157 total time=   1.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-18.943 total time=   1.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-19.635 total time=   1.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-20.071 total time=   1.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-18.668 total time=   1.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-18.362 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-19.151 total time=   1.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-19.649 total time=   1.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-18.604 total time=   1.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-19.517 total time=   1.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-17.885 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-18.798 total time=   1.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-18.595 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-18.843 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-17.912 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-18.208 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-17.776 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-18.617 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-19.228 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-18.204 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-18.150 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-17.641 total time=   1.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-18.752 total time=   1.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-18.714 total time=   1.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-17.985 total time=   1.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-17.716 total time=   1.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-18.415 total time=   1.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-18.614 total time=   1.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-19.095 total time=   1.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-18.132 total time=   1.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-18.028 total time=   1.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-17.526 total time=   1.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-18.619 total time=   1.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-18.762 total time=   1.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-18.272 total time=   1.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-17.673 total time=   1.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-18.477 total time=   1.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-18.722 total time=   1.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-18.731 total time=   1.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-35.993 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-17.695 total time=   1.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-37.533 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-35.663 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-35.710 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-32.685 total time=   0.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-18.552 total time=   1.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-33.240 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-33.000 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-38.806 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-17.742 total time=   1.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-18.708 total time=   1.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-33.889 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-37.061 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-34.835 total time=   0.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-18.526 total time=   1.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-17.756 total time=   1.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-37.056 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-33.296 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-33.923 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-32.930 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-33.521 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-36.795 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-37.672 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-33.233 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.05, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-18.458 total time=   1.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-36.821 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-35.205 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-39.580 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-38.542 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-34.307 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-33.223 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-34.475 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-32.212 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-35.111 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-37.597 total time=   0.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-33.598 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-35.183 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-32.272 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-34.962 total time=   0.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-33.699 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-35.640 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-29.341 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-28.803 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-33.833 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-27.239 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-29.704 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-28.000 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-30.412 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-28.798 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-29.069 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-29.277 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-27.337 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-27.837 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-28.378 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-26.862 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-29.911 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-28.535 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-30.188 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-30.071 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-27.221 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-30.400 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-28.382 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-28.630 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-29.994 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-28.876 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-26.652 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-26.780 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-30.472 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-28.503 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-29.435 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-27.301 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-27.009 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-29.135 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-29.516 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-29.415 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-27.737 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-25.903 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-25.455 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-28.007 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-25.036 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-25.480 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-25.138 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-26.366 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-26.489 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-26.985 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-24.781 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-26.023 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-24.852 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-26.738 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-25.843 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-24.419 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-26.738 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-27.398 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-25.710 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-27.094 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-25.405 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-26.241 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-25.566 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-26.318 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-27.279 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-24.921 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-27.125 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-24.144 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-25.769 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-26.066 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-26.469 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-25.564 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-25.942 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-24.349 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-25.305 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-24.929 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-23.907 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-25.176 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-24.692 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-24.176 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-23.563 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-24.003 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-23.349 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-25.466 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-22.939 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-25.021 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-25.381 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-24.020 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-24.550 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-24.082 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-23.840 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-24.457 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-24.488 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-24.238 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-24.844 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-23.590 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-25.208 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-24.242 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-25.139 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-23.555 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-25.964 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-24.675 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-24.235 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-25.435 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-23.903 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-24.378 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-23.909 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-24.106 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-28.592 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-31.782 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-24.672 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-29.617 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-28.657 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-26.817 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-28.613 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-25.037 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-28.961 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-23.160 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-29.493 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-32.231 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-29.504 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-27.829 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-27.498 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-29.095 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-26.462 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-29.330 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-28.130 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-28.284 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-28.803 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-30.469 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-28.813 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-27.862 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-29.286 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-28.519 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-31.775 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-27.373 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-27.491 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-28.708 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-29.159 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-29.050 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-27.832 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-28.222 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-29.916 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-30.751 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-29.177 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-28.498 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-25.639 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-24.369 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-25.981 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-24.939 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-25.137 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-23.700 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-25.338 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-25.568 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-25.704 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-25.407 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-25.269 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-27.131 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-25.517 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-25.005 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-25.672 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-23.714 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-25.842 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-24.804 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-25.251 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-25.321 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-25.846 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-25.033 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-25.644 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-25.168 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-24.226 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-24.800 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-24.830 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-27.275 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-25.207 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-25.013 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-25.528 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-24.972 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-25.525 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-24.264 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-24.820 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-23.029 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-23.952 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-24.567 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-24.157 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-23.872 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-23.264 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-22.840 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-24.526 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-23.157 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-23.604 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-22.869 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-24.520 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-24.895 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-24.113 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-21.361 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-23.309 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-23.656 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-22.980 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-23.167 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-23.978 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-23.137 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-24.043 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-23.538 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-23.462 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-23.649 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-23.332 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-22.931 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-23.453 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-22.706 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-22.457 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-23.716 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-23.846 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-24.063 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-23.846 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-23.711 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-21.479 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-22.247 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-22.005 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-21.184 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-23.301 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-22.220 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-21.550 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-23.314 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-22.288 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-22.079 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-22.624 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-21.478 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-22.449 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-24.253 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-21.266 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-22.901 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-21.727 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-22.475 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-22.177 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-21.487 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-23.362 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-22.544 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-23.025 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-22.679 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-22.314 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-21.821 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-22.017 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-23.223 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-22.896 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-24.031 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-29.381 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-22.128 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-31.177 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-29.764 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-22.894 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-22.904 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-29.556 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-28.151 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-26.830 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-22.188 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-30.618 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-22.810 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-32.911 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-29.694 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-31.332 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-27.259 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-30.829 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-29.342 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-30.669 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-31.634 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-28.735 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-29.401 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-29.166 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-30.451 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-31.360 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-28.512 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-28.924 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-28.141 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-31.208 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-29.469 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-31.724 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-28.338 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-28.940 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-29.377 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-29.285 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-28.321 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-28.187 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-31.656 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-30.902 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-30.749 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-24.229 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-24.684 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-25.105 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-24.041 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-25.851 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-23.641 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-25.007 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-23.698 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-23.772 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-23.643 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-25.027 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-25.726 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-23.538 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-24.975 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-24.763 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-23.786 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-24.329 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-26.512 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-24.833 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-25.106 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-24.312 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-24.630 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-25.410 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-25.172 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-23.747 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-24.627 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-23.837 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-24.032 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-24.220 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-24.042 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-23.258 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-24.411 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-25.077 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-25.307 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-25.000 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-22.482 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-22.168 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-22.333 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-22.103 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-22.335 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-21.391 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-22.361 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-22.814 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-22.197 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-23.313 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-22.539 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-23.231 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-23.674 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-23.282 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-22.418 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-23.435 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-21.986 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-22.542 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-22.792 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-22.408 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-22.390 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-21.435 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-22.228 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-23.913 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-20.792 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-22.178 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-22.875 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-21.892 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-22.330 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-21.879 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-23.587 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-22.563 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-23.434 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-22.410 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-21.302 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-21.605 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-21.328 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-21.286 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-20.675 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-21.950 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-21.411 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-21.009 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-21.310 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-20.750 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-21.846 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-20.518 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-21.852 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-22.295 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-21.263 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-20.489 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-22.348 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-20.888 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-21.636 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-21.629 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-21.195 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-20.937 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-21.087 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-21.666 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-20.404 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-20.010 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-21.810 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-21.473 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-21.249 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-21.274 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-20.575 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-26.442 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-24.560 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-22.518 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-20.579 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-21.116 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-21.298 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-26.905 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-24.036 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-26.475 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-24.667 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-21.392 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-25.300 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-26.678 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-25.148 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-24.842 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-24.987 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-25.089 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-27.247 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-24.746 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-24.741 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-24.374 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-25.621 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-25.740 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-24.685 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-25.154 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-24.643 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-25.274 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-25.198 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-25.491 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-26.470 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-26.108 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-25.065 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-25.299 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-24.879 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-25.532 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-24.493 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-23.743 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-26.877 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-25.210 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-25.433 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-22.611 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-22.595 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-22.089 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-21.393 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-21.288 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-22.247 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-22.341 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-23.248 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-22.501 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-21.594 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-23.072 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-20.896 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-21.993 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-22.123 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-22.350 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-22.604 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-22.775 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-21.939 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-22.097 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-21.085 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-22.068 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-22.956 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-22.102 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-22.841 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-22.937 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-21.289 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-22.452 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-23.239 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-22.413 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-22.091 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-21.809 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-22.057 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-21.186 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-21.445 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-22.293 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-20.689 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-21.023 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-20.486 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-20.469 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-20.189 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-20.788 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-20.462 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-20.750 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-20.238 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-21.540 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-21.248 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-20.228 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-21.125 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-20.940 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-21.704 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-21.156 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-20.877 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-21.023 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-21.380 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-19.925 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-19.648 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-20.759 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-20.804 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-20.994 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-20.156 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-20.666 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-20.589 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-20.682 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-20.270 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-21.046 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-21.119 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-20.179 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-21.235 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-20.612 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-20.353 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-20.212 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-19.945 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-20.764 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-19.958 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-19.968 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-19.627 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-19.943 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-19.987 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-19.456 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-20.223 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-18.926 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-20.779 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-19.674 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-19.544 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-19.526 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-18.847 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-19.949 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-19.476 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-19.709 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-18.528 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-19.043 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-19.408 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-20.200 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-19.130 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-20.002 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-18.567 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-19.343 total time=   1.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-19.632 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-19.122 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-19.489 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-18.477 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-23.681 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-25.336 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-25.823 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-19.634 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-25.316 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-26.585 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-24.908 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-19.178 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-25.583 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-26.706 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-19.547 total time=   1.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-24.912 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-25.979 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-24.303 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-19.617 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-25.562 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-25.299 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-25.847 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-26.528 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-26.663 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-24.634 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-25.733 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-25.906 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-25.545 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-24.306 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-24.765 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-23.489 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-24.380 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-24.278 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-25.728 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-23.952 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-25.370 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-26.751 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-25.627 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-24.646 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-24.554 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-27.193 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-25.057 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-25.481 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-21.983 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-22.746 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-21.724 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-21.229 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-22.334 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-21.194 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-21.380 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-21.899 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-21.706 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-21.373 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-21.218 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-21.341 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-21.384 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-20.182 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-20.517 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-21.000 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-21.994 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-20.782 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-20.815 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-22.134 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-20.760 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-21.549 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-22.162 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-20.158 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-20.348 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-20.320 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-21.132 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-21.375 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-21.411 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-20.292 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-22.068 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-21.767 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-20.882 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-21.384 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-21.922 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-19.803 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-20.364 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-19.999 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-19.069 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-19.984 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-20.054 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-19.245 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-19.416 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-19.968 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-19.510 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-18.870 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-19.966 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-19.437 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-19.284 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-19.242 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-19.572 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-19.994 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-19.732 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-18.979 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-19.914 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-18.858 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-19.681 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-20.028 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-19.005 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-19.042 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-19.361 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-20.350 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-19.197 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-18.845 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-19.520 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-20.400 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-19.518 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-19.997 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-20.160 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-19.857 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-18.676 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-18.547 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-19.485 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-18.790 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-19.736 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-18.415 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-19.172 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-18.464 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-19.187 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-18.825 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-17.297 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-19.300 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-18.655 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-18.332 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-18.133 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-17.962 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-19.077 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-19.396 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-18.361 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-18.257 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-18.264 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-18.420 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-17.875 total time=   1.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-17.999 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-18.908 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-18.032 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-18.324 total time=   1.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-19.112 total time=   1.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-18.233 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-17.666 total time=   1.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-19.315 total time=   1.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-19.280 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-21.821 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-19.360 total time=   1.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-22.624 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-22.886 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-21.571 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-18.468 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-21.390 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-21.788 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-18.061 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-21.358 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-22.725 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-21.808 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-21.608 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-21.651 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-22.749 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-21.504 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-21.576 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-22.175 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-22.890 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-21.480 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-21.895 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-21.285 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-21.862 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-21.784 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-22.393 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-22.194 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-22.553 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-21.706 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-21.372 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-22.216 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-21.722 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-20.784 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-22.240 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-21.363 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-22.256 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-22.153 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-21.958 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-20.765 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-18.993 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-19.894 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-19.644 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-19.778 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-18.771 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-18.931 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-19.612 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-19.670 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-19.588 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-20.223 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-19.285 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-18.877 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-19.774 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-18.894 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-19.943 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-18.359 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-19.589 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-21.051 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-18.795 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-18.619 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-18.573 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-19.729 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-19.823 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-19.079 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-19.396 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-19.796 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-18.246 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-19.558 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-18.758 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-19.611 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-18.754 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-19.241 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-19.488 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-18.378 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-18.981 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-18.847 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-17.934 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-19.247 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-18.256 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-18.595 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-17.408 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-18.503 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-18.330 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-17.899 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-17.975 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-16.916 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-18.202 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-18.250 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-17.207 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-17.781 total time=   1.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-17.615 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-18.537 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-18.408 total time=   1.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-17.359 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-17.888 total time=   1.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-17.190 total time=   1.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-18.301 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-17.949 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-17.565 total time=   1.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-17.768 total time=   1.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-16.940 total time=   1.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-18.389 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-18.819 total time=   1.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-17.545 total time=   1.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-18.091 total time=   1.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-16.739 total time=   1.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-18.578 total time=   1.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-18.342 total time=   1.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-18.015 total time=   1.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-18.050 total time=   1.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-17.952 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-16.518 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-17.940 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-16.862 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-17.644 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-16.440 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-17.993 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-18.030 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-16.749 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-17.627 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-16.392 total time=   1.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-17.648 total time=   1.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-17.681 total time=   1.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-16.486 total time=   1.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-16.893 total time=   1.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-16.600 total time=   1.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-17.826 total time=   1.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-17.204 total time=   1.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-17.082 total time=   1.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-17.166 total time=   1.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-16.253 total time=   1.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-17.665 total time=   1.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-16.709 total time=   1.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-16.852 total time=   1.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-17.940 total time=   1.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-16.406 total time=   1.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-17.483 total time=   1.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-17.534 total time=   1.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-17.032 total time=   1.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-33.367 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-32.227 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-31.991 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-32.282 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-16.839 total time=   1.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-31.155 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-29.658 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-35.548 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-32.139 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-17.653 total time=   1.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-32.780 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-16.061 total time=   2.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-17.225 total time=   1.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-28.692 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-16.552 total time=   1.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-31.775 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-28.596 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-31.422 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-31.599 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-27.522 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-28.741 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-30.195 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-30.579 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-31.013 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-37.174 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-30.097 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-35.585 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-28.715 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-33.846 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-31.890 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-30.264 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-32.137 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-32.769 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-30.350 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-31.635 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-30.911 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-34.436 total time=   0.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.075, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-17.284 total time=   1.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-33.117 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-33.491 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-30.221 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-28.160 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-27.430 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-26.177 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-26.511 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-26.487 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-26.138 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-26.111 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-29.179 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-25.975 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-26.010 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-26.058 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-26.962 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-26.477 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-25.917 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-26.132 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-26.359 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-26.274 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-27.184 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-26.533 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-26.154 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-25.546 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-26.688 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-25.523 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-26.683 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-26.086 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-27.694 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-26.586 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-27.001 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-25.501 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-25.843 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-25.307 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-28.365 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-26.974 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-26.057 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-25.403 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-23.482 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-24.787 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-23.963 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-24.177 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-24.536 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-22.509 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-25.652 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-24.683 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-25.323 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-23.905 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-23.915 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-25.607 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-24.981 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-24.526 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-23.114 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-24.983 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-24.260 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-24.664 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-23.859 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-24.677 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-24.283 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-24.931 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-25.246 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-22.126 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-26.040 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-24.148 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-23.212 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-23.805 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-25.097 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-23.549 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-24.754 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-25.255 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-23.357 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-24.323 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-24.362 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-22.695 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-22.970 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-22.992 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-21.813 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-24.116 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-23.857 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-22.462 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-23.709 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-21.538 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-23.643 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-22.743 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-23.485 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-22.801 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-23.743 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-24.235 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-23.829 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-23.182 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-22.998 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-23.792 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-22.705 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-22.341 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-22.590 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-25.271 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-24.022 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-23.525 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-23.946 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-22.736 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-22.826 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-22.315 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-26.287 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-23.916 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-22.085 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-23.795 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-29.201 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-28.476 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-28.614 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-25.912 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-22.614 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-22.322 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-26.463 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-23.146 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-27.685 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-28.847 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-29.468 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-27.028 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-28.955 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-26.593 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-26.834 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-27.416 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-26.387 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-27.925 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-28.041 total time=   0.2s[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-30.062 total time=   0.2s\n",
+      "\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-29.316 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-26.037 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-29.047 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-27.267 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-28.434 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-25.846 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-25.332 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-26.724 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-25.681 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-28.682 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-27.864 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-26.578 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-27.906 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-27.062 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-28.386 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-28.238 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-27.306 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-23.709 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-23.437 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-22.496 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-24.941 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-24.328 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-23.902 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-23.963 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-25.324 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-23.622 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-23.953 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-24.571 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-24.180 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-24.997 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-23.163 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-25.905 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-23.961 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-23.948 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-23.793 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-23.775 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-23.947 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-23.776 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-25.241 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-23.609 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-23.296 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-23.583 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-22.996 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-24.364 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-24.266 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-24.762 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-24.125 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-24.093 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-24.469 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-25.061 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-24.006 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-23.679 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-23.411 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-22.996 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-22.348 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-22.405 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-23.739 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-24.080 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-22.388 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-23.134 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-23.020 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-21.824 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-22.382 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-21.916 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-22.521 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-22.113 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-23.536 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-22.169 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-22.011 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-22.525 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-21.083 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-22.967 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-22.947 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-22.041 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-22.782 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-21.627 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-23.055 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-22.807 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-21.395 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-22.411 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-22.206 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-23.017 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-23.655 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-22.425 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-21.782 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-23.096 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-22.827 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-21.487 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-21.379 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-22.233 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-22.792 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-20.121 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-20.922 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-23.238 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-21.327 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-21.224 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-22.543 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-21.163 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-21.472 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-21.379 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-22.446 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-20.244 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-21.046 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-20.711 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-22.288 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-21.417 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-21.270 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-19.759 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-21.703 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-21.536 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-22.054 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-21.399 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-20.247 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-21.096 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-22.099 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-21.015 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-21.941 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-21.821 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-21.950 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-25.674 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-26.674 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-29.300 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-20.762 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-28.305 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-29.067 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-26.781 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-21.461 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-26.673 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-26.936 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-28.709 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-29.336 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-26.740 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-22.170 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-25.810 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-26.815 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-26.651 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-27.272 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-26.565 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-28.411 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-27.829 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-32.292 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-28.396 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-27.136 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-29.067 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-27.904 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-29.716 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-26.370 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-26.203 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-25.945 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-28.409 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-26.802 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-26.122 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-28.188 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-28.192 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-27.014 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-28.236 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-25.688 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-24.017 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-22.766 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-24.745 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-24.520 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-23.425 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-22.098 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-23.570 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-24.203 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-23.094 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-23.605 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-22.587 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-25.055 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-22.908 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-24.497 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-23.769 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-23.217 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-22.222 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-25.049 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-22.942 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-22.315 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-22.444 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-24.017 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-24.233 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-23.487 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-24.034 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-23.927 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-23.040 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-23.787 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-23.341 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-21.182 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-21.817 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-23.109 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-22.953 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-22.609 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-22.350 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-20.216 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-21.653 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-20.893 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-20.708 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-21.671 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-21.330 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-21.521 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-22.719 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-22.108 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-21.622 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-20.996 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-22.467 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-22.072 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-21.183 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-21.485 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-21.279 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-20.606 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-22.972 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-20.615 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-21.242 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-20.080 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-21.355 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-22.777 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-21.449 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-21.988 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-19.966 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-20.539 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-21.244 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-20.599 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-21.732 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-22.427 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-22.054 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-20.903 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-20.537 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-20.862 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-19.331 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-20.338 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-20.978 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-19.882 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-19.825 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-20.082 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-20.555 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-19.918 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-20.436 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-22.369 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-19.631 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-20.264 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-20.956 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-20.041 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-21.100 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-19.362 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-20.557 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-20.769 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-19.663 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-19.146 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-21.105 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-20.982 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-19.964 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-20.181 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-20.351 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-19.896 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-21.242 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-20.164 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-20.390 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-23.363 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-20.640 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-23.633 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-20.408 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-21.276 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-20.984 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-24.637 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-24.648 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-20.305 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-24.052 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-19.531 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-24.101 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-22.380 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-24.442 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-23.875 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-24.317 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-25.081 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-23.857 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-23.694 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-25.686 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-23.677 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-24.320 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-25.087 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-24.901 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-23.062 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-25.556 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-24.874 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-24.016 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-23.413 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-23.277 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-23.927 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-25.653 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-23.310 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-24.912 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-23.301 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-25.681 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-23.574 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-23.160 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-23.535 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-23.015 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-22.622 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-20.163 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-20.980 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-20.992 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-20.719 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-20.599 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-20.126 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-21.666 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-21.107 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-21.495 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-20.326 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-21.748 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-21.072 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-21.412 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-21.766 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-21.229 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-20.527 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-20.700 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-20.827 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-21.146 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-20.915 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-20.944 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-20.497 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-21.336 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-20.641 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-21.364 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-20.313 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-20.897 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-21.185 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-20.360 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-21.233 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-20.826 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-20.873 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-22.431 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-20.554 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-20.859 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-20.282 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-20.414 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-19.477 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-20.007 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-20.297 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-18.783 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-20.049 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-20.081 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-19.875 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-19.353 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-19.422 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-19.784 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-19.560 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-19.510 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-20.258 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-18.893 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-20.332 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-19.975 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-19.259 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-21.550 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-19.576 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-19.506 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-19.816 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-19.431 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-19.074 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-19.230 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-20.219 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-19.082 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-19.930 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-18.840 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-19.924 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-19.606 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-19.808 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-19.792 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-19.208 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-18.121 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-19.168 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-19.451 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-19.987 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-19.228 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-18.536 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-19.031 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-18.720 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-19.144 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-19.293 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-17.998 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-19.014 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-18.617 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-18.195 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-18.639 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-18.239 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-18.921 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-18.654 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-18.769 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-18.052 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-17.592 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-18.692 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-18.763 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-18.215 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-17.772 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-18.232 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-18.897 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-17.571 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-18.790 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-23.114 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-23.607 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-17.990 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-26.134 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-19.141 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-22.617 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-24.739 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-17.585 total time=   1.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-18.790 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-21.956 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-24.600 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-24.330 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-18.037 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-23.657 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-23.885 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-18.613 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-24.415 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-23.846 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-22.794 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-25.364 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-24.029 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-22.144 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-23.623 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-23.274 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-24.357 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-23.359 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-24.409 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-22.412 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-23.241 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-21.558 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-23.534 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-23.598 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-22.644 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-24.336 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-24.050 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-21.370 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-23.869 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-24.769 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-23.699 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-21.852 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-23.022 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-20.657 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-20.630 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-21.528 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-20.022 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-20.305 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-19.563 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-20.039 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-21.058 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-18.957 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-19.005 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-21.531 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-20.457 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-20.025 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-19.465 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-20.604 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-20.519 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-19.780 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-20.683 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-21.065 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-19.929 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-18.842 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-20.340 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-19.643 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-20.230 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-18.788 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-19.102 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-19.655 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-19.884 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-19.813 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-20.256 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-19.473 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-20.073 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-20.630 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-20.236 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-20.028 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-18.363 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-18.894 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-19.167 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-19.743 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-19.609 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-18.404 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-19.309 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-20.194 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-18.818 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-18.567 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-18.396 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-19.587 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-19.411 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-18.273 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-18.658 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-17.545 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-18.923 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-19.494 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-17.930 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-18.043 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-17.302 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-18.949 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-18.303 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-18.211 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-19.412 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-19.428 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-17.564 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-18.996 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-18.504 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-18.785 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-18.688 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-18.986 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-17.628 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-18.765 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-18.327 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-17.913 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-18.349 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-18.729 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-18.112 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-19.138 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-17.907 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-17.653 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-17.570 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-18.026 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-17.849 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-16.580 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-18.120 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-18.035 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-17.325 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-17.721 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-17.005 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-17.898 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-18.184 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-17.608 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-17.593 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-16.899 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-18.476 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-17.752 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-18.397 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-16.957 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-16.982 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-17.922 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-17.660 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-17.799 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-17.194 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-17.251 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-20.051 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-17.729 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-22.617 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-18.257 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-21.575 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-21.561 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-20.942 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-20.868 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-21.059 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-22.325 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-21.690 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-18.119 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-20.897 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-20.992 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-17.842 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-20.932 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-20.225 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-20.515 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-20.876 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-21.005 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-20.782 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-19.707 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-21.356 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-20.773 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-21.749 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-20.880 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-19.836 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-20.054 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-21.396 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-19.586 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-21.431 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-21.138 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-19.331 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-20.798 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-21.190 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-19.856 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-20.082 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-20.627 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-20.424 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-18.184 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-19.205 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-19.411 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-18.865 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-19.080 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-18.771 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-18.086 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-19.118 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-18.113 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-18.123 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-17.456 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-18.438 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-18.730 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-18.552 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-18.327 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-17.732 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-18.402 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-18.427 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-17.972 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-19.627 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-17.569 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-18.934 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-18.355 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-17.948 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-18.324 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-17.615 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-18.744 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-18.318 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-18.207 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-18.848 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-18.287 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-17.114 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-18.073 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-18.040 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-19.088 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-17.966 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-16.995 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-17.669 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-16.996 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-18.353 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-17.079 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-18.004 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-17.813 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-17.029 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-17.611 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-16.127 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-17.655 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-17.695 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-17.008 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-17.373 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-18.103 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-16.381 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-17.578 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-16.872 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-17.172 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-16.339 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-17.906 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-17.784 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-16.972 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-16.891 total time=   1.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-16.386 total time=   1.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-17.346 total time=   1.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-17.617 total time=   1.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-16.402 total time=   1.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-17.236 total time=   1.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-16.309 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-17.785 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-18.103 total time=   1.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-17.024 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-17.333 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-16.216 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-17.749 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-17.442 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-16.775 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-17.240 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-16.080 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-17.311 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-16.504 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-17.390 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-16.720 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-15.651 total time=   1.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-17.382 total time=   1.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-16.994 total time=   1.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-16.233 total time=   1.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-16.324 total time=   1.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-16.003 total time=   1.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-17.272 total time=   1.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-16.899 total time=   1.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-16.505 total time=   1.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-16.543 total time=   1.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-15.730 total time=   1.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-17.148 total time=   1.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-16.995 total time=   1.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-16.359 total time=   1.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-16.010 total time=   1.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-16.335 total time=   1.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-17.098 total time=   1.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-16.926 total time=   1.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-28.640 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-16.095 total time=   1.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-15.981 total time=   1.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-26.821 total time=   0.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-15.791 total time=   1.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-32.527 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-27.588 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-29.465 total time=   0.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-28.748 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-28.384 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-27.260 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-33.848 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-17.077 total time=   1.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-27.013 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-31.604 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-28.055 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-27.466 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-28.993 total time=   0.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-32.223 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-26.563 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-27.415 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-17.162 total time=   1.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-26.974 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-30.288 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-27.640 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-28.377 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-16.058 total time=   1.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-30.763 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-26.245 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-27.483 total time=   0.2s[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-27.479 total time=   0.2s\n",
+      "\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-26.908 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-25.930 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-29.956 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.1, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-16.719 total time=   1.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-28.435 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-29.116 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-27.472 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-30.168 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-33.775 total time=   0.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-32.374 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-27.992 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-25.360 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-24.799 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-26.243 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-25.463 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-23.991 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-25.008 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-24.815 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-26.557 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-24.552 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-24.793 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-23.616 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-23.951 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-25.029 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-24.079 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-26.459 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-23.939 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-23.771 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-24.438 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-25.892 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-24.900 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-23.082 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-25.032 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-26.141 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-24.266 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-25.413 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-23.513 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-24.198 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-23.301 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-24.415 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-23.721 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-24.352 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-23.896 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-23.905 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-24.678 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-24.155 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-23.513 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-22.091 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-23.672 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-23.293 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-22.527 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-24.121 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-22.774 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-22.596 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-22.510 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-22.543 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-22.736 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-23.976 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-22.240 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-21.482 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-22.929 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-23.885 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-23.076 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-23.654 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-23.050 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-22.667 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-22.718 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-23.057 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-22.164 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-24.576 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-23.332 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-23.045 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-22.743 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-21.757 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-21.467 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-22.088 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-23.094 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-22.445 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-23.786 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-23.352 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-23.738 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-21.974 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-22.587 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-20.128 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-21.828 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-23.319 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-22.853 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-21.317 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-22.056 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-21.908 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-23.379 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-21.306 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-20.550 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-22.334 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-20.694 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-20.939 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-21.922 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-21.414 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-22.072 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-21.643 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-21.507 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-20.474 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-22.104 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-22.100 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-21.492 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-20.725 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-20.072 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-22.241 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-21.417 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-20.828 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-24.259 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-22.434 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-24.636 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-23.315 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-26.516 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-21.613 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-21.875 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-25.003 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-25.279 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-25.566 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-23.229 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-25.383 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-24.685 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-21.994 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-24.983 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-25.436 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-24.825 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-24.429 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-25.117 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-25.909 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-24.816 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-24.420 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-24.925 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-25.542 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-25.340 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-24.507 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-26.342 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-24.865 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-26.660 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-24.147 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-24.830 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-24.475 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-25.347 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-25.025 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-25.329 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-23.819 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-27.205 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-25.671 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-25.053 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-26.123 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-24.699 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-22.465 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-22.972 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-22.420 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-23.311 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-22.110 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-23.259 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-21.622 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-21.854 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-22.853 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-23.056 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-23.970 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-22.271 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-22.188 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-21.198 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-24.647 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-22.319 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-21.626 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-23.815 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-24.167 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-21.912 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-21.441 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-22.133 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-23.065 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-22.592 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-22.053 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-22.198 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-22.708 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-23.225 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-21.726 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-22.322 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-22.411 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-22.421 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-22.447 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-21.464 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-21.333 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-20.689 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-20.755 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-20.918 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-21.172 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-21.781 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-22.329 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-21.743 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-19.957 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-19.954 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-22.074 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-20.491 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-20.655 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-20.440 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-21.501 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-20.783 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-21.854 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-22.304 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-21.238 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-21.431 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-20.994 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-20.074 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-20.971 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-21.046 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-21.155 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-21.273 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-19.910 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-22.468 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-20.752 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-21.729 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-21.458 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-21.092 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-20.968 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-21.407 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-21.226 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-22.402 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-20.325 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-21.252 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-20.899 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-19.355 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-19.584 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-20.496 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-20.680 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-20.143 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-20.616 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-21.144 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-20.282 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-19.848 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-21.133 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-19.176 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-20.760 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-19.122 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-20.597 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-19.540 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-20.469 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-22.248 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-20.415 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-20.964 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-20.635 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-19.317 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-19.820 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-20.373 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-19.103 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-19.512 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-26.586 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-20.310 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-19.432 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-19.839 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-20.955 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-19.620 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-23.737 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-26.434 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-24.468 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-24.587 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-23.521 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-24.689 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-19.903 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-20.268 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-24.197 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-26.192 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-24.331 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-23.576 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-23.711 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-26.000 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-24.671 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-24.423 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-24.959 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-23.477 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-26.778 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-25.143 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-24.189 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-24.659 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-27.656 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-26.053 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-23.805 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-23.925 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-23.745 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-24.360 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-24.137 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-24.947 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-24.987 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-24.484 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-22.671 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-24.782 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-25.467 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-24.524 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-22.507 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-21.439 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-20.171 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-20.930 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-22.505 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-21.816 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-20.471 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-21.425 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-22.014 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-21.261 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-21.128 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-20.550 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-21.747 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-22.013 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-22.508 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-20.807 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-20.848 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-23.078 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-21.786 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-20.359 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-21.813 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-20.583 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-20.786 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-20.874 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-21.099 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-21.672 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-19.860 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-22.011 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-21.761 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-21.188 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-21.777 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-22.132 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-21.774 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-20.896 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-24.089 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-20.385 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-21.386 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-20.665 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-21.338 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-21.073 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-19.654 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-20.185 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-19.684 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-19.611 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-20.807 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-19.850 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-18.781 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-19.914 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-19.341 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-20.299 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-20.334 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-19.662 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-20.469 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-18.481 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-19.061 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-21.503 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-19.750 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-20.947 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-18.748 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-19.902 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-19.316 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-19.557 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-21.114 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-19.781 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-19.772 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-19.611 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-20.002 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-19.053 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-19.237 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-19.942 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-19.079 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-21.141 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-19.510 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-19.262 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-18.524 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-19.232 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-18.485 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-19.720 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-19.325 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-19.775 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-18.200 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-19.491 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-19.903 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-19.723 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-19.711 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-17.368 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-19.265 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-19.251 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-19.223 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-18.559 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-19.459 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-19.181 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-18.981 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-18.743 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-19.227 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-19.044 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-20.496 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-19.936 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-19.215 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-21.052 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-22.942 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-20.094 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-17.752 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-19.595 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-23.552 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-20.413 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-21.866 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-22.075 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-19.341 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-18.729 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-21.934 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-23.731 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-19.026 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-22.179 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-21.725 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-21.418 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-22.731 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-21.671 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-22.303 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-24.852 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-21.993 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-21.868 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-23.091 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-22.348 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-22.227 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-22.230 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-22.351 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-21.845 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-23.536 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-24.519 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-22.433 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-22.423 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-21.387 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-23.339 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-21.881 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-23.085 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-21.491 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-23.037 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-21.893 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-22.905 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-20.435 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-20.548 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-19.297 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-20.537 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-20.631 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-19.412 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-19.952 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-20.751 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-18.345 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-20.525 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-19.681 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-20.023 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-18.271 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-19.698 total time=   0.6s[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-19.808 total time=   0.5s\n",
+      "\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-18.934 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-19.953 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-18.451 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-19.864 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-20.645 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-19.236 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-20.511 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-19.192 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-20.256 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-18.749 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-19.616 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-19.764 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-19.388 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-20.729 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-19.737 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-18.952 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-19.423 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-19.915 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-19.151 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-20.141 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-18.208 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-19.124 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-19.226 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-18.483 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-19.216 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-17.922 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-19.342 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-18.545 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-18.633 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-19.033 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-18.540 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-17.589 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-19.378 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-18.492 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-17.982 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-18.392 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-17.814 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-18.217 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-18.019 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-18.045 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-17.468 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-18.580 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-18.591 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-18.440 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-17.581 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-17.417 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-18.813 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-18.649 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-19.599 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-17.729 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-17.275 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-18.055 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-18.445 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-17.976 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-16.893 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-17.420 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-18.416 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-17.957 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-17.753 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-18.720 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-17.221 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-18.520 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-16.839 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-17.835 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-18.268 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-17.203 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-17.572 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-17.654 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-16.969 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-17.422 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-16.263 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-17.714 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-17.725 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-17.905 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-17.505 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-16.838 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-18.046 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-17.566 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-18.226 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-16.729 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-17.901 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-17.504 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-16.564 total time=   1.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-23.209 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-17.489 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-17.233 total time=   1.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-22.235 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-21.965 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-18.219 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-20.982 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-18.752 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-16.687 total time=   1.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-20.672 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-23.344 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-21.570 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-22.102 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-21.410 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-21.212 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-22.538 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-17.048 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-21.911 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-22.780 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-21.156 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-17.243 total time=   1.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-20.900 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-19.761 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-22.338 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-21.925 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-21.738 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-21.812 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-20.323 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-22.572 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-21.777 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-20.570 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-21.878 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-21.445 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-21.557 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-20.787 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-19.895 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-21.763 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-21.111 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-22.572 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-21.519 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-20.193 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-21.482 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-19.111 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-18.771 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-18.262 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-19.597 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-18.606 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-18.811 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-18.196 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-19.653 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-20.498 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-18.902 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-19.465 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-18.677 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-18.820 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-18.115 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-19.711 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-17.563 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-19.259 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-18.620 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-18.690 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-17.896 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-18.232 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-18.547 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-19.065 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-18.888 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-18.191 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-18.228 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-19.366 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-19.349 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-17.845 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-19.129 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-17.755 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-20.258 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-18.615 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-17.944 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-18.879 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-18.390 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-16.901 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-18.252 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-18.534 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-17.843 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-17.692 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-17.555 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-17.984 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-17.638 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-17.639 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-18.127 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-17.058 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-17.751 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-17.040 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-16.951 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-17.538 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-17.562 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-17.964 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-17.150 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-17.767 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-16.293 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-17.559 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-17.265 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-18.624 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-16.796 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-16.999 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-17.876 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-16.804 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-18.085 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-17.686 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-17.070 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-18.002 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-18.297 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-17.647 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-17.473 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-17.448 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-18.026 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-17.103 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-17.426 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-16.861 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-16.120 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-17.782 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-17.056 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-16.540 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-16.494 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-16.135 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-17.884 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-17.168 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-16.954 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-17.038 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-16.195 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-17.101 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-16.947 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-16.463 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-17.204 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-15.986 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-15.948 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-16.751 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-16.298 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-16.509 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-16.156 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-17.156 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-17.133 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-16.567 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-17.377 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-19.157 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-19.102 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-15.948 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-20.508 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-19.907 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-19.053 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-19.864 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-17.383 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-17.047 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-19.965 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-16.456 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-16.743 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-20.040 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-19.769 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-19.619 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-19.900 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-18.845 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-19.728 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-19.173 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-19.040 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-18.364 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-19.977 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-19.502 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-19.472 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-19.711 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-19.046 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-19.697 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-19.066 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-20.088 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-18.993 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-20.031 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-19.572 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-19.060 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-19.780 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-18.854 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-18.572 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-20.031 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-20.491 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-19.035 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-19.405 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-16.778 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-18.371 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-18.198 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-17.227 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-17.050 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-16.624 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-18.866 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-17.813 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-17.172 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-17.295 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-16.646 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-17.777 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-17.903 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-17.709 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-17.195 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-16.338 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-18.108 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-17.802 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-16.563 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-16.146 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-17.347 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-17.229 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-17.991 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-17.608 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-16.466 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-16.387 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-17.646 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-18.124 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-16.923 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-16.955 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-17.093 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-16.473 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-18.004 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-17.409 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-16.299 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-17.566 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-17.556 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-17.526 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-17.167 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-16.186 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-16.246 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-17.762 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-17.270 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-16.309 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-16.168 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-15.578 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-17.577 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-16.971 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-16.746 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-16.217 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-15.535 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-16.038 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-17.678 total time=   1.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-15.871 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-16.341 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-15.776 total time=   1.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-17.442 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-16.773 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-16.288 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-16.698 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-15.540 total time=   1.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-17.009 total time=   1.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-15.880 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-16.994 total time=   1.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-16.347 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-16.254 total time=   1.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-17.032 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-16.987 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-15.748 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-16.054 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-17.240 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-17.068 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-15.941 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-16.493 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-16.604 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-15.419 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-16.630 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-16.079 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-16.012 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-16.536 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-15.371 total time=   1.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-16.796 total time=   1.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-16.619 total time=   1.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-15.993 total time=   1.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-16.317 total time=   1.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-15.258 total time=   1.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-16.807 total time=   1.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-16.126 total time=   1.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-15.244 total time=   1.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-15.981 total time=   1.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-15.479 total time=   1.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-16.871 total time=   1.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-16.153 total time=   1.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-15.621 total time=   1.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-15.993 total time=   1.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-15.190 total time=   1.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-16.660 total time=   1.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-17.103 total time=   1.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-15.778 total time=   1.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-31.177 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-16.204 total time=   1.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-25.387 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-15.553 total time=   1.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-26.588 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-27.042 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-25.724 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-27.190 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-28.637 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-28.347 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-26.730 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-28.629 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-16.801 total time=   1.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-16.716 total time=   1.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-16.014 total time=   1.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-28.048 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-26.532 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-25.346 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-27.380 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-25.958 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-26.491 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-25.518 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-27.295 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-27.552 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-25.437 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.15, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-16.152 total time=   1.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-25.751 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-26.406 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-27.767 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-26.523 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-26.906 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-26.853 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-25.436 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-25.872 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-27.659 total time=   0.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-25.725 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-24.374 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-25.491 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-27.762 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-27.209 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-28.767 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-24.108 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-25.032 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-22.296 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-22.206 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-23.959 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-21.456 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-21.834 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-24.391 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-25.300 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-23.605 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-24.163 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-23.374 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-23.808 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-25.029 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-23.686 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-24.231 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-22.493 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-23.306 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-23.076 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-23.590 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-22.118 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-23.773 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-24.770 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-23.161 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-23.109 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-21.841 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-22.954 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-21.894 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-22.893 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-24.547 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-24.407 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-21.649 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-22.543 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-23.733 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-22.984 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-23.324 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-21.289 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-23.013 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-23.014 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-22.760 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-22.148 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-23.401 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-21.253 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-23.770 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-22.634 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-20.950 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-22.078 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-21.637 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-21.196 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-23.884 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-21.857 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-20.902 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-22.692 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-22.034 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-22.070 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-20.653 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-21.544 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-21.670 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-22.504 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-22.659 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-21.284 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-21.933 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-20.453 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-20.896 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-22.470 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-22.459 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-21.006 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-21.469 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-21.860 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-22.235 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-21.415 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-22.177 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-21.272 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-22.099 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-22.449 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-21.345 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-21.934 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-21.235 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-21.019 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-20.698 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-19.285 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-20.060 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-21.345 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-20.567 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-20.577 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-20.814 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-21.044 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-20.557 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-22.384 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-20.783 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-20.173 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-20.902 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-22.714 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-19.554 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-21.390 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-21.964 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-20.944 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-21.487 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-21.469 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-20.305 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-20.255 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-23.906 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-25.425 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-24.555 total time=   0.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-20.334 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-22.852 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-25.333 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-20.552 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-21.989 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-25.275 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-22.869 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-20.612 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-25.703 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-24.721 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-24.861 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-23.964 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-25.218 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-24.304 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-23.739 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-24.959 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-22.925 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-24.798 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-24.876 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-21.958 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-23.740 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-25.032 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-22.914 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-24.693 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-22.667 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-22.771 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-23.454 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-24.369 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-24.641 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-21.896 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-24.838 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-24.371 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-23.720 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-23.667 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-27.585 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-22.158 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-21.748 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-21.704 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-23.204 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-22.643 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-22.026 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-22.877 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-21.311 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-24.252 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-22.909 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-20.110 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-20.928 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-21.260 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-23.789 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-20.585 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-22.093 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-20.034 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-21.273 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-21.132 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-21.243 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-21.590 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-20.661 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-20.053 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-21.392 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-20.785 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-22.013 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-21.975 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-21.129 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-21.663 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-20.098 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-21.504 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-20.465 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-23.100 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-22.699 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-21.805 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-21.602 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-20.013 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-20.242 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-20.092 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-20.103 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-21.319 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-20.191 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-20.848 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-20.561 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-20.050 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-20.600 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-19.964 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-21.238 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-19.604 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-19.690 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-20.782 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-20.266 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-19.731 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-21.019 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-19.850 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-19.702 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-20.957 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-20.406 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-20.295 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-20.640 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-20.594 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-20.203 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-20.014 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-20.178 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-19.375 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-20.850 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-20.268 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-21.353 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-20.372 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-19.285 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-20.739 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-18.966 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-20.460 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-20.299 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-19.837 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-20.491 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-18.595 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-21.174 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-19.522 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-19.139 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-19.104 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-20.168 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-19.694 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-19.912 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-18.560 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-19.666 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-19.890 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-18.167 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-18.083 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-20.336 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-19.870 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-18.218 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-19.289 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-19.716 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-18.517 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-19.265 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-18.428 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-19.870 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-18.848 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-19.129 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-19.269 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-22.665 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-18.000 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-22.691 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-19.651 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-19.393 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-23.678 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-25.081 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-19.838 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-23.994 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=3, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-18.266 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-24.337 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-25.794 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-24.144 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-23.542 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-24.583 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-23.021 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-23.247 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-23.250 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-22.524 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-21.972 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-22.712 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-23.529 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-26.613 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-20.859 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-24.094 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-23.476 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-23.948 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-24.495 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-24.009 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-22.558 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-25.614 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-24.082 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-23.820 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-24.038 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-24.931 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-22.266 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-25.972 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-24.355 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-22.952 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-23.356 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-20.908 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-19.830 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-19.900 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-21.538 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-19.884 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-21.755 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-19.533 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-19.892 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-21.425 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-20.582 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-20.437 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-20.624 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-20.043 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-20.126 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-22.188 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-20.615 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-20.782 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-21.141 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-21.252 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-20.395 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-20.851 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-18.993 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-21.512 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-19.925 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-19.058 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-19.108 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-20.765 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-20.324 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-19.898 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-19.565 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-20.447 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-20.949 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-20.044 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-19.721 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-20.153 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-20.616 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-19.002 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-20.962 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-18.860 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-21.488 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-18.604 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-19.723 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-19.564 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-18.899 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-20.416 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-18.130 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-18.931 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-20.412 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-20.619 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-19.485 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-17.958 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-19.569 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-20.365 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-19.216 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-20.339 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-19.504 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-19.162 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-20.411 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-18.857 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-19.412 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-18.837 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-18.250 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-20.370 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-19.363 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-17.698 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-19.283 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-19.114 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-19.875 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-18.948 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-19.416 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-18.575 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-19.361 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-19.632 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-18.861 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-19.798 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-18.517 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-19.597 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-18.378 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-18.070 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-18.455 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-18.621 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-19.472 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-17.586 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-19.093 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-17.729 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-18.567 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-18.784 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-19.546 total time=  24.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-18.297 total time=  24.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-19.365 total time=  24.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-17.968 total time=  24.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-18.220 total time=  24.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-18.311 total time=  24.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-18.496 total time=  24.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-18.839 total time=  24.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-17.735 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-18.925 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-19.305 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-17.539 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-17.720 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-21.913 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-17.928 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-18.503 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-21.887 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-22.317 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-20.663 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-17.614 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-21.885 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-18.430 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-17.673 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-22.149 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-21.509 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-21.144 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-21.002 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-21.792 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-21.647 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-22.020 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-21.452 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-20.959 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-21.413 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-22.303 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-22.170 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-21.568 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-20.844 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-20.073 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-20.476 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-20.775 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-21.539 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-21.298 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-21.044 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-21.440 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-22.378 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-21.559 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-21.312 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-20.101 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-23.194 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-20.261 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-21.480 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-20.600 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-21.199 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-18.444 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-19.326 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-18.831 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-19.348 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-19.172 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-18.211 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-19.387 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-19.412 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-18.507 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-19.111 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-18.242 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-18.937 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-18.994 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-19.454 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-19.201 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-19.383 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-18.089 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-18.356 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-19.854 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-18.325 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-18.845 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-19.156 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-18.606 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-17.356 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-18.296 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-19.371 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-19.305 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-19.259 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-18.373 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-18.365 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-18.754 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-18.917 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-18.510 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-17.918 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-19.732 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-16.921 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-18.788 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-19.340 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-17.434 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-18.220 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-18.445 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-18.417 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-18.220 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-17.021 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-18.390 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-16.494 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-18.603 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-18.036 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-17.054 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-18.185 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-17.114 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-18.580 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-17.689 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-16.756 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-17.905 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-16.758 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-18.131 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-18.368 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-17.408 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-17.747 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-17.320 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-18.776 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-17.630 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-17.226 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-16.886 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-17.368 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-17.748 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-18.124 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-17.920 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-17.405 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-16.552 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-17.575 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-18.125 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-17.666 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-18.113 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-16.892 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-17.479 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-17.332 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-17.344 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-17.102 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-17.868 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-17.613 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-17.125 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-16.582 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-16.427 total time=   1.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-16.472 total time=   1.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-17.801 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-17.180 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-17.133 total time=   1.1s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-16.736 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-17.446 total time=   1.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-15.767 total time=   1.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-17.266 total time=   1.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-16.828 total time=   1.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-16.574 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-15.897 total time=   1.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-17.497 total time=   1.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-16.710 total time=   1.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-17.896 total time=   1.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-16.240 total time=   1.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-16.215 total time=   1.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-20.089 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-21.479 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-21.388 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-22.910 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.5;, score=-20.015 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-17.641 total time=   1.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-21.089 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-20.324 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-22.019 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-20.860 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-16.919 total time=   1.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.618;, score=-20.498 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-20.952 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-16.520 total time=   1.1s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-18.745 total time=   0.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-19.999 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-20.629 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=5, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-17.249 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.8;, score=-19.632 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-21.811 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-20.584 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-19.491 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-21.045 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-19.922 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.85;, score=-19.463 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-20.575 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-20.402 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-19.822 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-19.549 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.9;, score=-19.729 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-20.302 total time=   0.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-20.855 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-20.771 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-18.668 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=0.95;, score=-20.304 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-21.720 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-20.365 total time=   0.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-21.816 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=50, model1__subsample=1.0;, score=-19.611 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-16.872 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-19.410 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-18.787 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-18.167 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-17.087 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.5;, score=-18.946 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-18.090 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-18.850 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-17.629 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.618;, score=-18.301 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-17.527 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-18.324 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-18.447 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-17.955 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.8;, score=-18.907 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-17.756 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-18.918 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-17.355 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-18.505 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.85;, score=-17.398 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-19.006 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-17.570 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-19.175 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-17.798 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.9;, score=-18.168 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-17.368 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-17.660 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-18.448 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-17.300 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=0.95;, score=-17.767 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-18.322 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-18.336 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-18.277 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-18.436 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=100, model1__subsample=1.0;, score=-18.519 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-17.049 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-17.330 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-17.259 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-17.395 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.5;, score=-18.008 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-18.563 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-18.759 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-17.508 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-16.788 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.618;, score=-17.484 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-16.875 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-17.557 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-17.440 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-17.476 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.8;, score=-16.923 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-17.832 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-16.582 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-16.726 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-16.876 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.85;, score=-17.232 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-16.343 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-18.063 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-16.738 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-17.149 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.9;, score=-17.042 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-17.154 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-18.410 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-16.906 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-17.574 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=0.95;, score=-16.653 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-16.837 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-17.605 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-17.642 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-16.589 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=150, model1__subsample=1.0;, score=-16.535 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-16.853 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-17.406 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-16.934 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-17.200 total time=   0.6s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.5;, score=-17.212 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-16.236 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-17.501 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-17.584 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-16.623 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.618;, score=-16.413 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-17.275 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-16.983 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-15.768 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-16.005 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.8;, score=-16.255 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-15.421 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-17.067 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-17.159 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-15.976 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.85;, score=-16.567 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-16.101 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-17.112 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-17.069 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-16.620 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.9;, score=-16.912 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-15.875 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-16.858 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-15.931 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-16.579 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=0.95;, score=-15.571 total time=   1.1s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-19.046 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-16.116 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-19.398 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-16.983 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-19.048 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-18.628 total time=   0.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-18.299 total time=   0.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.5;, score=-19.605 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-19.361 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-18.207 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-17.438 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-16.731 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-17.990 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.618;, score=-18.230 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=log2, model1__n_estimators=200, model1__subsample=1.0;, score=-16.394 total time=   0.9s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-18.463 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-18.000 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-18.404 total time=   0.3s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-18.264 total time=   0.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.8;, score=-18.517 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-20.164 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-19.350 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-19.082 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-18.498 total time=   0.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.85;, score=-18.035 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-19.648 total time=   0.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-17.945 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-18.583 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-18.311 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.9;, score=-18.873 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-17.634 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-19.317 total time=   0.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-18.847 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-18.054 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=0.95;, score=-17.772 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-18.493 total time=   0.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-18.359 total time=   0.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-18.567 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-18.491 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=50, model1__subsample=1.0;, score=-18.602 total time=   0.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-16.540 total time=   0.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-17.905 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-18.621 total time=   0.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-16.371 total time=   0.5s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.5;, score=-17.664 total time=   0.5s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-17.735 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-16.456 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-16.241 total time=   0.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-17.616 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.618;, score=-16.920 total time=   0.5s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-16.589 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-17.718 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-17.243 total time=   0.5s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-16.845 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.8;, score=-16.693 total time=   0.6s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-16.087 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-16.803 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-17.833 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-16.099 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.85;, score=-16.895 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-16.060 total time=   0.7s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-17.655 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-16.907 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-16.840 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.9;, score=-16.917 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-16.204 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-17.014 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-16.008 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-17.255 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=0.95;, score=-16.659 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-16.507 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-17.528 total time=   0.7s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-16.797 total time=   0.7s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-16.774 total time=   0.7s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=100, model1__subsample=1.0;, score=-16.872 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-15.950 total time=   0.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-17.514 total time=   0.6s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-17.220 total time=   0.6s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-17.289 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.5;, score=-16.512 total time=   0.7s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-16.132 total time=   0.8s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-17.489 total time=   0.8s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-17.071 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-16.290 total time=   0.8s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.618;, score=-16.715 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-15.117 total time=   0.9s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-17.257 total time=   0.9s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-16.699 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-15.624 total time=   1.0s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.8;, score=-16.523 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-15.494 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-17.182 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-16.833 total time=   1.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-15.763 total time=   1.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.85;, score=-16.363 total time=   1.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-15.671 total time=   1.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-16.719 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-17.398 total time=   1.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-15.980 total time=   1.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.9;, score=-16.410 total time=   1.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-17.165 total time=   1.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-16.559 total time=   1.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-15.813 total time=   1.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-15.594 total time=   1.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=0.95;, score=-16.420 total time=   1.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-15.413 total time=   1.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-16.463 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-16.988 total time=   1.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-16.414 total time=   0.9s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-16.076 total time=   1.1s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-17.254 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=150, model1__subsample=1.0;, score=-16.670 total time=   1.1s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-16.561 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-16.570 total time=   0.8s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.5;, score=-16.262 total time=   0.8s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-14.915 total time=   1.0s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-16.578 total time=   1.0s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-16.617 total time=   0.9s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-16.567 total time=   1.0s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.618;, score=-15.417 total time=   1.0s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-15.474 total time=   1.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-16.980 total time=   1.2s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-16.383 total time=   1.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-16.658 total time=   1.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.8;, score=-16.071 total time=   1.2s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-15.455 total time=   1.2s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-16.539 total time=   1.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-16.162 total time=   1.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-15.336 total time=   1.3s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.85;, score=-16.263 total time=   1.3s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-15.428 total time=   1.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-16.856 total time=   1.3s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-16.516 total time=   1.3s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-15.464 total time=   1.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.9;, score=-15.622 total time=   1.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-15.290 total time=   1.5s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-16.766 total time=   1.6s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-16.314 total time=   1.4s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-15.581 total time=   1.4s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=0.95;, score=-16.311 total time=   1.4s\n",
+      "[CV 1/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-15.682 total time=   1.4s\n",
+      "[CV 2/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-16.456 total time=   1.4s\n",
+      "[CV 3/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-16.420 total time=   1.2s\n",
+      "[CV 4/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-16.117 total time=   1.2s\n",
+      "[CV 5/5] END model1__criterion=friedman_mse, model1__learning_rate=0.2, model1__max_depth=8, model1__max_features=sqrt, model1__n_estimators=200, model1__subsample=1.0;, score=-16.080 total time=   1.0s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-8 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-8 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-8 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-8 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-8 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
+       "             estimator=Pipeline(steps=[(&#x27;Data_processing&#x27;,\n",
+       "                                        Pipeline(steps=[(&#x27;step1&#x27;,\n",
+       "                                                         ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                                                                           transformers=[(&#x27;col_tnf&#x27;,\n",
+       "                                                                                          OneHotEncoder(drop=&#x27;first&#x27;,\n",
+       "                                                                                                        sparse_output=False),\n",
+       "                                                                                          [0,\n",
+       "                                                                                           2])])),\n",
+       "                                                        (&#x27;step2&#x27;,\n",
+       "                                                         StandardScaler())])),\n",
+       "                                       (&#x27;model1&#x27;,\n",
+       "                                        GradientBoostingRegressor())]),\n",
+       "             n_jobs=-1,\n",
+       "             param_grid={&#x27;model1__criterion&#x27;: [&#x27;friedman_mse&#x27;],\n",
+       "                         &#x27;model1__learning_rate&#x27;: [0.01, 0.025, 0.05, 0.075,\n",
+       "                                                   0.1, 0.15, 0.2],\n",
+       "                         &#x27;model1__max_depth&#x27;: [3, 5, 8],\n",
+       "                         &#x27;model1__max_features&#x27;: [&#x27;log2&#x27;, &#x27;sqrt&#x27;],\n",
+       "                         &#x27;model1__n_estimators&#x27;: [50, 100, 150, 200],\n",
+       "                         &#x27;model1__subsample&#x27;: [0.5, 0.618, 0.8, 0.85, 0.9, 0.95,\n",
+       "                                               1.0]},\n",
+       "             scoring=&#x27;neg_mean_absolute_error&#x27;, verbose=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-60\" type=\"checkbox\" ><label for=\"sk-estimator-id-60\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5,\n",
+       "             estimator=Pipeline(steps=[(&#x27;Data_processing&#x27;,\n",
+       "                                        Pipeline(steps=[(&#x27;step1&#x27;,\n",
+       "                                                         ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                                                                           transformers=[(&#x27;col_tnf&#x27;,\n",
+       "                                                                                          OneHotEncoder(drop=&#x27;first&#x27;,\n",
+       "                                                                                                        sparse_output=False),\n",
+       "                                                                                          [0,\n",
+       "                                                                                           2])])),\n",
+       "                                                        (&#x27;step2&#x27;,\n",
+       "                                                         StandardScaler())])),\n",
+       "                                       (&#x27;model1&#x27;,\n",
+       "                                        GradientBoostingRegressor())]),\n",
+       "             n_jobs=-1,\n",
+       "             param_grid={&#x27;model1__criterion&#x27;: [&#x27;friedman_mse&#x27;],\n",
+       "                         &#x27;model1__learning_rate&#x27;: [0.01, 0.025, 0.05, 0.075,\n",
+       "                                                   0.1, 0.15, 0.2],\n",
+       "                         &#x27;model1__max_depth&#x27;: [3, 5, 8],\n",
+       "                         &#x27;model1__max_features&#x27;: [&#x27;log2&#x27;, &#x27;sqrt&#x27;],\n",
+       "                         &#x27;model1__n_estimators&#x27;: [50, 100, 150, 200],\n",
+       "                         &#x27;model1__subsample&#x27;: [0.5, 0.618, 0.8, 0.85, 0.9, 0.95,\n",
+       "                                               1.0]},\n",
+       "             scoring=&#x27;neg_mean_absolute_error&#x27;, verbose=3)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-61\" type=\"checkbox\" ><label for=\"sk-estimator-id-61\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;Data_processing&#x27;,\n",
+       "                 Pipeline(steps=[(&#x27;step1&#x27;,\n",
+       "                                  ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                                                    transformers=[(&#x27;col_tnf&#x27;,\n",
+       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;,\n",
+       "                                                                                 sparse_output=False),\n",
+       "                                                                   [0, 2])])),\n",
+       "                                 (&#x27;step2&#x27;, StandardScaler())])),\n",
+       "                (&#x27;model1&#x27;, GradientBoostingRegressor())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-62\" type=\"checkbox\" ><label for=\"sk-estimator-id-62\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;Data_processing: Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Data_processing: Pipeline</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;step1&#x27;,\n",
+       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                                   transformers=[(&#x27;col_tnf&#x27;,\n",
+       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
+       "                                                                sparse_output=False),\n",
+       "                                                  [0, 2])])),\n",
+       "                (&#x27;step2&#x27;, StandardScaler())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-63\" type=\"checkbox\" ><label for=\"sk-estimator-id-63\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;step1: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for step1: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                  transformers=[(&#x27;col_tnf&#x27;,\n",
+       "                                 OneHotEncoder(drop=&#x27;first&#x27;,\n",
+       "                                               sparse_output=False),\n",
+       "                                 [0, 2])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-64\" type=\"checkbox\" ><label for=\"sk-estimator-id-64\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">col_tnf</label><div class=\"sk-toggleable__content fitted\"><pre>[0, 2]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-65\" type=\"checkbox\" ><label for=\"sk-estimator-id-65\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, sparse_output=False)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-66\" type=\"checkbox\" ><label for=\"sk-estimator-id-66\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">remainder</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;availability&#x27;, &#x27;total_sqft&#x27;, &#x27;bath&#x27;, &#x27;bhk&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-67\" type=\"checkbox\" ><label for=\"sk-estimator-id-67\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">passthrough</label><div class=\"sk-toggleable__content fitted\"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-68\" type=\"checkbox\" ><label for=\"sk-estimator-id-68\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-69\" type=\"checkbox\" ><label for=\"sk-estimator-id-69\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;GradientBoostingRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html\">?<span>Documentation for GradientBoostingRegressor</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingRegressor()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=5,\n",
+       "             estimator=Pipeline(steps=[('Data_processing',\n",
+       "                                        Pipeline(steps=[('step1',\n",
+       "                                                         ColumnTransformer(remainder='passthrough',\n",
+       "                                                                           transformers=[('col_tnf',\n",
+       "                                                                                          OneHotEncoder(drop='first',\n",
+       "                                                                                                        sparse_output=False),\n",
+       "                                                                                          [0,\n",
+       "                                                                                           2])])),\n",
+       "                                                        ('step2',\n",
+       "                                                         StandardScaler())])),\n",
+       "                                       ('model1',\n",
+       "                                        GradientBoostingRegressor())]),\n",
+       "             n_jobs=-1,\n",
+       "             param_grid={'model1__criterion': ['friedman_mse'],\n",
+       "                         'model1__learning_rate': [0.01, 0.025, 0.05, 0.075,\n",
+       "                                                   0.1, 0.15, 0.2],\n",
+       "                         'model1__max_depth': [3, 5, 8],\n",
+       "                         'model1__max_features': ['log2', 'sqrt'],\n",
+       "                         'model1__n_estimators': [50, 100, 150, 200],\n",
+       "                         'model1__subsample': [0.5, 0.618, 0.8, 0.85, 0.9, 0.95,\n",
+       "                                               1.0]},\n",
+       "             scoring='neg_mean_absolute_error', verbose=3)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model1 = GradientBoostingRegressor()\n",
+    "model1_pipe = Pipeline([('Data_processing' , data_pipeline) , ('model1' , model1)])\n",
+    "parameters = {\n",
+    "    \"model1__learning_rate\": [0.01, 0.025, 0.05, 0.075, 0.1, 0.15, 0.2],\n",
+    "    \"model1__max_depth\":[3,5,8],\n",
+    "    \"model1__max_features\":[\"log2\",\"sqrt\"],\n",
+    "    \"model1__criterion\": [\"friedman_mse\"],\n",
+    "    \"model1__subsample\":[0.5, 0.618, 0.8, 0.85, 0.9, 0.95, 1.0],\n",
+    "    \"model1__n_estimators\":[50, 100, 150, 200]\n",
+    "}\n",
+    "\n",
+    "gscv1 = GridSearchCV(model1_pipe , parameters , scoring='neg_mean_absolute_error' , cv = 5 , n_jobs=-1 , verbose = 3)\n",
+    "gscv1.fit(x_train , y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pipeline(steps=[('Data_processing',\n",
+      "                 Pipeline(steps=[('step1',\n",
+      "                                  ColumnTransformer(remainder='passthrough',\n",
+      "                                                    transformers=[('col_tnf',\n",
+      "                                                                   OneHotEncoder(drop='first',\n",
+      "                                                                                 sparse_output=False),\n",
+      "                                                                   [0, 2])])),\n",
+      "                                 ('step2', StandardScaler())])),\n",
+      "                ('model1',\n",
+      "                 GradientBoostingRegressor(learning_rate=0.15, max_depth=8,\n",
+      "                                           max_features='sqrt',\n",
+      "                                           n_estimators=200, subsample=0.85))])\n",
+      "--------------------\n",
+      "Best score :-15.883028237152882\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(gscv1.best_estimator_)\n",
+    "print('--'*10)\n",
+    "print(f'Best score :{gscv1.best_score_}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 84 candidates, totalling 420 fits\n",
+      "[CV 2/5] END model2__learning_rate=0.01, model2__loss=linear, model2__n_estimators=50;, score=-26.372 total time=   2.1s\n",
+      "[CV 5/5] END model2__learning_rate=0.01, model2__loss=linear, model2__n_estimators=50;, score=-27.088 total time=   2.1s\n",
+      "[CV 3/5] END model2__learning_rate=0.01, model2__loss=linear, model2__n_estimators=50;, score=-26.715 total time=   2.1s\n",
+      "[CV 1/5] END model2__learning_rate=0.01, model2__loss=linear, model2__n_estimators=50;, score=-25.348 total time=   2.2s\n",
+      "[CV 4/5] END model2__learning_rate=0.01, model2__loss=linear, model2__n_estimators=50;, score=-25.086 total time=   2.2s\n",
+      "[CV 2/5] END model2__learning_rate=0.01, model2__loss=linear, model2__n_estimators=100;, score=-26.744 total time=   4.0s\n",
+      "[CV 1/5] END model2__learning_rate=0.01, model2__loss=linear, model2__n_estimators=100;, score=-25.348 total time=   4.2s\n",
+      "[CV 3/5] END model2__learning_rate=0.01, model2__loss=linear, model2__n_estimators=100;, score=-26.622 total time=   4.3s\n",
+      "[CV 5/5] END model2__learning_rate=0.01, model2__loss=linear, model2__n_estimators=100;, score=-26.748 total time=   3.8s\n",
+      "[CV 4/5] END model2__learning_rate=0.01, model2__loss=linear, model2__n_estimators=100;, score=-25.202 total time=   4.0s\n",
+      "[CV 2/5] END model2__learning_rate=0.01, model2__loss=linear, model2__n_estimators=150;, score=-26.890 total time=   5.8s\n",
+      "[CV 3/5] END model2__learning_rate=0.01, model2__loss=linear, model2__n_estimators=150;, score=-26.645 total time=   5.9s\n",
+      "[CV 1/5] END model2__learning_rate=0.01, model2__loss=linear, model2__n_estimators=150;, score=-25.387 total time=   6.3s\n",
+      "[CV 4/5] END model2__learning_rate=0.01, model2__loss=linear, model2__n_estimators=150;, score=-24.894 total time=   6.1s\n",
+      "[CV 5/5] END model2__learning_rate=0.01, model2__loss=linear, model2__n_estimators=150;, score=-26.645 total time=   5.9s\n",
+      "[CV 1/5] END model2__learning_rate=0.01, model2__loss=square, model2__n_estimators=50;, score=-24.991 total time=   1.9s\n",
+      "[CV 2/5] END model2__learning_rate=0.01, model2__loss=square, model2__n_estimators=50;, score=-26.346 total time=   1.8s\n",
+      "[CV 3/5] END model2__learning_rate=0.01, model2__loss=square, model2__n_estimators=50;, score=-26.766 total time=   1.8s\n",
+      "[CV 1/5] END model2__learning_rate=0.01, model2__loss=linear, model2__n_estimators=200;, score=-25.515 total time=   7.7s\n",
+      "[CV 4/5] END model2__learning_rate=0.01, model2__loss=square, model2__n_estimators=50;, score=-25.298 total time=   1.9s\n",
+      "[CV 2/5] END model2__learning_rate=0.01, model2__loss=linear, model2__n_estimators=200;, score=-26.891 total time=   7.6s\n",
+      "[CV 3/5] END model2__learning_rate=0.01, model2__loss=linear, model2__n_estimators=200;, score=-26.532 total time=   7.6s\n",
+      "[CV 5/5] END model2__learning_rate=0.01, model2__loss=square, model2__n_estimators=50;, score=-27.005 total time=   2.2s\n",
+      "[CV 1/5] END model2__learning_rate=0.01, model2__loss=square, model2__n_estimators=100;, score=-25.314 total time=   3.7s\n",
+      "[CV 2/5] END model2__learning_rate=0.01, model2__loss=square, model2__n_estimators=100;, score=-26.803 total time=   3.8s\n",
+      "[CV 5/5] END model2__learning_rate=0.01, model2__loss=linear, model2__n_estimators=200;, score=-26.698 total time=   7.8s\n",
+      "[CV 3/5] END model2__learning_rate=0.01, model2__loss=square, model2__n_estimators=100;, score=-26.577 total time=   3.8s\n",
+      "[CV 4/5] END model2__learning_rate=0.01, model2__loss=linear, model2__n_estimators=200;, score=-24.803 total time=   8.2s\n",
+      "[CV 4/5] END model2__learning_rate=0.01, model2__loss=square, model2__n_estimators=100;, score=-25.254 total time=   3.7s\n",
+      "[CV 5/5] END model2__learning_rate=0.01, model2__loss=square, model2__n_estimators=100;, score=-26.913 total time=   4.0s\n",
+      "[CV 1/5] END model2__learning_rate=0.01, model2__loss=square, model2__n_estimators=150;, score=-25.258 total time=   5.9s\n",
+      "[CV 2/5] END model2__learning_rate=0.01, model2__loss=square, model2__n_estimators=150;, score=-26.809 total time=   6.5s\n",
+      "[CV 3/5] END model2__learning_rate=0.01, model2__loss=square, model2__n_estimators=150;, score=-26.447 total time=   6.8s\n",
+      "[CV 4/5] END model2__learning_rate=0.01, model2__loss=square, model2__n_estimators=150;, score=-24.951 total time=   7.1s\n",
+      "[CV 5/5] END model2__learning_rate=0.01, model2__loss=square, model2__n_estimators=150;, score=-26.679 total time=   7.2s\n",
+      "[CV 1/5] END model2__learning_rate=0.01, model2__loss=exponential, model2__n_estimators=50;, score=-25.386 total time=   2.4s\n",
+      "[CV 1/5] END model2__learning_rate=0.01, model2__loss=square, model2__n_estimators=200;, score=-25.422 total time=   9.3s\n",
+      "[CV 2/5] END model2__learning_rate=0.01, model2__loss=exponential, model2__n_estimators=50;, score=-26.250 total time=   2.5s\n",
+      "[CV 3/5] END model2__learning_rate=0.01, model2__loss=exponential, model2__n_estimators=50;, score=-26.737 total time=   2.4s\n",
+      "[CV 2/5] END model2__learning_rate=0.01, model2__loss=square, model2__n_estimators=200;, score=-26.686 total time=   9.2s\n",
+      "[CV 3/5] END model2__learning_rate=0.01, model2__loss=square, model2__n_estimators=200;, score=-26.658 total time=   9.2s\n",
+      "[CV 4/5] END model2__learning_rate=0.01, model2__loss=exponential, model2__n_estimators=50;, score=-25.580 total time=   1.9s\n",
+      "[CV 5/5] END model2__learning_rate=0.01, model2__loss=exponential, model2__n_estimators=50;, score=-26.830 total time=   1.9s\n",
+      "[CV 4/5] END model2__learning_rate=0.01, model2__loss=square, model2__n_estimators=200;, score=-25.116 total time=   9.0s\n",
+      "[CV 2/5] END model2__learning_rate=0.01, model2__loss=exponential, model2__n_estimators=100;, score=-26.602 total time=   3.6s\n",
+      "[CV 1/5] END model2__learning_rate=0.01, model2__loss=exponential, model2__n_estimators=100;, score=-25.356 total time=   4.1s\n",
+      "[CV 3/5] END model2__learning_rate=0.01, model2__loss=exponential, model2__n_estimators=100;, score=-26.684 total time=   3.8s\n",
+      "[CV 5/5] END model2__learning_rate=0.01, model2__loss=square, model2__n_estimators=200;, score=-27.227 total time=   8.6s\n",
+      "[CV 4/5] END model2__learning_rate=0.01, model2__loss=exponential, model2__n_estimators=100;, score=-25.274 total time=   4.0s\n",
+      "[CV 5/5] END model2__learning_rate=0.01, model2__loss=exponential, model2__n_estimators=100;, score=-26.652 total time=   3.8s\n",
+      "[CV 1/5] END model2__learning_rate=0.01, model2__loss=exponential, model2__n_estimators=150;, score=-25.268 total time=   5.7s\n",
+      "[CV 2/5] END model2__learning_rate=0.01, model2__loss=exponential, model2__n_estimators=150;, score=-26.732 total time=   5.9s\n",
+      "[CV 3/5] END model2__learning_rate=0.01, model2__loss=exponential, model2__n_estimators=150;, score=-26.630 total time=   5.9s\n",
+      "[CV 4/5] END model2__learning_rate=0.01, model2__loss=exponential, model2__n_estimators=150;, score=-24.954 total time=   6.1s\n",
+      "[CV 5/5] END model2__learning_rate=0.01, model2__loss=exponential, model2__n_estimators=150;, score=-26.748 total time=   6.1s\n",
+      "[CV 1/5] END model2__learning_rate=0.025, model2__loss=linear, model2__n_estimators=50;, score=-25.364 total time=   1.9s\n",
+      "[CV 2/5] END model2__learning_rate=0.025, model2__loss=linear, model2__n_estimators=50;, score=-26.787 total time=   1.8s\n",
+      "[CV 3/5] END model2__learning_rate=0.025, model2__loss=linear, model2__n_estimators=50;, score=-26.749 total time=   1.9s\n",
+      "[CV 1/5] END model2__learning_rate=0.01, model2__loss=exponential, model2__n_estimators=200;, score=-25.473 total time=   7.9s\n",
+      "[CV 2/5] END model2__learning_rate=0.01, model2__loss=exponential, model2__n_estimators=200;, score=-26.736 total time=   8.0s\n",
+      "[CV 3/5] END model2__learning_rate=0.01, model2__loss=exponential, model2__n_estimators=200;, score=-26.548 total time=   8.2s\n",
+      "[CV 4/5] END model2__learning_rate=0.025, model2__loss=linear, model2__n_estimators=50;, score=-24.982 total time=   1.9s\n",
+      "[CV 5/5] END model2__learning_rate=0.025, model2__loss=linear, model2__n_estimators=50;, score=-26.811 total time=   2.0s\n",
+      "[CV 4/5] END model2__learning_rate=0.01, model2__loss=exponential, model2__n_estimators=200;, score=-24.844 total time=   7.9s\n",
+      "[CV 1/5] END model2__learning_rate=0.025, model2__loss=linear, model2__n_estimators=100;, score=-25.591 total time=   3.9s\n",
+      "[CV 5/5] END model2__learning_rate=0.01, model2__loss=exponential, model2__n_estimators=200;, score=-26.769 total time=   7.4s\n",
+      "[CV 2/5] END model2__learning_rate=0.025, model2__loss=linear, model2__n_estimators=100;, score=-26.933 total time=   3.7s\n",
+      "[CV 3/5] END model2__learning_rate=0.025, model2__loss=linear, model2__n_estimators=100;, score=-26.444 total time=   3.8s\n",
+      "[CV 4/5] END model2__learning_rate=0.025, model2__loss=linear, model2__n_estimators=100;, score=-24.917 total time=   3.8s\n",
+      "[CV 5/5] END model2__learning_rate=0.025, model2__loss=linear, model2__n_estimators=100;, score=-26.747 total time=   3.9s\n",
+      "[CV 1/5] END model2__learning_rate=0.025, model2__loss=linear, model2__n_estimators=150;, score=-25.888 total time=   5.9s\n",
+      "[CV 2/5] END model2__learning_rate=0.025, model2__loss=linear, model2__n_estimators=150;, score=-27.203 total time=   6.1s\n",
+      "[CV 3/5] END model2__learning_rate=0.025, model2__loss=linear, model2__n_estimators=150;, score=-27.176 total time=   6.4s\n",
+      "[CV 4/5] END model2__learning_rate=0.025, model2__loss=linear, model2__n_estimators=150;, score=-25.419 total time=   6.2s\n",
+      "[CV 5/5] END model2__learning_rate=0.025, model2__loss=linear, model2__n_estimators=150;, score=-27.821 total time=   6.3s\n",
+      "[CV 2/5] END model2__learning_rate=0.025, model2__loss=square, model2__n_estimators=50;, score=-26.879 total time=   2.0s\n",
+      "[CV 1/5] END model2__learning_rate=0.025, model2__loss=square, model2__n_estimators=50;, score=-25.002 total time=   2.1s\n",
+      "[CV 3/5] END model2__learning_rate=0.025, model2__loss=square, model2__n_estimators=50;, score=-26.390 total time=   1.9s\n",
+      "[CV 3/5] END model2__learning_rate=0.025, model2__loss=linear, model2__n_estimators=200;, score=-27.587 total time=   7.9s\n",
+      "[CV 2/5] END model2__learning_rate=0.025, model2__loss=linear, model2__n_estimators=200;, score=-27.336 total time=   8.0s\n",
+      "[CV 1/5] END model2__learning_rate=0.025, model2__loss=linear, model2__n_estimators=200;, score=-26.051 total time=   8.4s\n",
+      "[CV 5/5] END model2__learning_rate=0.025, model2__loss=square, model2__n_estimators=50;, score=-26.822 total time=   2.0s\n",
+      "[CV 4/5] END model2__learning_rate=0.025, model2__loss=square, model2__n_estimators=50;, score=-24.807 total time=   2.0s\n",
+      "[CV 4/5] END model2__learning_rate=0.025, model2__loss=linear, model2__n_estimators=200;, score=-25.302 total time=   7.9s\n",
+      "[CV 1/5] END model2__learning_rate=0.025, model2__loss=square, model2__n_estimators=100;, score=-25.628 total time=   4.2s\n",
+      "[CV 2/5] END model2__learning_rate=0.025, model2__loss=square, model2__n_estimators=100;, score=-26.727 total time=   4.1s\n",
+      "[CV 3/5] END model2__learning_rate=0.025, model2__loss=square, model2__n_estimators=100;, score=-26.781 total time=   4.2s\n",
+      "[CV 4/5] END model2__learning_rate=0.025, model2__loss=square, model2__n_estimators=100;, score=-25.207 total time=   4.1s\n",
+      "[CV 5/5] END model2__learning_rate=0.025, model2__loss=linear, model2__n_estimators=200;, score=-28.541 total time=   8.7s\n",
+      "[CV 5/5] END model2__learning_rate=0.025, model2__loss=square, model2__n_estimators=100;, score=-27.716 total time=   4.0s\n",
+      "[CV 1/5] END model2__learning_rate=0.025, model2__loss=square, model2__n_estimators=150;, score=-26.616 total time=   5.9s\n",
+      "[CV 2/5] END model2__learning_rate=0.025, model2__loss=square, model2__n_estimators=150;, score=-27.046 total time=   6.2s\n",
+      "[CV 3/5] END model2__learning_rate=0.025, model2__loss=square, model2__n_estimators=150;, score=-28.370 total time=   6.1s\n",
+      "[CV 4/5] END model2__learning_rate=0.025, model2__loss=square, model2__n_estimators=150;, score=-26.414 total time=   6.2s\n",
+      "[CV 5/5] END model2__learning_rate=0.025, model2__loss=square, model2__n_estimators=150;, score=-29.287 total time=   6.3s\n",
+      "[CV 1/5] END model2__learning_rate=0.025, model2__loss=exponential, model2__n_estimators=50;, score=-25.198 total time=   2.0s\n",
+      "[CV 2/5] END model2__learning_rate=0.025, model2__loss=exponential, model2__n_estimators=50;, score=-26.655 total time=   1.8s\n",
+      "[CV 1/5] END model2__learning_rate=0.025, model2__loss=square, model2__n_estimators=200;, score=-26.720 total time=   7.8s\n",
+      "[CV 3/5] END model2__learning_rate=0.025, model2__loss=exponential, model2__n_estimators=50;, score=-27.133 total time=   2.0s\n",
+      "[CV 2/5] END model2__learning_rate=0.025, model2__loss=square, model2__n_estimators=200;, score=-28.136 total time=   8.0s\n",
+      "[CV 3/5] END model2__learning_rate=0.025, model2__loss=square, model2__n_estimators=200;, score=-28.638 total time=   8.0s\n",
+      "[CV 4/5] END model2__learning_rate=0.025, model2__loss=exponential, model2__n_estimators=50;, score=-25.101 total time=   2.0s\n",
+      "[CV 5/5] END model2__learning_rate=0.025, model2__loss=exponential, model2__n_estimators=50;, score=-26.817 total time=   1.9s\n",
+      "[CV 4/5] END model2__learning_rate=0.025, model2__loss=square, model2__n_estimators=200;, score=-26.590 total time=   7.5s\n",
+      "[CV 1/5] END model2__learning_rate=0.025, model2__loss=exponential, model2__n_estimators=100;, score=-25.408 total time=   4.0s\n",
+      "[CV 5/5] END model2__learning_rate=0.025, model2__loss=square, model2__n_estimators=200;, score=-28.912 total time=   7.6s\n",
+      "[CV 3/5] END model2__learning_rate=0.025, model2__loss=exponential, model2__n_estimators=100;, score=-26.572 total time=   3.6s\n",
+      "[CV 2/5] END model2__learning_rate=0.025, model2__loss=exponential, model2__n_estimators=100;, score=-26.907 total time=   4.0s\n",
+      "[CV 4/5] END model2__learning_rate=0.025, model2__loss=exponential, model2__n_estimators=100;, score=-24.833 total time=   3.8s\n",
+      "[CV 5/5] END model2__learning_rate=0.025, model2__loss=exponential, model2__n_estimators=100;, score=-26.936 total time=   4.1s\n",
+      "[CV 1/5] END model2__learning_rate=0.025, model2__loss=exponential, model2__n_estimators=150;, score=-25.826 total time=   5.9s\n",
+      "[CV 2/5] END model2__learning_rate=0.025, model2__loss=exponential, model2__n_estimators=150;, score=-27.032 total time=   6.0s\n",
+      "[CV 4/5] END model2__learning_rate=0.025, model2__loss=exponential, model2__n_estimators=150;, score=-24.995 total time=   5.7s\n",
+      "[CV 3/5] END model2__learning_rate=0.025, model2__loss=exponential, model2__n_estimators=150;, score=-26.583 total time=   5.9s\n",
+      "[CV 5/5] END model2__learning_rate=0.025, model2__loss=exponential, model2__n_estimators=150;, score=-26.968 total time=   6.1s\n",
+      "[CV 1/5] END model2__learning_rate=0.05, model2__loss=linear, model2__n_estimators=50;, score=-25.717 total time=   1.9s\n",
+      "[CV 2/5] END model2__learning_rate=0.05, model2__loss=linear, model2__n_estimators=50;, score=-27.099 total time=   2.0s\n",
+      "[CV 3/5] END model2__learning_rate=0.05, model2__loss=linear, model2__n_estimators=50;, score=-26.506 total time=   1.8s\n",
+      "[CV 1/5] END model2__learning_rate=0.025, model2__loss=exponential, model2__n_estimators=200;, score=-26.097 total time=   8.0s\n",
+      "[CV 2/5] END model2__learning_rate=0.025, model2__loss=exponential, model2__n_estimators=200;, score=-27.395 total time=   7.3s\n",
+      "[CV 3/5] END model2__learning_rate=0.025, model2__loss=exponential, model2__n_estimators=200;, score=-27.245 total time=   7.5s\n",
+      "[CV 4/5] END model2__learning_rate=0.05, model2__loss=linear, model2__n_estimators=50;, score=-24.930 total time=   2.0s\n",
+      "[CV 5/5] END model2__learning_rate=0.05, model2__loss=linear, model2__n_estimators=50;, score=-26.646 total time=   1.9s\n",
+      "[CV 4/5] END model2__learning_rate=0.025, model2__loss=exponential, model2__n_estimators=200;, score=-25.398 total time=   7.9s\n",
+      "[CV 1/5] END model2__learning_rate=0.05, model2__loss=linear, model2__n_estimators=100;, score=-26.074 total time=   3.7s\n",
+      "[CV 2/5] END model2__learning_rate=0.05, model2__loss=linear, model2__n_estimators=100;, score=-27.306 total time=   3.8s\n",
+      "[CV 5/5] END model2__learning_rate=0.025, model2__loss=exponential, model2__n_estimators=200;, score=-27.452 total time=   7.6s\n",
+      "[CV 3/5] END model2__learning_rate=0.05, model2__loss=linear, model2__n_estimators=100;, score=-27.591 total time=   3.9s\n",
+      "[CV 4/5] END model2__learning_rate=0.05, model2__loss=linear, model2__n_estimators=100;, score=-25.225 total time=   3.6s\n",
+      "[CV 5/5] END model2__learning_rate=0.05, model2__loss=linear, model2__n_estimators=100;, score=-28.105 total time=   3.8s\n",
+      "[CV 1/5] END model2__learning_rate=0.05, model2__loss=linear, model2__n_estimators=150;, score=-26.989 total time=   5.6s\n",
+      "[CV 2/5] END model2__learning_rate=0.05, model2__loss=linear, model2__n_estimators=150;, score=-28.638 total time=   5.1s\n",
+      "[CV 3/5] END model2__learning_rate=0.05, model2__loss=linear, model2__n_estimators=150;, score=-29.079 total time=   5.5s\n",
+      "[CV 4/5] END model2__learning_rate=0.05, model2__loss=linear, model2__n_estimators=150;, score=-26.515 total time=   5.4s\n",
+      "[CV 5/5] END model2__learning_rate=0.05, model2__loss=linear, model2__n_estimators=150;, score=-29.297 total time=   5.7s\n",
+      "[CV 1/5] END model2__learning_rate=0.05, model2__loss=square, model2__n_estimators=50;, score=-25.436 total time=   1.9s\n",
+      "[CV 2/5] END model2__learning_rate=0.05, model2__loss=square, model2__n_estimators=50;, score=-27.036 total time=   1.7s\n",
+      "[CV 1/5] END model2__learning_rate=0.05, model2__loss=linear, model2__n_estimators=200;, score=-26.971 total time=   7.1s\n",
+      "[CV 2/5] END model2__learning_rate=0.05, model2__loss=linear, model2__n_estimators=200;, score=-29.073 total time=   7.0s\n",
+      "[CV 3/5] END model2__learning_rate=0.05, model2__loss=square, model2__n_estimators=50;, score=-26.669 total time=   2.1s\n",
+      "[CV 3/5] END model2__learning_rate=0.05, model2__loss=linear, model2__n_estimators=200;, score=-29.721 total time=   7.2s\n",
+      "[CV 4/5] END model2__learning_rate=0.05, model2__loss=square, model2__n_estimators=50;, score=-25.134 total time=   2.0s\n",
+      "[CV 5/5] END model2__learning_rate=0.05, model2__loss=square, model2__n_estimators=50;, score=-27.369 total time=   2.0s\n",
+      "[CV 4/5] END model2__learning_rate=0.05, model2__loss=linear, model2__n_estimators=200;, score=-26.742 total time=   7.5s\n",
+      "[CV 1/5] END model2__learning_rate=0.05, model2__loss=square, model2__n_estimators=100;, score=-26.845 total time=   3.7s\n",
+      "[CV 2/5] END model2__learning_rate=0.05, model2__loss=square, model2__n_estimators=100;, score=-28.270 total time=   3.7s\n",
+      "[CV 3/5] END model2__learning_rate=0.05, model2__loss=square, model2__n_estimators=100;, score=-28.576 total time=   3.9s\n",
+      "[CV 5/5] END model2__learning_rate=0.05, model2__loss=linear, model2__n_estimators=200;, score=-29.225 total time=   7.6s\n",
+      "[CV 4/5] END model2__learning_rate=0.05, model2__loss=square, model2__n_estimators=100;, score=-26.877 total time=   4.0s\n",
+      "[CV 5/5] END model2__learning_rate=0.05, model2__loss=square, model2__n_estimators=100;, score=-29.187 total time=   3.9s\n",
+      "[CV 1/5] END model2__learning_rate=0.05, model2__loss=square, model2__n_estimators=150;, score=-26.872 total time=   6.1s\n",
+      "[CV 2/5] END model2__learning_rate=0.05, model2__loss=square, model2__n_estimators=150;, score=-28.735 total time=   6.1s\n",
+      "[CV 3/5] END model2__learning_rate=0.05, model2__loss=square, model2__n_estimators=150;, score=-29.280 total time=   6.2s\n",
+      "[CV 4/5] END model2__learning_rate=0.05, model2__loss=square, model2__n_estimators=150;, score=-26.721 total time=   6.2s\n",
+      "[CV 5/5] END model2__learning_rate=0.05, model2__loss=square, model2__n_estimators=150;, score=-29.001 total time=   6.4s\n",
+      "[CV 1/5] END model2__learning_rate=0.05, model2__loss=exponential, model2__n_estimators=50;, score=-25.659 total time=   2.3s\n",
+      "[CV 1/5] END model2__learning_rate=0.05, model2__loss=square, model2__n_estimators=200;, score=-27.080 total time=   8.0s\n",
+      "[CV 2/5] END model2__learning_rate=0.05, model2__loss=exponential, model2__n_estimators=50;, score=-26.975 total time=   2.5s\n",
+      "[CV 2/5] END model2__learning_rate=0.05, model2__loss=square, model2__n_estimators=200;, score=-28.544 total time=   8.0s\n",
+      "[CV 3/5] END model2__learning_rate=0.05, model2__loss=exponential, model2__n_estimators=50;, score=-26.439 total time=   2.3s\n",
+      "[CV 3/5] END model2__learning_rate=0.05, model2__loss=square, model2__n_estimators=200;, score=-29.546 total time=   8.5s\n",
+      "[CV 4/5] END model2__learning_rate=0.05, model2__loss=exponential, model2__n_estimators=50;, score=-24.858 total time=   2.2s\n",
+      "[CV 5/5] END model2__learning_rate=0.05, model2__loss=exponential, model2__n_estimators=50;, score=-26.853 total time=   2.2s\n",
+      "[CV 4/5] END model2__learning_rate=0.05, model2__loss=square, model2__n_estimators=200;, score=-26.514 total time=   8.0s\n",
+      "[CV 1/5] END model2__learning_rate=0.05, model2__loss=exponential, model2__n_estimators=100;, score=-25.829 total time=   4.2s\n",
+      "[CV 2/5] END model2__learning_rate=0.05, model2__loss=exponential, model2__n_estimators=100;, score=-27.257 total time=   4.2s\n",
+      "[CV 3/5] END model2__learning_rate=0.05, model2__loss=exponential, model2__n_estimators=100;, score=-27.179 total time=   4.1s\n",
+      "[CV 5/5] END model2__learning_rate=0.05, model2__loss=square, model2__n_estimators=200;, score=-28.813 total time=   8.1s\n",
+      "[CV 4/5] END model2__learning_rate=0.05, model2__loss=exponential, model2__n_estimators=100;, score=-25.305 total time=   4.3s\n",
+      "[CV 5/5] END model2__learning_rate=0.05, model2__loss=exponential, model2__n_estimators=100;, score=-27.313 total time=   4.3s\n",
+      "[CV 1/5] END model2__learning_rate=0.05, model2__loss=exponential, model2__n_estimators=150;, score=-26.909 total time=   6.4s\n",
+      "[CV 2/5] END model2__learning_rate=0.05, model2__loss=exponential, model2__n_estimators=150;, score=-27.855 total time=   6.2s\n",
+      "[CV 3/5] END model2__learning_rate=0.05, model2__loss=exponential, model2__n_estimators=150;, score=-27.931 total time=   6.1s\n",
+      "[CV 4/5] END model2__learning_rate=0.05, model2__loss=exponential, model2__n_estimators=150;, score=-26.456 total time=   6.6s\n",
+      "[CV 5/5] END model2__learning_rate=0.05, model2__loss=exponential, model2__n_estimators=150;, score=-29.084 total time=   6.4s\n",
+      "[CV 1/5] END model2__learning_rate=0.075, model2__loss=linear, model2__n_estimators=50;, score=-25.876 total time=   2.1s\n",
+      "[CV 1/5] END model2__learning_rate=0.05, model2__loss=exponential, model2__n_estimators=200;, score=-27.229 total time=   8.2s\n",
+      "[CV 2/5] END model2__learning_rate=0.075, model2__loss=linear, model2__n_estimators=50;, score=-27.411 total time=   1.9s\n",
+      "[CV 3/5] END model2__learning_rate=0.075, model2__loss=linear, model2__n_estimators=50;, score=-27.004 total time=   2.1s\n",
+      "[CV 2/5] END model2__learning_rate=0.05, model2__loss=exponential, model2__n_estimators=200;, score=-28.374 total time=   8.3s\n",
+      "[CV 3/5] END model2__learning_rate=0.05, model2__loss=exponential, model2__n_estimators=200;, score=-29.248 total time=   8.3s\n",
+      "[CV 4/5] END model2__learning_rate=0.075, model2__loss=linear, model2__n_estimators=50;, score=-25.397 total time=   2.3s\n",
+      "[CV 5/5] END model2__learning_rate=0.075, model2__loss=linear, model2__n_estimators=50;, score=-26.989 total time=   2.3s\n",
+      "[CV 4/5] END model2__learning_rate=0.05, model2__loss=exponential, model2__n_estimators=200;, score=-26.862 total time=   8.2s\n",
+      "[CV 1/5] END model2__learning_rate=0.075, model2__loss=linear, model2__n_estimators=100;, score=-26.618 total time=   4.0s\n",
+      "[CV 2/5] END model2__learning_rate=0.075, model2__loss=linear, model2__n_estimators=100;, score=-28.514 total time=   4.1s\n",
+      "[CV 5/5] END model2__learning_rate=0.05, model2__loss=exponential, model2__n_estimators=200;, score=-29.431 total time=   8.1s\n",
+      "[CV 3/5] END model2__learning_rate=0.075, model2__loss=linear, model2__n_estimators=100;, score=-29.250 total time=   4.1s\n",
+      "[CV 4/5] END model2__learning_rate=0.075, model2__loss=linear, model2__n_estimators=100;, score=-26.471 total time=   4.3s\n",
+      "[CV 5/5] END model2__learning_rate=0.075, model2__loss=linear, model2__n_estimators=100;, score=-28.832 total time=   4.1s\n",
+      "[CV 1/5] END model2__learning_rate=0.075, model2__loss=linear, model2__n_estimators=150;, score=-26.906 total time=   5.8s\n",
+      "[CV 2/5] END model2__learning_rate=0.075, model2__loss=linear, model2__n_estimators=150;, score=-29.573 total time=   6.0s\n",
+      "[CV 3/5] END model2__learning_rate=0.075, model2__loss=linear, model2__n_estimators=150;, score=-29.823 total time=   6.2s\n",
+      "[CV 4/5] END model2__learning_rate=0.075, model2__loss=linear, model2__n_estimators=150;, score=-26.744 total time=   6.2s\n",
+      "[CV 1/5] END model2__learning_rate=0.075, model2__loss=linear, model2__n_estimators=200;, score=-27.169 total time=   5.8s\n",
+      "[CV 5/5] END model2__learning_rate=0.075, model2__loss=linear, model2__n_estimators=150;, score=-29.435 total time=   6.5s\n",
+      "[CV 1/5] END model2__learning_rate=0.075, model2__loss=square, model2__n_estimators=50;, score=-26.897 total time=   2.4s\n",
+      "[CV 2/5] END model2__learning_rate=0.075, model2__loss=square, model2__n_estimators=50;, score=-26.937 total time=   2.3s\n",
+      "[CV 2/5] END model2__learning_rate=0.075, model2__loss=linear, model2__n_estimators=200;, score=-29.870 total time=   7.4s\n",
+      "[CV 3/5] END model2__learning_rate=0.075, model2__loss=square, model2__n_estimators=50;, score=-28.102 total time=   2.5s\n",
+      "[CV 4/5] END model2__learning_rate=0.075, model2__loss=square, model2__n_estimators=50;, score=-25.168 total time=   2.6s\n",
+      "[CV 4/5] END model2__learning_rate=0.075, model2__loss=linear, model2__n_estimators=200;, score=-26.520 total time=   6.0s\n",
+      "[CV 3/5] END model2__learning_rate=0.075, model2__loss=linear, model2__n_estimators=200;, score=-30.599 total time=   8.4s\n",
+      "[CV 5/5] END model2__learning_rate=0.075, model2__loss=square, model2__n_estimators=50;, score=-29.373 total time=   2.4s\n",
+      "[CV 1/5] END model2__learning_rate=0.075, model2__loss=square, model2__n_estimators=100;, score=-27.103 total time=   4.1s\n",
+      "[CV 2/5] END model2__learning_rate=0.075, model2__loss=square, model2__n_estimators=100;, score=-28.695 total time=   4.1s\n",
+      "[CV 5/5] END model2__learning_rate=0.075, model2__loss=linear, model2__n_estimators=200;, score=-29.580 total time=   7.7s\n",
+      "[CV 3/5] END model2__learning_rate=0.075, model2__loss=square, model2__n_estimators=100;, score=-29.037 total time=   4.0s\n",
+      "[CV 4/5] END model2__learning_rate=0.075, model2__loss=square, model2__n_estimators=100;, score=-27.283 total time=   4.1s\n",
+      "[CV 5/5] END model2__learning_rate=0.075, model2__loss=square, model2__n_estimators=100;, score=-29.050 total time=   4.2s\n",
+      "[CV 1/5] END model2__learning_rate=0.075, model2__loss=square, model2__n_estimators=150;, score=-27.005 total time=   5.9s\n",
+      "[CV 2/5] END model2__learning_rate=0.075, model2__loss=square, model2__n_estimators=150;, score=-28.830 total time=   6.1s\n",
+      "[CV 3/5] END model2__learning_rate=0.075, model2__loss=square, model2__n_estimators=150;, score=-29.510 total time=   5.9s\n",
+      "[CV 4/5] END model2__learning_rate=0.075, model2__loss=square, model2__n_estimators=150;, score=-27.172 total time=   6.3s\n",
+      "[CV 5/5] END model2__learning_rate=0.075, model2__loss=square, model2__n_estimators=150;, score=-29.082 total time=   6.6s\n",
+      "[CV 1/5] END model2__learning_rate=0.075, model2__loss=exponential, model2__n_estimators=50;, score=-25.923 total time=   2.5s\n",
+      "[CV 2/5] END model2__learning_rate=0.075, model2__loss=square, model2__n_estimators=200;, score=-29.138 total time=   8.1s\n",
+      "[CV 2/5] END model2__learning_rate=0.075, model2__loss=exponential, model2__n_estimators=50;, score=-27.028 total time=   2.4s\n",
+      "[CV 3/5] END model2__learning_rate=0.075, model2__loss=exponential, model2__n_estimators=50;, score=-26.606 total time=   2.4s\n",
+      "[CV 1/5] END model2__learning_rate=0.075, model2__loss=square, model2__n_estimators=200;, score=-27.365 total time=   9.2s\n",
+      "[CV 3/5] END model2__learning_rate=0.075, model2__loss=square, model2__n_estimators=200;, score=-30.241 total time=   9.1s\n",
+      "[CV 4/5] END model2__learning_rate=0.075, model2__loss=exponential, model2__n_estimators=50;, score=-24.863 total time=   2.9s\n",
+      "[CV 5/5] END model2__learning_rate=0.075, model2__loss=exponential, model2__n_estimators=50;, score=-27.079 total time=   3.1s\n",
+      "[CV 4/5] END model2__learning_rate=0.075, model2__loss=square, model2__n_estimators=200;, score=-26.523 total time=   9.9s\n",
+      "[CV 5/5] END model2__learning_rate=0.075, model2__loss=square, model2__n_estimators=200;, score=-29.318 total time=   9.9s\n",
+      "[CV 1/5] END model2__learning_rate=0.075, model2__loss=exponential, model2__n_estimators=100;, score=-25.943 total time=   5.9s\n",
+      "[CV 2/5] END model2__learning_rate=0.075, model2__loss=exponential, model2__n_estimators=100;, score=-27.944 total time=   5.7s\n",
+      "[CV 3/5] END model2__learning_rate=0.075, model2__loss=exponential, model2__n_estimators=100;, score=-27.708 total time=   5.6s\n",
+      "[CV 4/5] END model2__learning_rate=0.075, model2__loss=exponential, model2__n_estimators=100;, score=-26.430 total time=   6.0s\n",
+      "[CV 5/5] END model2__learning_rate=0.075, model2__loss=exponential, model2__n_estimators=100;, score=-28.646 total time=   6.1s\n",
+      "[CV 1/5] END model2__learning_rate=0.075, model2__loss=exponential, model2__n_estimators=150;, score=-27.389 total time=   7.7s\n",
+      "[CV 2/5] END model2__learning_rate=0.075, model2__loss=exponential, model2__n_estimators=150;, score=-29.762 total time=   6.8s\n",
+      "[CV 3/5] END model2__learning_rate=0.075, model2__loss=exponential, model2__n_estimators=150;, score=-29.792 total time=   6.7s\n",
+      "[CV 4/5] END model2__learning_rate=0.075, model2__loss=exponential, model2__n_estimators=150;, score=-27.048 total time=   6.6s\n",
+      "[CV 5/5] END model2__learning_rate=0.075, model2__loss=exponential, model2__n_estimators=150;, score=-29.571 total time=   7.1s\n",
+      "[CV 1/5] END model2__learning_rate=0.1, model2__loss=linear, model2__n_estimators=50;, score=-26.658 total time=   2.0s\n",
+      "[CV 2/5] END model2__learning_rate=0.1, model2__loss=linear, model2__n_estimators=50;, score=-27.466 total time=   1.9s\n",
+      "[CV 1/5] END model2__learning_rate=0.075, model2__loss=exponential, model2__n_estimators=200;, score=-27.924 total time=   8.4s\n",
+      "[CV 2/5] END model2__learning_rate=0.075, model2__loss=exponential, model2__n_estimators=200;, score=-30.876 total time=   7.5s\n",
+      "[CV 3/5] END model2__learning_rate=0.1, model2__loss=linear, model2__n_estimators=50;, score=-27.306 total time=   2.0s\n",
+      "[CV 4/5] END model2__learning_rate=0.1, model2__loss=linear, model2__n_estimators=50;, score=-25.437 total time=   2.1s\n",
+      "[CV 3/5] END model2__learning_rate=0.075, model2__loss=exponential, model2__n_estimators=200;, score=-31.075 total time=   7.9s\n",
+      "[CV 5/5] END model2__learning_rate=0.1, model2__loss=linear, model2__n_estimators=50;, score=-28.778 total time=   2.2s\n",
+      "[CV 4/5] END model2__learning_rate=0.075, model2__loss=exponential, model2__n_estimators=200;, score=-27.601 total time=   8.3s\n",
+      "[CV 2/5] END model2__learning_rate=0.1, model2__loss=linear, model2__n_estimators=100;, score=-29.409 total time=   4.2s\n",
+      "[CV 1/5] END model2__learning_rate=0.1, model2__loss=linear, model2__n_estimators=100;, score=-26.418 total time=   4.6s\n",
+      "[CV 5/5] END model2__learning_rate=0.075, model2__loss=exponential, model2__n_estimators=200;, score=-29.966 total time=   8.3s\n",
+      "[CV 3/5] END model2__learning_rate=0.1, model2__loss=linear, model2__n_estimators=100;, score=-29.512 total time=   4.4s\n",
+      "[CV 4/5] END model2__learning_rate=0.1, model2__loss=linear, model2__n_estimators=100;, score=-26.786 total time=   4.6s\n",
+      "[CV 5/5] END model2__learning_rate=0.1, model2__loss=linear, model2__n_estimators=100;, score=-29.130 total time=   4.6s\n",
+      "[CV 1/5] END model2__learning_rate=0.1, model2__loss=linear, model2__n_estimators=150;, score=-27.319 total time=   4.5s\n",
+      "[CV 4/5] END model2__learning_rate=0.1, model2__loss=linear, model2__n_estimators=150;, score=-26.593 total time=   4.7s\n",
+      "[CV 1/5] END model2__learning_rate=0.1, model2__loss=linear, model2__n_estimators=200;, score=-26.848 total time=   4.5s\n",
+      "[CV 2/5] END model2__learning_rate=0.1, model2__loss=linear, model2__n_estimators=150;, score=-29.988 total time=   5.3s\n",
+      "[CV 5/5] END model2__learning_rate=0.1, model2__loss=linear, model2__n_estimators=150;, score=-29.264 total time=   5.0s\n",
+      "[CV 3/5] END model2__learning_rate=0.1, model2__loss=linear, model2__n_estimators=150;, score=-30.200 total time=   6.3s\n",
+      "[CV 2/5] END model2__learning_rate=0.1, model2__loss=linear, model2__n_estimators=200;, score=-29.540 total time=   5.0s\n",
+      "[CV 4/5] END model2__learning_rate=0.1, model2__loss=linear, model2__n_estimators=200;, score=-26.805 total time=   4.9s\n",
+      "[CV 1/5] END model2__learning_rate=0.1, model2__loss=square, model2__n_estimators=50;, score=-26.922 total time=   2.3s\n",
+      "[CV 2/5] END model2__learning_rate=0.1, model2__loss=square, model2__n_estimators=50;, score=-28.483 total time=   2.4s\n",
+      "[CV 3/5] END model2__learning_rate=0.1, model2__loss=square, model2__n_estimators=50;, score=-28.846 total time=   2.4s\n",
+      "[CV 3/5] END model2__learning_rate=0.1, model2__loss=linear, model2__n_estimators=200;, score=-30.674 total time=   6.6s\n",
+      "[CV 4/5] END model2__learning_rate=0.1, model2__loss=square, model2__n_estimators=50;, score=-27.086 total time=   2.5s\n",
+      "[CV 5/5] END model2__learning_rate=0.1, model2__loss=square, model2__n_estimators=50;, score=-29.395 total time=   2.4s\n",
+      "[CV 5/5] END model2__learning_rate=0.1, model2__loss=linear, model2__n_estimators=200;, score=-29.650 total time=   5.6s\n",
+      "[CV 1/5] END model2__learning_rate=0.1, model2__loss=square, model2__n_estimators=100;, score=-27.133 total time=   4.6s\n",
+      "[CV 2/5] END model2__learning_rate=0.1, model2__loss=square, model2__n_estimators=100;, score=-28.815 total time=   4.8s\n",
+      "[CV 3/5] END model2__learning_rate=0.1, model2__loss=square, model2__n_estimators=100;, score=-28.877 total time=   4.9s\n",
+      "[CV 4/5] END model2__learning_rate=0.1, model2__loss=square, model2__n_estimators=100;, score=-27.097 total time=   5.0s\n",
+      "[CV 5/5] END model2__learning_rate=0.1, model2__loss=square, model2__n_estimators=100;, score=-28.623 total time=   5.0s\n",
+      "[CV 2/5] END model2__learning_rate=0.1, model2__loss=square, model2__n_estimators=150;, score=-29.527 total time=   6.5s\n",
+      "[CV 1/5] END model2__learning_rate=0.1, model2__loss=square, model2__n_estimators=150;, score=-27.396 total time=   6.5s\n",
+      "[CV 1/5] END model2__learning_rate=0.1, model2__loss=square, model2__n_estimators=200;, score=-27.143 total time=   4.5s\n",
+      "[CV 3/5] END model2__learning_rate=0.1, model2__loss=square, model2__n_estimators=150;, score=-30.140 total time=   6.9s\n",
+      "[CV 4/5] END model2__learning_rate=0.1, model2__loss=square, model2__n_estimators=150;, score=-26.424 total time=   6.4s\n",
+      "[CV 5/5] END model2__learning_rate=0.1, model2__loss=square, model2__n_estimators=150;, score=-29.732 total time=   6.7s\n",
+      "[CV 1/5] END model2__learning_rate=0.1, model2__loss=exponential, model2__n_estimators=50;, score=-25.902 total time=   2.2s\n",
+      "[CV 2/5] END model2__learning_rate=0.1, model2__loss=exponential, model2__n_estimators=50;, score=-27.178 total time=   2.2s\n",
+      "[CV 3/5] END model2__learning_rate=0.1, model2__loss=exponential, model2__n_estimators=50;, score=-26.940 total time=   2.2s\n",
+      "[CV 2/5] END model2__learning_rate=0.1, model2__loss=square, model2__n_estimators=200;, score=-30.017 total time=   8.1s\n",
+      "[CV 4/5] END model2__learning_rate=0.1, model2__loss=exponential, model2__n_estimators=50;, score=-25.352 total time=   2.5s\n",
+      "[CV 5/5] END model2__learning_rate=0.1, model2__loss=exponential, model2__n_estimators=50;, score=-27.355 total time=   2.4s\n",
+      "[CV 3/5] END model2__learning_rate=0.1, model2__loss=square, model2__n_estimators=200;, score=-30.277 total time=   8.7s\n",
+      "[CV 1/5] END model2__learning_rate=0.1, model2__loss=exponential, model2__n_estimators=100;, score=-27.081 total time=   4.6s\n",
+      "[CV 4/5] END model2__learning_rate=0.1, model2__loss=square, model2__n_estimators=200;, score=-26.679 total time=   8.9s\n",
+      "[CV 5/5] END model2__learning_rate=0.1, model2__loss=square, model2__n_estimators=200;, score=-31.132 total time=   9.0s\n",
+      "[CV 2/5] END model2__learning_rate=0.1, model2__loss=exponential, model2__n_estimators=100;, score=-29.336 total time=   4.8s\n",
+      "[CV 3/5] END model2__learning_rate=0.1, model2__loss=exponential, model2__n_estimators=100;, score=-29.367 total time=   5.0s\n",
+      "[CV 4/5] END model2__learning_rate=0.1, model2__loss=exponential, model2__n_estimators=100;, score=-26.544 total time=   4.9s\n",
+      "[CV 5/5] END model2__learning_rate=0.1, model2__loss=exponential, model2__n_estimators=100;, score=-29.363 total time=   5.0s\n",
+      "[CV 1/5] END model2__learning_rate=0.1, model2__loss=exponential, model2__n_estimators=150;, score=-28.340 total time=   7.0s\n",
+      "[CV 2/5] END model2__learning_rate=0.1, model2__loss=exponential, model2__n_estimators=150;, score=-30.323 total time=   6.5s\n",
+      "[CV 4/5] END model2__learning_rate=0.1, model2__loss=exponential, model2__n_estimators=150;, score=-27.793 total time=   6.4s\n",
+      "[CV 3/5] END model2__learning_rate=0.1, model2__loss=exponential, model2__n_estimators=150;, score=-30.548 total time=   6.9s\n",
+      "[CV 5/5] END model2__learning_rate=0.1, model2__loss=exponential, model2__n_estimators=150;, score=-30.186 total time=   7.0s\n",
+      "[CV 1/5] END model2__learning_rate=0.15, model2__loss=linear, model2__n_estimators=50;, score=-26.800 total time=   2.4s\n",
+      "[CV 2/5] END model2__learning_rate=0.15, model2__loss=linear, model2__n_estimators=50;, score=-28.015 total time=   2.1s\n",
+      "[CV 3/5] END model2__learning_rate=0.15, model2__loss=linear, model2__n_estimators=50;, score=-29.107 total time=   2.2s\n",
+      "[CV 1/5] END model2__learning_rate=0.1, model2__loss=exponential, model2__n_estimators=200;, score=-29.185 total time=   8.7s\n",
+      "[CV 2/5] END model2__learning_rate=0.1, model2__loss=exponential, model2__n_estimators=200;, score=-31.752 total time=   8.5s\n",
+      "[CV 3/5] END model2__learning_rate=0.1, model2__loss=exponential, model2__n_estimators=200;, score=-32.065 total time=   8.5s\n",
+      "[CV 4/5] END model2__learning_rate=0.15, model2__loss=linear, model2__n_estimators=50;, score=-26.944 total time=   2.1s\n",
+      "[CV 5/5] END model2__learning_rate=0.15, model2__loss=linear, model2__n_estimators=50;, score=-29.014 total time=   2.1s\n",
+      "[CV 1/5] END model2__learning_rate=0.15, model2__loss=linear, model2__n_estimators=100;, score=-26.938 total time=   3.2s\n",
+      "[CV 4/5] END model2__learning_rate=0.1, model2__loss=exponential, model2__n_estimators=200;, score=-28.113 total time=   8.4s\n",
+      "[CV 2/5] END model2__learning_rate=0.15, model2__loss=linear, model2__n_estimators=100;, score=-29.185 total time=   3.3s\n",
+      "[CV 4/5] END model2__learning_rate=0.15, model2__loss=linear, model2__n_estimators=100;, score=-26.793 total time=   3.0s\n",
+      "[CV 1/5] END model2__learning_rate=0.15, model2__loss=linear, model2__n_estimators=150;, score=-27.100 total time=   2.9s\n",
+      "[CV 3/5] END model2__learning_rate=0.15, model2__loss=linear, model2__n_estimators=100;, score=-30.317 total time=   4.0s\n",
+      "[CV 5/5] END model2__learning_rate=0.15, model2__loss=linear, model2__n_estimators=100;, score=-29.459 total time=   3.4s\n",
+      "[CV 5/5] END model2__learning_rate=0.1, model2__loss=exponential, model2__n_estimators=200;, score=-30.882 total time=   8.3s\n",
+      "[CV 2/5] END model2__learning_rate=0.15, model2__loss=linear, model2__n_estimators=150;, score=-29.733 total time=   3.2s\n",
+      "[CV 4/5] END model2__learning_rate=0.15, model2__loss=linear, model2__n_estimators=150;, score=-26.855 total time=   3.0s\n",
+      "[CV 3/5] END model2__learning_rate=0.15, model2__loss=linear, model2__n_estimators=150;, score=-30.200 total time=   3.8s\n",
+      "[CV 1/5] END model2__learning_rate=0.15, model2__loss=linear, model2__n_estimators=200;, score=-27.234 total time=   2.9s\n",
+      "[CV 5/5] END model2__learning_rate=0.15, model2__loss=linear, model2__n_estimators=150;, score=-29.424 total time=   3.5s\n",
+      "[CV 4/5] END model2__learning_rate=0.15, model2__loss=linear, model2__n_estimators=200;, score=-26.725 total time=   3.4s\n",
+      "[CV 3/5] END model2__learning_rate=0.15, model2__loss=linear, model2__n_estimators=200;, score=-29.931 total time=   4.0s\n",
+      "[CV 1/5] END model2__learning_rate=0.15, model2__loss=square, model2__n_estimators=50;, score=-26.711 total time=   2.2s\n",
+      "[CV 2/5] END model2__learning_rate=0.15, model2__loss=square, model2__n_estimators=50;, score=-28.741 total time=   1.9s\n",
+      "[CV 2/5] END model2__learning_rate=0.15, model2__loss=linear, model2__n_estimators=200;, score=-30.120 total time=   4.4s\n",
+      "[CV 3/5] END model2__learning_rate=0.15, model2__loss=square, model2__n_estimators=50;, score=-29.135 total time=   2.1s\n",
+      "[CV 4/5] END model2__learning_rate=0.15, model2__loss=square, model2__n_estimators=50;, score=-27.871 total time=   2.1s\n",
+      "[CV 5/5] END model2__learning_rate=0.15, model2__loss=linear, model2__n_estimators=200;, score=-29.226 total time=   3.4s\n",
+      "[CV 5/5] END model2__learning_rate=0.15, model2__loss=square, model2__n_estimators=50;, score=-28.892 total time=   2.1s\n",
+      "[CV 2/5] END model2__learning_rate=0.15, model2__loss=square, model2__n_estimators=100;, score=-28.994 total time=   3.9s\n",
+      "[CV 1/5] END model2__learning_rate=0.15, model2__loss=square, model2__n_estimators=100;, score=-27.825 total time=   4.1s\n",
+      "[CV 3/5] END model2__learning_rate=0.15, model2__loss=square, model2__n_estimators=100;, score=-29.971 total time=   4.0s\n",
+      "[CV 5/5] END model2__learning_rate=0.15, model2__loss=square, model2__n_estimators=100;, score=-29.868 total time=   4.1s\n",
+      "[CV 4/5] END model2__learning_rate=0.15, model2__loss=square, model2__n_estimators=100;, score=-27.004 total time=   4.3s\n",
+      "[CV 1/5] END model2__learning_rate=0.15, model2__loss=square, model2__n_estimators=150;, score=-28.126 total time=   5.1s\n",
+      "[CV 2/5] END model2__learning_rate=0.15, model2__loss=square, model2__n_estimators=150;, score=-31.275 total time=   5.4s\n",
+      "[CV 3/5] END model2__learning_rate=0.15, model2__loss=square, model2__n_estimators=150;, score=-30.378 total time=   5.8s\n",
+      "[CV 1/5] END model2__learning_rate=0.15, model2__loss=square, model2__n_estimators=200;, score=-27.669 total time=   3.3s\n",
+      "[CV 2/5] END model2__learning_rate=0.15, model2__loss=exponential, model2__n_estimators=50;, score=-28.071 total time=   1.9s\n",
+      "[CV 1/5] END model2__learning_rate=0.15, model2__loss=exponential, model2__n_estimators=50;, score=-26.720 total time=   2.1s\n",
+      "[CV 4/5] END model2__learning_rate=0.15, model2__loss=square, model2__n_estimators=150;, score=-26.776 total time=   5.6s\n",
+      "[CV 5/5] END model2__learning_rate=0.15, model2__loss=square, model2__n_estimators=150;, score=-31.601 total time=   5.6s\n",
+      "[CV 3/5] END model2__learning_rate=0.15, model2__loss=exponential, model2__n_estimators=50;, score=-27.730 total time=   2.1s\n",
+      "[CV 2/5] END model2__learning_rate=0.15, model2__loss=square, model2__n_estimators=200;, score=-35.911 total time=   7.0s\n",
+      "[CV 4/5] END model2__learning_rate=0.15, model2__loss=exponential, model2__n_estimators=50;, score=-25.381 total time=   2.2s\n",
+      "[CV 4/5] END model2__learning_rate=0.15, model2__loss=square, model2__n_estimators=200;, score=-26.689 total time=   5.8s\n",
+      "[CV 5/5] END model2__learning_rate=0.15, model2__loss=exponential, model2__n_estimators=50;, score=-28.791 total time=   2.2s\n",
+      "[CV 3/5] END model2__learning_rate=0.15, model2__loss=square, model2__n_estimators=200;, score=-30.667 total time=   7.3s\n",
+      "[CV 5/5] END model2__learning_rate=0.15, model2__loss=square, model2__n_estimators=200;, score=-34.106 total time=   7.2s\n",
+      "[CV 1/5] END model2__learning_rate=0.15, model2__loss=exponential, model2__n_estimators=100;, score=-28.081 total time=   4.3s\n",
+      "[CV 2/5] END model2__learning_rate=0.15, model2__loss=exponential, model2__n_estimators=100;, score=-30.827 total time=   4.1s\n",
+      "[CV 3/5] END model2__learning_rate=0.15, model2__loss=exponential, model2__n_estimators=100;, score=-30.774 total time=   4.4s\n",
+      "[CV 4/5] END model2__learning_rate=0.15, model2__loss=exponential, model2__n_estimators=100;, score=-27.432 total time=   4.5s\n",
+      "[CV 5/5] END model2__learning_rate=0.15, model2__loss=exponential, model2__n_estimators=100;, score=-29.845 total time=   4.5s\n",
+      "[CV 2/5] END model2__learning_rate=0.15, model2__loss=exponential, model2__n_estimators=150;, score=-33.362 total time=   6.1s\n",
+      "[CV 1/5] END model2__learning_rate=0.15, model2__loss=exponential, model2__n_estimators=150;, score=-29.644 total time=   6.4s\n",
+      "[CV 3/5] END model2__learning_rate=0.15, model2__loss=exponential, model2__n_estimators=150;, score=-32.834 total time=   6.1s\n",
+      "[CV 4/5] END model2__learning_rate=0.15, model2__loss=exponential, model2__n_estimators=150;, score=-29.157 total time=   6.4s\n",
+      "[CV 5/5] END model2__learning_rate=0.15, model2__loss=exponential, model2__n_estimators=150;, score=-31.698 total time=   6.0s\n",
+      "[CV 1/5] END model2__learning_rate=0.2, model2__loss=linear, model2__n_estimators=50;, score=-27.303 total time=   2.0s\n",
+      "[CV 2/5] END model2__learning_rate=0.2, model2__loss=linear, model2__n_estimators=50;, score=-29.153 total time=   2.0s\n",
+      "[CV 2/5] END model2__learning_rate=0.15, model2__loss=exponential, model2__n_estimators=200;, score=-38.467 total time=   7.6s\n",
+      "[CV 3/5] END model2__learning_rate=0.2, model2__loss=linear, model2__n_estimators=50;, score=-29.147 total time=   2.1s\n",
+      "[CV 1/5] END model2__learning_rate=0.15, model2__loss=exponential, model2__n_estimators=200;, score=-31.899 total time=   7.9s\n",
+      "[CV 4/5] END model2__learning_rate=0.2, model2__loss=linear, model2__n_estimators=50;, score=-26.414 total time=   2.2s\n",
+      "[CV 3/5] END model2__learning_rate=0.15, model2__loss=exponential, model2__n_estimators=200;, score=-37.271 total time=   7.8s\n",
+      "[CV 5/5] END model2__learning_rate=0.2, model2__loss=linear, model2__n_estimators=50;, score=-29.346 total time=   2.1s\n",
+      "[CV 5/5] END model2__learning_rate=0.15, model2__loss=exponential, model2__n_estimators=200;, score=-35.352 total time=   7.6s\n",
+      "[CV 1/5] END model2__learning_rate=0.2, model2__loss=linear, model2__n_estimators=100;, score=-27.731 total time=   2.2s\n",
+      "[CV 4/5] END model2__learning_rate=0.15, model2__loss=exponential, model2__n_estimators=200;, score=-31.143 total time=   7.9s\n",
+      "[CV 4/5] END model2__learning_rate=0.2, model2__loss=linear, model2__n_estimators=100;, score=-26.814 total time=   2.2s\n",
+      "[CV 2/5] END model2__learning_rate=0.2, model2__loss=linear, model2__n_estimators=100;, score=-29.735 total time=   2.6s\n",
+      "[CV 5/5] END model2__learning_rate=0.2, model2__loss=linear, model2__n_estimators=100;, score=-29.852 total time=   2.4s\n",
+      "[CV 1/5] END model2__learning_rate=0.2, model2__loss=linear, model2__n_estimators=150;, score=-27.222 total time=   2.0s\n",
+      "[CV 3/5] END model2__learning_rate=0.2, model2__loss=linear, model2__n_estimators=100;, score=-29.899 total time=   3.1s\n",
+      "[CV 4/5] END model2__learning_rate=0.2, model2__loss=linear, model2__n_estimators=150;, score=-27.079 total time=   2.0s\n",
+      "[CV 2/5] END model2__learning_rate=0.2, model2__loss=linear, model2__n_estimators=150;, score=-29.628 total time=   2.5s\n",
+      "[CV 5/5] END model2__learning_rate=0.2, model2__loss=linear, model2__n_estimators=150;, score=-29.230 total time=   2.1s\n",
+      "[CV 3/5] END model2__learning_rate=0.2, model2__loss=linear, model2__n_estimators=150;, score=-30.047 total time=   2.5s\n",
+      "[CV 1/5] END model2__learning_rate=0.2, model2__loss=linear, model2__n_estimators=200;, score=-27.010 total time=   2.3s\n",
+      "[CV 4/5] END model2__learning_rate=0.2, model2__loss=linear, model2__n_estimators=200;, score=-26.844 total time=   2.3s\n",
+      "[CV 3/5] END model2__learning_rate=0.2, model2__loss=linear, model2__n_estimators=200;, score=-30.440 total time=   2.9s\n",
+      "[CV 2/5] END model2__learning_rate=0.2, model2__loss=linear, model2__n_estimators=200;, score=-29.900 total time=   3.3s\n",
+      "[CV 2/5] END model2__learning_rate=0.2, model2__loss=square, model2__n_estimators=50;, score=-28.398 total time=   2.0s\n",
+      "[CV 3/5] END model2__learning_rate=0.2, model2__loss=square, model2__n_estimators=50;, score=-30.276 total time=   2.0s\n",
+      "[CV 1/5] END model2__learning_rate=0.2, model2__loss=square, model2__n_estimators=50;, score=-26.829 total time=   2.2s\n",
+      "[CV 5/5] END model2__learning_rate=0.2, model2__loss=linear, model2__n_estimators=200;, score=-29.852 total time=   2.8s\n",
+      "[CV 4/5] END model2__learning_rate=0.2, model2__loss=square, model2__n_estimators=50;, score=-26.690 total time=   2.1s\n",
+      "[CV 5/5] END model2__learning_rate=0.2, model2__loss=square, model2__n_estimators=50;, score=-28.866 total time=   2.1s\n",
+      "[CV 1/5] END model2__learning_rate=0.2, model2__loss=square, model2__n_estimators=100;, score=-26.860 total time=   2.2s\n",
+      "[CV 2/5] END model2__learning_rate=0.2, model2__loss=square, model2__n_estimators=100;, score=-30.215 total time=   3.7s\n",
+      "[CV 3/5] END model2__learning_rate=0.2, model2__loss=square, model2__n_estimators=100;, score=-30.178 total time=   4.3s\n",
+      "[CV 4/5] END model2__learning_rate=0.2, model2__loss=square, model2__n_estimators=100;, score=-26.595 total time=   4.3s\n",
+      "[CV 5/5] END model2__learning_rate=0.2, model2__loss=square, model2__n_estimators=100;, score=-30.584 total time=   4.2s\n",
+      "[CV 1/5] END model2__learning_rate=0.2, model2__loss=square, model2__n_estimators=150;, score=-28.027 total time=   4.4s\n",
+      "[CV 2/5] END model2__learning_rate=0.2, model2__loss=square, model2__n_estimators=150;, score=-33.914 total time=   5.9s\n",
+      "[CV 3/5] END model2__learning_rate=0.2, model2__loss=square, model2__n_estimators=150;, score=-30.750 total time=   6.2s\n",
+      "[CV 4/5] END model2__learning_rate=0.2, model2__loss=square, model2__n_estimators=150;, score=-27.549 total time=   6.3s\n",
+      "[CV 1/5] END model2__learning_rate=0.2, model2__loss=square, model2__n_estimators=200;, score=-28.548 total time=   4.3s\n",
+      "[CV 5/5] END model2__learning_rate=0.2, model2__loss=square, model2__n_estimators=150;, score=-33.242 total time=   5.9s\n",
+      "[CV 1/5] END model2__learning_rate=0.2, model2__loss=exponential, model2__n_estimators=50;, score=-27.374 total time=   2.1s\n",
+      "[CV 2/5] END model2__learning_rate=0.2, model2__loss=exponential, model2__n_estimators=50;, score=-29.672 total time=   2.2s\n",
+      "[CV 4/5] END model2__learning_rate=0.2, model2__loss=square, model2__n_estimators=200;, score=-27.443 total time=   5.3s\n",
+      "[CV 3/5] END model2__learning_rate=0.2, model2__loss=exponential, model2__n_estimators=50;, score=-29.363 total time=   2.2s\n",
+      "[CV 2/5] END model2__learning_rate=0.2, model2__loss=square, model2__n_estimators=200;, score=-46.436 total time=   6.8s\n",
+      "[CV 4/5] END model2__learning_rate=0.2, model2__loss=exponential, model2__n_estimators=50;, score=-26.355 total time=   2.0s\n",
+      "[CV 5/5] END model2__learning_rate=0.2, model2__loss=exponential, model2__n_estimators=50;, score=-29.277 total time=   2.2s\n",
+      "[CV 3/5] END model2__learning_rate=0.2, model2__loss=square, model2__n_estimators=200;, score=-31.668 total time=   7.7s\n",
+      "[CV 5/5] END model2__learning_rate=0.2, model2__loss=square, model2__n_estimators=200;, score=-42.953 total time=   7.4s\n",
+      "[CV 1/5] END model2__learning_rate=0.2, model2__loss=exponential, model2__n_estimators=100;, score=-29.277 total time=   4.2s\n",
+      "[CV 2/5] END model2__learning_rate=0.2, model2__loss=exponential, model2__n_estimators=100;, score=-31.727 total time=   4.1s\n",
+      "[CV 3/5] END model2__learning_rate=0.2, model2__loss=exponential, model2__n_estimators=100;, score=-32.275 total time=   4.1s\n",
+      "[CV 5/5] END model2__learning_rate=0.2, model2__loss=exponential, model2__n_estimators=100;, score=-31.129 total time=   4.0s\n",
+      "[CV 4/5] END model2__learning_rate=0.2, model2__loss=exponential, model2__n_estimators=100;, score=-28.799 total time=   4.4s\n",
+      "[CV 1/5] END model2__learning_rate=0.2, model2__loss=exponential, model2__n_estimators=150;, score=-31.904 total time=   5.8s\n",
+      "[CV 2/5] END model2__learning_rate=0.2, model2__loss=exponential, model2__n_estimators=150;, score=-37.566 total time=   5.4s\n",
+      "[CV 3/5] END model2__learning_rate=0.2, model2__loss=exponential, model2__n_estimators=150;, score=-36.363 total time=   5.5s\n",
+      "[CV 5/5] END model2__learning_rate=0.2, model2__loss=exponential, model2__n_estimators=150;, score=-33.962 total time=   5.4s\n",
+      "[CV 4/5] END model2__learning_rate=0.2, model2__loss=exponential, model2__n_estimators=150;, score=-31.493 total time=   5.7s\n",
+      "[CV 2/5] END model2__learning_rate=0.2, model2__loss=exponential, model2__n_estimators=200;, score=-52.550 total time=   6.0s\n",
+      "[CV 1/5] END model2__learning_rate=0.2, model2__loss=exponential, model2__n_estimators=200;, score=-34.798 total time=   6.3s\n",
+      "[CV 3/5] END model2__learning_rate=0.2, model2__loss=exponential, model2__n_estimators=200;, score=-39.547 total time=   6.0s\n",
+      "[CV 4/5] END model2__learning_rate=0.2, model2__loss=exponential, model2__n_estimators=200;, score=-33.937 total time=   4.8s\n",
+      "[CV 5/5] END model2__learning_rate=0.2, model2__loss=exponential, model2__n_estimators=200;, score=-42.545 total time=   4.8s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-10 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-10 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-10 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-10 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-10 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-10 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-10 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-10 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-10 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-10 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-10 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-10 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-10 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
+       "             estimator=Pipeline(steps=[(&#x27;Data_processing&#x27;,\n",
+       "                                        Pipeline(steps=[(&#x27;step1&#x27;,\n",
+       "                                                         ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                                                                           transformers=[(&#x27;col_tnf&#x27;,\n",
+       "                                                                                          OneHotEncoder(drop=&#x27;first&#x27;,\n",
+       "                                                                                                        sparse_output=False),\n",
+       "                                                                                          [0,\n",
+       "                                                                                           2])])),\n",
+       "                                                        (&#x27;step2&#x27;,\n",
+       "                                                         StandardScaler())])),\n",
+       "                                       (&#x27;model2&#x27;, AdaBoostRegressor())]),\n",
+       "             n_jobs=-1,\n",
+       "             param_grid={&#x27;model2__learning_rate&#x27;: [0.01, 0.025, 0.05, 0.075,\n",
+       "                                                   0.1, 0.15, 0.2],\n",
+       "                         &#x27;model2__loss&#x27;: [&#x27;linear&#x27;, &#x27;square&#x27;, &#x27;exponential&#x27;],\n",
+       "                         &#x27;model2__n_estimators&#x27;: [50, 100, 150, 200]},\n",
+       "             scoring=&#x27;neg_mean_absolute_error&#x27;, verbose=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-80\" type=\"checkbox\" ><label for=\"sk-estimator-id-80\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5,\n",
+       "             estimator=Pipeline(steps=[(&#x27;Data_processing&#x27;,\n",
+       "                                        Pipeline(steps=[(&#x27;step1&#x27;,\n",
+       "                                                         ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                                                                           transformers=[(&#x27;col_tnf&#x27;,\n",
+       "                                                                                          OneHotEncoder(drop=&#x27;first&#x27;,\n",
+       "                                                                                                        sparse_output=False),\n",
+       "                                                                                          [0,\n",
+       "                                                                                           2])])),\n",
+       "                                                        (&#x27;step2&#x27;,\n",
+       "                                                         StandardScaler())])),\n",
+       "                                       (&#x27;model2&#x27;, AdaBoostRegressor())]),\n",
+       "             n_jobs=-1,\n",
+       "             param_grid={&#x27;model2__learning_rate&#x27;: [0.01, 0.025, 0.05, 0.075,\n",
+       "                                                   0.1, 0.15, 0.2],\n",
+       "                         &#x27;model2__loss&#x27;: [&#x27;linear&#x27;, &#x27;square&#x27;, &#x27;exponential&#x27;],\n",
+       "                         &#x27;model2__n_estimators&#x27;: [50, 100, 150, 200]},\n",
+       "             scoring=&#x27;neg_mean_absolute_error&#x27;, verbose=3)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-81\" type=\"checkbox\" ><label for=\"sk-estimator-id-81\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;Data_processing&#x27;,\n",
+       "                 Pipeline(steps=[(&#x27;step1&#x27;,\n",
+       "                                  ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                                                    transformers=[(&#x27;col_tnf&#x27;,\n",
+       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;,\n",
+       "                                                                                 sparse_output=False),\n",
+       "                                                                   [0, 2])])),\n",
+       "                                 (&#x27;step2&#x27;, StandardScaler())])),\n",
+       "                (&#x27;model2&#x27;, AdaBoostRegressor())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-82\" type=\"checkbox\" ><label for=\"sk-estimator-id-82\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;Data_processing: Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Data_processing: Pipeline</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;step1&#x27;,\n",
+       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                                   transformers=[(&#x27;col_tnf&#x27;,\n",
+       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
+       "                                                                sparse_output=False),\n",
+       "                                                  [0, 2])])),\n",
+       "                (&#x27;step2&#x27;, StandardScaler())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-83\" type=\"checkbox\" ><label for=\"sk-estimator-id-83\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;step1: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for step1: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                  transformers=[(&#x27;col_tnf&#x27;,\n",
+       "                                 OneHotEncoder(drop=&#x27;first&#x27;,\n",
+       "                                               sparse_output=False),\n",
+       "                                 [0, 2])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-84\" type=\"checkbox\" ><label for=\"sk-estimator-id-84\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">col_tnf</label><div class=\"sk-toggleable__content fitted\"><pre>[0, 2]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-85\" type=\"checkbox\" ><label for=\"sk-estimator-id-85\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, sparse_output=False)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-86\" type=\"checkbox\" ><label for=\"sk-estimator-id-86\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">remainder</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;availability&#x27;, &#x27;total_sqft&#x27;, &#x27;bath&#x27;, &#x27;bhk&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-87\" type=\"checkbox\" ><label for=\"sk-estimator-id-87\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">passthrough</label><div class=\"sk-toggleable__content fitted\"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-88\" type=\"checkbox\" ><label for=\"sk-estimator-id-88\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-89\" type=\"checkbox\" ><label for=\"sk-estimator-id-89\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;AdaBoostRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.AdaBoostRegressor.html\">?<span>Documentation for AdaBoostRegressor</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>AdaBoostRegressor()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=5,\n",
+       "             estimator=Pipeline(steps=[('Data_processing',\n",
+       "                                        Pipeline(steps=[('step1',\n",
+       "                                                         ColumnTransformer(remainder='passthrough',\n",
+       "                                                                           transformers=[('col_tnf',\n",
+       "                                                                                          OneHotEncoder(drop='first',\n",
+       "                                                                                                        sparse_output=False),\n",
+       "                                                                                          [0,\n",
+       "                                                                                           2])])),\n",
+       "                                                        ('step2',\n",
+       "                                                         StandardScaler())])),\n",
+       "                                       ('model2', AdaBoostRegressor())]),\n",
+       "             n_jobs=-1,\n",
+       "             param_grid={'model2__learning_rate': [0.01, 0.025, 0.05, 0.075,\n",
+       "                                                   0.1, 0.15, 0.2],\n",
+       "                         'model2__loss': ['linear', 'square', 'exponential'],\n",
+       "                         'model2__n_estimators': [50, 100, 150, 200]},\n",
+       "             scoring='neg_mean_absolute_error', verbose=3)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model2 = AdaBoostRegressor()\n",
+    "model2_pipe = Pipeline([('Data_processing' , data_pipeline) , ('model2' , model2)])\n",
+    "parameters = {\n",
+    "    \"model2__learning_rate\": [0.01, 0.025, 0.05, 0.075, 0.1, 0.15, 0.2],\n",
+    "    'model2__loss' : ['linear', 'square', 'exponential'],\n",
+    "    \"model2__n_estimators\":[50, 100, 150, 200]\n",
+    "}\n",
+    "\n",
+    "gscv2 = GridSearchCV(model2_pipe , parameters , scoring='neg_mean_absolute_error' , cv = 5 , n_jobs=-1 , verbose = 3)\n",
+    "gscv2.fit(x_train , y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pipeline(steps=[('Data_processing',\n",
+      "                 Pipeline(steps=[('step1',\n",
+      "                                  ColumnTransformer(remainder='passthrough',\n",
+      "                                                    transformers=[('col_tnf',\n",
+      "                                                                   OneHotEncoder(drop='first',\n",
+      "                                                                                 sparse_output=False),\n",
+      "                                                                   [0, 2])])),\n",
+      "                                 ('step2', StandardScaler())])),\n",
+      "                ('model2',\n",
+      "                 AdaBoostRegressor(learning_rate=0.025, loss='square'))])\n",
+      "--------------------\n",
+      "Best score :-25.98007300710056\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(gscv2.best_estimator_)\n",
+    "print('--'*10)\n",
+    "print(f'Best score :{gscv2.best_score_}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(12.311008461717156, 12.823689637660669)"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(gscv1.best_estimator_.predict(x_test) , y_test) , mean_absolute_error(pipeline.predict(x_test) , y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# lets save the Gradient boosting model\n",
+    "with open('GradientBoostingRegressor.pkl' , 'wb') as file:\n",
+    "    pickle.dump(gscv1.best_estimator_ , file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/Bingumalla Likith/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
+      "  warnings.warn(\n",
+      "/Users/Bingumalla Likith/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-30 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-30 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-30 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-30 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-30 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-30 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-30 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-30 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-30 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-30 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-30 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-30 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-30 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-30 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-30 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-30 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-30 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-30 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-30 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-30 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-30 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-30 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-30 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-30 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-30 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-30 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-30 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-30 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-30 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-30 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-30 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-30 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-30 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-30 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-30 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-30 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-30 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-30 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-30 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-30 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-30 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-30 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-30\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;Data_pipeline&#x27;,\n",
+       "                 Pipeline(steps=[(&#x27;step1&#x27;,\n",
+       "                                  ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                                                    transformers=[(&#x27;col_tnf&#x27;,\n",
+       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;,\n",
+       "                                                                                 sparse_output=False),\n",
+       "                                                                   [0, 2])])),\n",
+       "                                 (&#x27;step2&#x27;, StandardScaler())])),\n",
+       "                (&#x27;stacking_model&#x27;,\n",
+       "                 StackingRegressor(estimators=[(&#x27;xgbr&#x27;,\n",
+       "                                                XGBRegressor(base_score=0.5,\n",
+       "                                                             booster=&#x27;gbtree&#x27;,\n",
+       "                                                             callbacks=None,\n",
+       "                                                             colsample_bylevel=1,\n",
+       "                                                             c...\n",
+       "                                                             max_delta_step=0,\n",
+       "                                                             max_depth=6,\n",
+       "                                                             max_leaves=0,\n",
+       "                                                             min_child_weight=1,\n",
+       "                                                             missing=nan,\n",
+       "                                                             monotone_constraints=&#x27;()&#x27;,\n",
+       "                                                             n_estimators=100,\n",
+       "                                                             n_jobs=0,\n",
+       "                                                             num_parallel_tree=1,\n",
+       "                                                             predictor=&#x27;auto&#x27;,\n",
+       "                                                             random_state=0,\n",
+       "                                                             reg_alpha=0,\n",
+       "                                                             reg_lambda=1, ...)),\n",
+       "                                               (&#x27;gbr&#x27;,\n",
+       "                                                GradientBoostingRegressor(learning_rate=0.15,\n",
+       "                                                                          max_depth=8,\n",
+       "                                                                          max_features=&#x27;sqrt&#x27;,\n",
+       "                                                                          n_estimators=200,\n",
+       "                                                                          subsample=0.85))],\n",
+       "                                   final_estimator=LinearSVR()))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-318\" type=\"checkbox\" ><label for=\"sk-estimator-id-318\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;Data_pipeline&#x27;,\n",
+       "                 Pipeline(steps=[(&#x27;step1&#x27;,\n",
+       "                                  ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                                                    transformers=[(&#x27;col_tnf&#x27;,\n",
+       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;,\n",
+       "                                                                                 sparse_output=False),\n",
+       "                                                                   [0, 2])])),\n",
+       "                                 (&#x27;step2&#x27;, StandardScaler())])),\n",
+       "                (&#x27;stacking_model&#x27;,\n",
+       "                 StackingRegressor(estimators=[(&#x27;xgbr&#x27;,\n",
+       "                                                XGBRegressor(base_score=0.5,\n",
+       "                                                             booster=&#x27;gbtree&#x27;,\n",
+       "                                                             callbacks=None,\n",
+       "                                                             colsample_bylevel=1,\n",
+       "                                                             c...\n",
+       "                                                             max_delta_step=0,\n",
+       "                                                             max_depth=6,\n",
+       "                                                             max_leaves=0,\n",
+       "                                                             min_child_weight=1,\n",
+       "                                                             missing=nan,\n",
+       "                                                             monotone_constraints=&#x27;()&#x27;,\n",
+       "                                                             n_estimators=100,\n",
+       "                                                             n_jobs=0,\n",
+       "                                                             num_parallel_tree=1,\n",
+       "                                                             predictor=&#x27;auto&#x27;,\n",
+       "                                                             random_state=0,\n",
+       "                                                             reg_alpha=0,\n",
+       "                                                             reg_lambda=1, ...)),\n",
+       "                                               (&#x27;gbr&#x27;,\n",
+       "                                                GradientBoostingRegressor(learning_rate=0.15,\n",
+       "                                                                          max_depth=8,\n",
+       "                                                                          max_features=&#x27;sqrt&#x27;,\n",
+       "                                                                          n_estimators=200,\n",
+       "                                                                          subsample=0.85))],\n",
+       "                                   final_estimator=LinearSVR()))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-319\" type=\"checkbox\" ><label for=\"sk-estimator-id-319\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;Data_pipeline: Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Data_pipeline: Pipeline</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;step1&#x27;,\n",
+       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                                   transformers=[(&#x27;col_tnf&#x27;,\n",
+       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
+       "                                                                sparse_output=False),\n",
+       "                                                  [0, 2])])),\n",
+       "                (&#x27;step2&#x27;, StandardScaler())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-320\" type=\"checkbox\" ><label for=\"sk-estimator-id-320\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;step1: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for step1: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                  transformers=[(&#x27;col_tnf&#x27;,\n",
+       "                                 OneHotEncoder(drop=&#x27;first&#x27;,\n",
+       "                                               sparse_output=False),\n",
+       "                                 [0, 2])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-321\" type=\"checkbox\" ><label for=\"sk-estimator-id-321\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">col_tnf</label><div class=\"sk-toggleable__content fitted\"><pre>[0, 2]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-322\" type=\"checkbox\" ><label for=\"sk-estimator-id-322\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, sparse_output=False)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-323\" type=\"checkbox\" ><label for=\"sk-estimator-id-323\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">remainder</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;availability&#x27;, &#x27;total_sqft&#x27;, &#x27;bath&#x27;, &#x27;bhk&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-324\" type=\"checkbox\" ><label for=\"sk-estimator-id-324\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">passthrough</label><div class=\"sk-toggleable__content fitted\"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-325\" type=\"checkbox\" ><label for=\"sk-estimator-id-325\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-326\" type=\"checkbox\" ><label for=\"sk-estimator-id-326\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;stacking_model: StackingRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.StackingRegressor.html\">?<span>Documentation for stacking_model: StackingRegressor</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StackingRegressor(estimators=[(&#x27;xgbr&#x27;,\n",
+       "                               XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;,\n",
+       "                                            callbacks=None, colsample_bylevel=1,\n",
+       "                                            colsample_bynode=1,\n",
+       "                                            colsample_bytree=1,\n",
+       "                                            early_stopping_rounds=None,\n",
+       "                                            enable_categorical=False,\n",
+       "                                            eval_metric=None, gamma=0,\n",
+       "                                            gpu_id=-1, grow_policy=&#x27;depthwise&#x27;,\n",
+       "                                            importance_type=None,\n",
+       "                                            interaction_constraints=&#x27;&#x27;,\n",
+       "                                            learning_rate=0.300000012,\n",
+       "                                            max_bin=2...\n",
+       "                                            max_delta_step=0, max_depth=6,\n",
+       "                                            max_leaves=0, min_child_weight=1,\n",
+       "                                            missing=nan,\n",
+       "                                            monotone_constraints=&#x27;()&#x27;,\n",
+       "                                            n_estimators=100, n_jobs=0,\n",
+       "                                            num_parallel_tree=1,\n",
+       "                                            predictor=&#x27;auto&#x27;, random_state=0,\n",
+       "                                            reg_alpha=0, reg_lambda=1, ...)),\n",
+       "                              (&#x27;gbr&#x27;,\n",
+       "                               GradientBoostingRegressor(learning_rate=0.15,\n",
+       "                                                         max_depth=8,\n",
+       "                                                         max_features=&#x27;sqrt&#x27;,\n",
+       "                                                         n_estimators=200,\n",
+       "                                                         subsample=0.85))],\n",
+       "                  final_estimator=LinearSVR())</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>xgbr</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-327\" type=\"checkbox\" ><label for=\"sk-estimator-id-327\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">XGBRegressor</label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
+       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
+       "             early_stopping_rounds=None, enable_categorical=False,\n",
+       "             eval_metric=None, gamma=0, gpu_id=-1, grow_policy=&#x27;depthwise&#x27;,\n",
+       "             importance_type=None, interaction_constraints=&#x27;&#x27;,\n",
+       "             learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
+       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
+       "             missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=0,\n",
+       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, reg_alpha=0,\n",
+       "             reg_lambda=1, ...)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>gbr</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-328\" type=\"checkbox\" ><label for=\"sk-estimator-id-328\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;GradientBoostingRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html\">?<span>Documentation for GradientBoostingRegressor</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingRegressor(learning_rate=0.15, max_depth=8, max_features=&#x27;sqrt&#x27;,\n",
+       "                          n_estimators=200, subsample=0.85)</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>final_estimator</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-329\" type=\"checkbox\" ><label for=\"sk-estimator-id-329\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LinearSVR<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.LinearSVR.html\">?<span>Documentation for LinearSVR</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LinearSVR()</pre></div> </div></div></div></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('Data_pipeline',\n",
+       "                 Pipeline(steps=[('step1',\n",
+       "                                  ColumnTransformer(remainder='passthrough',\n",
+       "                                                    transformers=[('col_tnf',\n",
+       "                                                                   OneHotEncoder(drop='first',\n",
+       "                                                                                 sparse_output=False),\n",
+       "                                                                   [0, 2])])),\n",
+       "                                 ('step2', StandardScaler())])),\n",
+       "                ('stacking_model',\n",
+       "                 StackingRegressor(estimators=[('xgbr',\n",
+       "                                                XGBRegressor(base_score=0.5,\n",
+       "                                                             booster='gbtree',\n",
+       "                                                             callbacks=None,\n",
+       "                                                             colsample_bylevel=1,\n",
+       "                                                             c...\n",
+       "                                                             max_delta_step=0,\n",
+       "                                                             max_depth=6,\n",
+       "                                                             max_leaves=0,\n",
+       "                                                             min_child_weight=1,\n",
+       "                                                             missing=nan,\n",
+       "                                                             monotone_constraints='()',\n",
+       "                                                             n_estimators=100,\n",
+       "                                                             n_jobs=0,\n",
+       "                                                             num_parallel_tree=1,\n",
+       "                                                             predictor='auto',\n",
+       "                                                             random_state=0,\n",
+       "                                                             reg_alpha=0,\n",
+       "                                                             reg_lambda=1, ...)),\n",
+       "                                               ('gbr',\n",
+       "                                                GradientBoostingRegressor(learning_rate=0.15,\n",
+       "                                                                          max_depth=8,\n",
+       "                                                                          max_features='sqrt',\n",
+       "                                                                          n_estimators=200,\n",
+       "                                                                          subsample=0.85))],\n",
+       "                                   final_estimator=LinearSVR()))])"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Now as Gradient Boosting is performing well lets create a stacking model using GradientBoosting and Xgboost Model that was used earlier\n",
+    "xgbr = pipeline.steps[-1][1]\n",
+    "gbr = gscv1.best_estimator_.steps[-1][1]\n",
+    "\n",
+    "from sklearn.svm import LinearSVR\n",
+    "\n",
+    "estimators = [('xgbr', xgbr), ('gbr', gbr)]\n",
+    "stacking_model = StackingRegressor(estimators = estimators , final_estimator = LinearSVR())\n",
+    "stacking_model_pipeline = Pipeline([\n",
+    "                            ('Data_pipeline' , data_pipeline),\n",
+    "                            ('stacking_model' , stacking_model)\n",
+    "                        ])\n",
+    "stacking_model_pipeline.fit(x_train , y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18.38473245692642"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(stacking_model_pipeline.predict(x_test) , y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n"
+     ]
+    }
+   ],
+   "source": [
+    "param_grid = {\n",
+    "    'stacking_model__final_estimator__C': [0.01, 0.1, 1, 10, 100],      \n",
+    "    'stacking_model__final_estimator__epsilon': [0.01, 0.1, 0.2, 0.5, 1], \n",
+    "    'stacking_model__final_estimator__tol': [1e-4, 1e-3, 1e-2, 1e-1],     \n",
+    "}\n",
+    "\n",
+    "gscv3 = GridSearchCV(stacking_model_pipeline , param_grid , n_jobs = -1 , scoring = 'neg_mean_squared_error' , verbose = 3)\n",
+    "gscv3.fit(x_train , y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 9e1a6e69668540a39dd843a6c2c3bbb07cef1ccb Mon Sep 17 00:00:00 2001
From: Bingumalla Likith <138435877+binguliki@users.noreply.github.com>
Date: Mon, 3 Jun 2024 17:44:09 +0530
Subject: [PATCH 3/3] "Add Stacking Model Image"

---
 .../StackingModel.png                          | Bin 0 -> 137859 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 Hedging with Real Estate/New_Upgrade_Bengaluru_House_Price_Predition/StackingModel.png

diff --git a/Hedging with Real Estate/New_Upgrade_Bengaluru_House_Price_Predition/StackingModel.png b/Hedging with Real Estate/New_Upgrade_Bengaluru_House_Price_Predition/StackingModel.png
new file mode 100644
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