Advanced House Prediction - Feature Selection Comparison.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advanced House Pricing Prediction: Feature Importance Case Study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pdpipe in c:\\programdata\\anaconda3\\lib\\site-packages (0.0.46)\n",
      "Requirement already satisfied: skutil>=0.0.15 in c:\\programdata\\anaconda3\\lib\\site-packages (from pdpipe) (0.0.16)\n",
      "Requirement already satisfied: tqdm in c:\\programdata\\anaconda3\\lib\\site-packages (from pdpipe) (4.42.0)\n",
      "Requirement already satisfied: sortedcontainers in c:\\programdata\\anaconda3\\lib\\site-packages (from pdpipe) (2.0.5)\n",
      "Requirement already satisfied: pandas>=0.18.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pdpipe) (0.25.3)\n",
      "Requirement already satisfied: strct in c:\\programdata\\anaconda3\\lib\\site-packages (from pdpipe) (0.0.30)\n",
      "Requirement already satisfied: decore in c:\\programdata\\anaconda3\\lib\\site-packages (from skutil>=0.0.15->pdpipe) (0.0.1)\n",
      "Requirement already satisfied: numpy in c:\\programdata\\anaconda3\\lib\\site-packages (from skutil>=0.0.15->pdpipe) (1.16.2)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas>=0.18.0->pdpipe) (2.7.3)\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas>=0.18.0->pdpipe) (2018.5)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\user-pc\\appdata\\roaming\\python\\python37\\site-packages (from python-dateutil>=2.6.1->pandas>=0.18.0->pdpipe) (1.12.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 20.1; however, version 20.1.1 is available.\n",
      "You should consider upgrading via the 'c:\\programdata\\anaconda3\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "!pip install pdpipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading neccesary packages\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pdpipe as pdp\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "#\n",
    "\n",
    "from scipy import stats\n",
    "from scipy.stats import skew, boxcox_normmax, norm\n",
    "from scipy.special import boxcox1p\n",
    "\n",
    "#\n",
    "\n",
    "from typing import Dict, List, Tuple, Sequence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Importance in the Age of Interpretable AI/ML\n",
    "\n",
    "In the recent years, we see increasing demand in interpretable AI/ML. Human decision-makers would like to trust their AI-based decision support on the ground of rationale rather than via religious belief in what AI system calculates/suggest/forecasts.\n",
    "\n",
    "It was quite easy in good old days when the stage was preoccupied by the easily interpretable ML algorithms (like Linear regression, polynomial regression, logistic regression, CART-based decision trees etc.).\n",
    "\n",
    "Unfortunately such algorithms lacked accuracy in many real-world business forecasting and decision support scenarios. It resulted in the advent of highly accurate and complicated algorithms (starting from Random Forests through Gradient Boosting Machine-like algorithms up to the wide spectrum of Neural Networks of the second generation). \n",
    "\n",
    "As everything in this world, however, the accuracy came at a price. There was no more easy way to interpret the decision-making flow of such AI/ML algorithms in a human-friendly and rational way.\n",
    "\n",
    "One of the early attempts to address the challenge was adding the supplementary capability to calculate the feature importance scores by some of the modern algorithms (this is featured, for instance, by Random Forest, GBM, or lightgbm).\n",
    "\n",
    "The poor side of the feature importance scores is that they are sometimes confusing / misleading due to the fact they are calculated separately from the ML model training itself.\n",
    "\n",
    "Such a collision gave a birth to several analytical algorithms to calculate the feature importance / do the feature selection for ML models. As opposed to the classical statistical (filtering) approaches (where feature importance is determined on a basis of a certain statistical metric, whether it is a Pierson correlation or Chi Square), such techniques embrace a series of model training experiments under certain feature space tweaks. In such a way, they relatively score the importance of each feature for a specific model to train.\n",
    "\n",
    "In this notebook, we are going to review and implement such feature selection / feature importance detection methods. They all will be useful in the strive to build the industrial culture of interpretable AI/ML. \n",
    "\n",
    "With this direction, we are on a par with the industry giant like Google (who recently launched the services of Explainable AI – see https://cloud.google.com/explainable-ai).\n",
    "\n",
    "**Note**: in addition to building comprehensive interpretable ML models, the relevant feature selection will also help you to handle other ML challenges as follows\n",
    "- Dropping the garbage (non-informative) features from your modelling pipeline\n",
    "- Tackling the curse of dimensionality as well as minimizing the impact of the model overfitting\n",
    "- Improve accuracy/performance of our model forecasting\n",
    "\n",
    "Now, we are ready to get our hands dirty in the code, to orchestrate a solid ML solution that benefits from industrial feature selection techniques.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Initial Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data_file_path(in_kaggle: bool) -> Tuple[str, str]:\n",
    "    train_set_path = ''\n",
    "    test_set_path = ''\n",
    "    \n",
    "    if in_kaggle:\n",
    "        # running in Kaggle, inside \n",
    "        # 'House Prices: Advanced Regression Techniques' competition kernel container\n",
    "        train_set_path = '../input/house-prices-advanced-regression-techniques/train.csv'\n",
    "        test_set_path = '../input/house-prices-advanced-regression-techniques/test.csv'\n",
    "    else:\n",
    "        # running locally\n",
    "        train_set_path = 'data/train.csv'\n",
    "        test_set_path = 'data/test.csv'\n",
    "    \n",
    "    return train_set_path,test_set_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Loading datasets\n",
    "in_kaggle = False\n",
    "train_set_path, test_set_path = get_data_file_path(in_kaggle)\n",
    "\n",
    "train = pd.read_csv(train_set_path)\n",
    "test = pd.read_csv(test_set_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 81)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check train dimension\n",
    "display(train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 80)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check test dimension\n",
    "display(test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initial Data Transformation: Dropping Id col\n",
    "\n",
    "Starting this section and down the activities in **'Setting Model Data and Log Transforming the Target'** section below, we are going to reuse the feature enginering steps defined, justified and explained in https://www.kaggle.com/datafan07/beginner-eda-with-feature-eng-and-blending-models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping unneccessary columns, merging training and test sets\n",
    "\n",
    "train.drop('Id', axis=1, inplace=True)\n",
    "test.drop('Id', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dropping Outliers From Training Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping outliers after detecting them by eye\n",
    "\n",
    "train = train.drop(train[(train['OverallQual'] < 5)\n",
    "                                  & (train['SalePrice'] > 200000)].index)\n",
    "train = train.drop(train[(train['GrLivArea'] > 4000)\n",
    "                                  & (train['SalePrice'] < 200000)].index)\n",
    "train = train.drop(train[(train['GarageArea'] > 1200)\n",
    "                                  & (train['SalePrice'] < 200000)].index)\n",
    "train = train.drop(train[(train['TotalBsmtSF'] > 3000)\n",
    "                                  & (train['SalePrice'] > 320000)].index)\n",
    "train = train.drop(train[(train['1stFlrSF'] < 3000)\n",
    "                                  & (train['SalePrice'] > 600000)].index)\n",
    "train = train.drop(train[(train['1stFlrSF'] > 3000)\n",
    "                                  & (train['SalePrice'] < 200000)].index)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merging Train and Test Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2908, 79)\n"
     ]
    }
   ],
   "source": [
    "# Backing up target variables and dropping them from train data\n",
    "\n",
    "y = train['SalePrice'].reset_index(drop=True)\n",
    "train_features = train.drop(['SalePrice'], axis=1)\n",
    "test_features = test\n",
    "\n",
    "# Merging features\n",
    "\n",
    "features = pd.concat([train_features, test_features]).reset_index(drop=True)\n",
    "print(features.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imputing Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of NaN including columns where NaN's mean none.\n",
    "none_cols = [\n",
    "    'Alley', 'PoolQC', 'MiscFeature', 'Fence', 'FireplaceQu', 'GarageType',\n",
    "    'GarageFinish', 'GarageQual', 'GarageCond', 'BsmtQual', 'BsmtCond',\n",
    "    'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'MasVnrType'\n",
    "]\n",
    "\n",
    "# List of NaN including columns where NaN's mean 0.\n",
    "\n",
    "zero_cols = [\n",
    "    'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'BsmtFullBath',\n",
    "    'BsmtHalfBath', 'GarageYrBlt', 'GarageArea', 'GarageCars', 'MasVnrArea'\n",
    "]\n",
    "\n",
    "# List of NaN including columns where NaN's actually missing gonna replaced with mode.\n",
    "\n",
    "freq_cols = [\n",
    "    'Electrical', 'Exterior1st', 'Exterior2nd', 'Functional', 'KitchenQual',\n",
    "    'SaleType', 'Utilities'\n",
    "]\n",
    "\n",
    "# Filling the list of columns above:\n",
    "\n",
    "for col in zero_cols:\n",
    "    features[col].replace(np.nan, 0, inplace=True)\n",
    "\n",
    "for col in none_cols:\n",
    "    features[col].replace(np.nan, 'None', inplace=True)\n",
    "\n",
    "for col in freq_cols:\n",
    "    features[col].replace(np.nan, features[col].mode()[0], inplace=True)\n",
    "    \n",
    "# Filling MSZoning according to MSSubClass\n",
    "features['MSZoning'] = features.groupby('MSSubClass')['MSZoning'].apply(\n",
    "    lambda x: x.fillna(x.mode()[0]))\n",
    "\n",
    "# Filling LotFrontage according to Neighborhood\n",
    "features['LotFrontage'] = features.groupby(\n",
    "    ['Neighborhood'])['LotFrontage'].apply(lambda x: x.fillna(x.median()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numeric Features to be Treated as Categories\n",
    "\n",
    "Below we cast some numeric features to categories based on the logical assessment of the feature essence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Features which numerical on data but should be treated as category.\n",
    "features['MSSubClass'] = features['MSSubClass'].astype(str)\n",
    "features['YrSold'] = features['YrSold'].astype(str)\n",
    "features['MoSold'] = features['MoSold'].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# New Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Binning the Rare Category Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    }
   ],
   "source": [
    "# Transforming rare values(less than 10) into one group - dimensionality reduction\n",
    "\n",
    "others = [\n",
    "    'Condition1', 'Condition2', 'RoofMatl', 'Exterior1st', 'Exterior2nd',\n",
    "    'Heating', 'Electrical', 'Functional', 'SaleType'\n",
    "]\n",
    "\n",
    "for col in others:\n",
    "    mask = features[col].isin(\n",
    "        features[col].value_counts()[features[col].value_counts() < 10].index)\n",
    "    features[col][mask] = 'Other'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Converting Some Categorical Variables to Numeric Ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting some of the categorical values to numeric ones.\n",
    "\n",
    "neigh_map = {\n",
    "    'MeadowV': 1,\n",
    "    'IDOTRR': 1,\n",
    "    'BrDale': 1,\n",
    "    'BrkSide': 2,\n",
    "    'OldTown': 2,\n",
    "    'Edwards': 2,\n",
    "    'Sawyer': 3,\n",
    "    'Blueste': 3,\n",
    "    'SWISU': 3,\n",
    "    'NPkVill': 3,\n",
    "    'NAmes': 3,\n",
    "    'Mitchel': 4,\n",
    "    'SawyerW': 5,\n",
    "    'NWAmes': 5,\n",
    "    'Gilbert': 5,\n",
    "    'Blmngtn': 5,\n",
    "    'CollgCr': 5,\n",
    "    'ClearCr': 6,\n",
    "    'Crawfor': 6,\n",
    "    'Veenker': 7,\n",
    "    'Somerst': 7,\n",
    "    'Timber': 8,\n",
    "    'StoneBr': 9,\n",
    "    'NridgHt': 10,\n",
    "    'NoRidge': 10\n",
    "}\n",
    "\n",
    "features['Neighborhood'] = features['Neighborhood'].map(neigh_map).astype('int')\n",
    "ext_map = {'Po': 1, 'Fa': 2, 'TA': 3, 'Gd': 4, 'Ex': 5}\n",
    "features['ExterQual'] = features['ExterQual'].map(ext_map).astype('int')\n",
    "features['ExterCond'] = features['ExterCond'].map(ext_map).astype('int')\n",
    "bsm_map = {'None': 0, 'Po': 1, 'Fa': 2, 'TA': 3, 'Gd': 4, 'Ex': 5}\n",
    "features['BsmtQual'] = features['BsmtQual'].map(bsm_map).astype('int')\n",
    "features['BsmtCond'] = features['BsmtCond'].map(bsm_map).astype('int')\n",
    "bsmf_map = {\n",
    "    'None': 0,\n",
    "    'Unf': 1,\n",
    "    'LwQ': 2,\n",
    "    'Rec': 3,\n",
    "    'BLQ': 4,\n",
    "    'ALQ': 5,\n",
    "    'GLQ': 6\n",
    "}\n",
    "\n",
    "features['BsmtFinType1'] = features['BsmtFinType1'].map(bsmf_map).astype('int')\n",
    "features['BsmtFinType2'] = features['BsmtFinType2'].map(bsmf_map).astype('int')\n",
    "heat_map = {'Po': 1, 'Fa': 2, 'TA': 3, 'Gd': 4, 'Ex': 5}\n",
    "features['HeatingQC'] = features['HeatingQC'].map(heat_map).astype('int')\n",
    "features['KitchenQual'] = features['KitchenQual'].map(heat_map).astype('int')\n",
    "features['FireplaceQu'] = features['FireplaceQu'].map(bsm_map).astype('int')\n",
    "features['GarageCond'] = features['GarageCond'].map(bsm_map).astype('int')\n",
    "features['GarageQual'] = features['GarageQual'].map(bsm_map).astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating New Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating new features  based on previous observations\n",
    "\n",
    "features['TotalSF'] = (features['BsmtFinSF1'] + features['BsmtFinSF2'] +\n",
    "                       features['1stFlrSF'] + features['2ndFlrSF'])\n",
    "features['TotalBathrooms'] = (features['FullBath'] +\n",
    "                              (0.5 * features['HalfBath']) +\n",
    "                              features['BsmtFullBath'] +\n",
    "                              (0.5 * features['BsmtHalfBath']))\n",
    "\n",
    "features['TotalPorchSF'] = (features['OpenPorchSF'] + features['3SsnPorch'] +\n",
    "                            features['EnclosedPorch'] +\n",
    "                            features['ScreenPorch'] + features['WoodDeckSF'])\n",
    "\n",
    "features['YearBlRm'] = (features['YearBuilt'] + features['YearRemodAdd'])\n",
    "\n",
    "# Merging quality and conditions\n",
    "\n",
    "features['TotalExtQual'] = (features['ExterQual'] + features['ExterCond'])\n",
    "features['TotalBsmQual'] = (features['BsmtQual'] + features['BsmtCond'] +\n",
    "                            features['BsmtFinType1'] +\n",
    "                            features['BsmtFinType2'])\n",
    "features['TotalGrgQual'] = (features['GarageQual'] + features['GarageCond'])\n",
    "features['TotalQual'] = features['OverallQual'] + features[\n",
    "    'TotalExtQual'] + features['TotalBsmQual'] + features[\n",
    "        'TotalGrgQual'] + features['KitchenQual'] + features['HeatingQC']\n",
    "\n",
    "# Creating new features by using new quality indicators\n",
    "\n",
    "features['QualGr'] = features['TotalQual'] * features['GrLivArea']\n",
    "features['QualBsm'] = features['TotalBsmQual'] * (features['BsmtFinSF1'] +\n",
    "                                                  features['BsmtFinSF2'])\n",
    "features['QualPorch'] = features['TotalExtQual'] * features['TotalPorchSF']\n",
    "features['QualExt'] = features['TotalExtQual'] * features['MasVnrArea']\n",
    "features['QualGrg'] = features['TotalGrgQual'] * features['GarageArea']\n",
    "features['QlLivArea'] = (features['GrLivArea'] -\n",
    "                         features['LowQualFinSF']) * (features['TotalQual'])\n",
    "features['QualSFNg'] = features['QualGr'] * features['Neighborhood']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating some simple features\n",
    "\n",
    "features['HasPool'] = features['PoolArea'].apply(lambda x: 1 if x > 0 else 0)\n",
    "\n",
    "features['Has2ndFloor'] = features['2ndFlrSF'].apply(lambda x: 1 if x > 0 else 0)\n",
    "\n",
    "features['HasGarage'] = features['QualGrg'].apply(lambda x: 1 if x > 0 else 0)\n",
    "\n",
    "features['HasBsmt'] = features['QualBsm'].apply(lambda x: 1 if x > 0 else 0)\n",
    "\n",
    "features['HasFireplace'] = features['Fireplaces'].apply(lambda x: 1 if x > 0 else 0)\n",
    "\n",
    "features['HasPorch'] = features['QualPorch'].apply(lambda x: 1 if x > 0 else 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transforming The Skewed Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:3508: PearsonRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.\n",
      "  warnings.warn(PearsonRConstantInputWarning())\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:3538: PearsonRNearConstantInputWarning: An input array is nearly constant; the computed correlation coefficent may be inaccurate.\n",
      "  warnings.warn(PearsonRNearConstantInputWarning())\n"
     ]
    }
   ],
   "source": [
    "possible_skewed = [\n",
    "    'LotFrontage', 'LotArea', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2',\n",
    "    'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'GrLivArea',\n",
    "    'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch',\n",
    "    'ScreenPorch', 'PoolArea', 'LowQualFinSF', 'MiscVal'\n",
    "]\n",
    "\n",
    "# Finding skewness of the numerical features\n",
    "\n",
    "skew_features = np.abs(features[possible_skewed].apply(lambda x: skew(x)).sort_values(\n",
    "    ascending=False))\n",
    "\n",
    "# Filtering skewed features\n",
    "\n",
    "high_skew = skew_features[skew_features > 0.3]\n",
    "\n",
    "# Taking indexes of high skew\n",
    "\n",
    "skew_index = high_skew.index\n",
    "\n",
    "# Applying boxcox transformation to fix skewness\n",
    "\n",
    "for i in skew_index:\n",
    "    features[i] = boxcox1p(features[i], boxcox_normmax(features[i] + 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dropping Unnecessary Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Features to drop\n",
    "\n",
    "to_drop = [\n",
    "    'Utilities',\n",
    "    'PoolQC',\n",
    "    'YrSold',\n",
    "    'MoSold',\n",
    "    'ExterQual',\n",
    "    'BsmtQual',\n",
    "    'GarageQual',\n",
    "    'KitchenQual',\n",
    "    'HeatingQC',\n",
    "]\n",
    "\n",
    "# Dropping ML-irrelevant features\n",
    "\n",
    "features.drop(columns=to_drop, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Label Encoding The Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting dummy variables for ategorical data\n",
    "features = pd.get_dummies(data=features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Check of The Data Before Feature Selection and ML Experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of missing values: 0\n"
     ]
    }
   ],
   "source": [
    "print(f'Number of missing values: {features.isna().sum().sum()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2908, 226)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Separating Train and Test Sets Again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separating train and test set\n",
    "\n",
    "train = features.iloc[:len(y), :]\n",
    "test = features.iloc[len(train):, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting Model Data and Log Transforming the Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting model data\n",
    "\n",
    "X = train\n",
    "X_test = test\n",
    "y = np.log1p(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What’s In Your Toolbox?\n",
    "\n",
    "Before we jump on the practical implementation of instrumentla Feature Importance calculation and Feature selection techniques, I would like to briefly outline the essence of each of the methods we are going to use down the road.\n",
    "\n",
    "In real machine-learning problems, tackling the curse of dimensionality as well as increasing the model interpretability are translated to solid feature selection techniques.\n",
    "\n",
    "Apart from the filtering-based methods (correlation- or chi-square-based) or PCA (the latter is mostly applicable to linear regression problems), there is a set of analytical (computational) feature selection methods. \n",
    "In the subsections below, we will review the available analytical feature selection options, along with the technical implementation details for them in Python.\n",
    "\n",
    "## Wrapper-based methods\n",
    "Wrapper-based methods treat the selection of a set of features as a search problem. \n",
    "\n",
    "**RFE** and its implementation in sckit-learn can be referred to as one of the good options to go on with it, for example. \n",
    "The utility function to benefit from RFE feature importance score calculations is provided below\n",
    "\n",
    "https://gist.github.com/gvyshnya/7349198e74b4c5fc6caad18ac150ff07\n",
    "\n",
    "Other options in the wrapper-based feature selection domain are Backward Elimination, Forward Selection, and Bidirectional Elimination.\n",
    "\n",
    "## Embedded methods\n",
    "Embedded methods are applicable to ML algorithms that have built-in feature selection methods. For instance, **Lasso, Random Forest, or lightgbm** have them.\n",
    "\n",
    "From the technical standpoint, feature selection with the embedded methods relies on scikit-learn’s SelectFromModel. You can see it in action with the demo code snippet below\n",
    "\n",
    "https://gist.github.com/gvyshnya/de775c04f7f752eb66c1d40ed40bcb05\n",
    "\n",
    "## Permutation method\n",
    "\n",
    "Put simply, this method changes the data in a column and then tests how much that affects the model accuracy. If changing the data in a column drops the accuracy, then that column is assumed to be important.\n",
    "\n",
    "You can benefit from the out-of-the box scikit-learn utility to facilitate permutable feature importance calculation. I wrapped it up in a utility function below\n",
    "https://gist.github.com/gvyshnya/abe6c06767922f8762bd288c2d897ce5\n",
    "\n",
    "## Drop-Column Method\n",
    "\n",
    "This method is focused on measuring performance of a ML model on a full-feature dataset of predictors vs. the set of smaller datasets (each one of them to drop exactly one feature from the original fully featured dataset). In such a way, the difference between the performance of the model on the original dataset and every dataset with a dropped feature will be a metric of the dropped feature importance for the model performance.\n",
    "\n",
    "I could not find the stable Pythonic implementation of such a feature selection / feature importance measurement therefore I ended up with the custom implementation below\n",
    "\n",
    "https://gist.github.com/gvyshnya/513080f611491b8baa08cc1bf6987144\n",
    "\n",
    "## Several Practical Considerations\n",
    "When deciding which feature selection method is the best one for your specific problem, you should keep in mind the points below\n",
    "- there is no a single silver bullet-proof method of feature selection that worsks well for each and every project - typically, you will always have to undertake several feature selection experiments (using different methods) to figure out which one leads to the ML model with the highest performance metric score\n",
    "- all methods except filter-based ones have its computational time tall, and it may take too much time to go through them appropriately for large datasets\n",
    "- embedded methods sometimes introduce the confusion (or even a misinterpretation on the feature importance) as the feature importance scores are often calculated separately from the model training\n",
    "\n",
    "With that said, let's get back to coding.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelling and Feature Selection Pre-Requisite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(n_jobs=-1, verbose=1)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading neccesary packages for modelling and feature selection\n",
    "from sklearn.model_selection import cross_val_score, KFold, cross_validate\n",
    "from sklearn.feature_selection import RFE, f_regression\n",
    "from sklearn.linear_model import (LinearRegression, Ridge, Lasso)\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# Setting kfold for future use\n",
    "kf = KFold(10, random_state=42, shuffle=True)\n",
    "\n",
    "# Train our baseline RF Regression model for feature importance scoring/feature selection\n",
    "n_trees = 100\n",
    "rf = RandomForestRegressor(n_jobs=-1, n_estimators=n_trees, verbose=1)\n",
    "rf.fit(X, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rfe_select_featurs(X, y, estimator, num_features) -> List[str]:\n",
    "    rfe_selector = RFE(estimator=estimator, \n",
    "                       n_features_to_select=num_features, \n",
    "                       step=10, verbose=5)\n",
    "    rfe_selector.fit(X, y)\n",
    "    rfe_support = rfe_selector.get_support()\n",
    "    rfe_feature = X.loc[:,rfe_support].columns.tolist()\n",
    "    print(str(len(rfe_feature)), 'selected features')\n",
    "    \n",
    "    return rfe_feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# total list of features\n",
    "colnames = X.columns\n",
    "# Define dictionary to store our rankings\n",
    "ranks = {}\n",
    "# Create our function which stores the feature rankings to the ranks dictionary\n",
    "def ranking(ranks, names, order=1):\n",
    "    minmax = MinMaxScaler()\n",
    "    ranks = minmax.fit_transform(order*np.array([ranks]).T).T[0]\n",
    "    ranks = map(lambda x: round(x,2), ranks)\n",
    "    return dict(zip(names, ranks))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Importance Scores From Model and via RFE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 226 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 225 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 224 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
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      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 223 features.\n"
     ]
    },
    {
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      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
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    },
    {
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     "text": [
      "Fitting estimator with 222 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
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      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
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    {
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     "text": [
      "Fitting estimator with 221 features.\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
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      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 220 features.\n"
     ]
    },
    {
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      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
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    {
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     "text": [
      "Fitting estimator with 219 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
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     "text": [
      "Fitting estimator with 218 features.\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
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      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "Fitting estimator with 217 features.\n"
     ]
    },
    {
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      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
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      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
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    {
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     "output_type": "stream",
     "text": [
      "Fitting estimator with 216 features.\n"
     ]
    },
    {
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      "Fitting estimator with 215 features.\n"
     ]
    },
    {
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      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
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      "Fitting estimator with 214 features.\n"
     ]
    },
    {
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      "Fitting estimator with 213 features.\n"
     ]
    },
    {
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      "Fitting estimator with 212 features.\n"
     ]
    },
    {
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      "Fitting estimator with 211 features.\n"
     ]
    },
    {
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      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
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      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
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    {
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     "output_type": "stream",
     "text": [
      "Fitting estimator with 210 features.\n"
     ]
    },
    {
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      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
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      "Fitting estimator with 209 features.\n"
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    {
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      "Fitting estimator with 208 features.\n"
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      "Fitting estimator with 207 features.\n"
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    {
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      "Fitting estimator with 206 features.\n"
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    {
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      "Fitting estimator with 205 features.\n"
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      "Fitting estimator with 204 features.\n"
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    {
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      "Fitting estimator with 203 features.\n"
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      "Fitting estimator with 202 features.\n"
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      "Fitting estimator with 201 features.\n"
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    {
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      "Fitting estimator with 200 features.\n"
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     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 199 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 198 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 197 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 196 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 195 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 194 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 193 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 192 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 191 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 190 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 189 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 188 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 187 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 186 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 185 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 184 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 183 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 182 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 181 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 180 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 179 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 178 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 177 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 176 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 175 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 174 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 173 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 172 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 171 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 170 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 169 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 168 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 167 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 166 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 165 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 164 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 163 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 162 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 161 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 160 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 159 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 158 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 157 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 156 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 155 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 154 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 153 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 152 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 151 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 150 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 149 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 148 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 147 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 146 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 145 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 144 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 143 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 142 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 141 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 140 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 139 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 138 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 137 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 136 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 135 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 134 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 133 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 132 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 131 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 130 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 129 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 128 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 127 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 126 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 125 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 124 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 123 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 122 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 121 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 120 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 119 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 118 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 117 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 116 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
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      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 115 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 114 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 113 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 112 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 111 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 110 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 109 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 108 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 107 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 106 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 105 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 104 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 103 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 102 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 101 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 100 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 99 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 98 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 97 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 96 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 95 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 94 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 93 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 92 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 91 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 90 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 89 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 88 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 87 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 86 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 85 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 84 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 83 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 82 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 81 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 80 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 79 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 78 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 77 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 76 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 75 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 74 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 73 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 72 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 71 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 70 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 69 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 68 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 67 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 66 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 65 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 64 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 63 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 62 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 61 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 60 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 59 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 58 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 57 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 56 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 55 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 54 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 53 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 52 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 51 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 50 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 49 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 48 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 47 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 46 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 45 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 44 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 43 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 42 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 41 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 40 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 39 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 38 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 37 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 36 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 35 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 34 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 33 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 32 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 31 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 30 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 29 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 28 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 27 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 26 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 25 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 24 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 23 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 22 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 21 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 20 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 19 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 18 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 17 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 16 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 15 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 14 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 13 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 12 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 11 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 10 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 9 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 8 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 7 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 6 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 5 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 4 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 3 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 2 features.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    }
   ],
   "source": [
    "# Do FRE feature importance scoring - \n",
    "# stop the search when only the last feature is left\n",
    "rfe = RFE(rf, n_features_to_select=1, verbose =3 )\n",
    "rfe.fit(X, y)\n",
    "ranks[\"RFE_RF\"] = ranking(list(map(float, rfe.ranking_)), colnames, order=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Extract feature importance coefficients as calculated by the trained model\n",
    "ranks[\"RF\"] = ranking(rf.feature_importances_, colnames);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating the Feature Importance Dataframe\n",
    "\n",
    "Let's build the initial version of the dataframe to collect feature importance scores calculated by different methods.\n",
    "\n",
    "Initially, it will contain the RFE scores as well as the scores calculated by the model directly.\n",
    "\n",
    "In future, we will extend it with the additional metrics (permutational feature importance, and drop-column feature importance)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>rfe_importance</th>\n",
       "      <th>alg_importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LotFrontage</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LotArea</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Neighborhood</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OverallQual</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OverallCond</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>YearBuilt</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>YearRemodAdd</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MasVnrArea</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ExterCond</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BsmtCond</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Feature  rfe_importance  alg_importance\n",
       "0   LotFrontage            0.92            0.00\n",
       "1       LotArea            0.97            0.01\n",
       "2  Neighborhood            0.79            0.00\n",
       "3   OverallQual            0.98            0.04\n",
       "4   OverallCond            0.94            0.01\n",
       "5     YearBuilt            0.92            0.00\n",
       "6  YearRemodAdd            0.93            0.01\n",
       "7    MasVnrArea            0.84            0.00\n",
       "8     ExterCond            0.83            0.00\n",
       "9      BsmtCond            0.72            0.00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all ranks\n",
    "# Put the mean scores into a Pandas dataframe\n",
    "rfe_rf_df = pd.DataFrame(list(ranks['RFE_RF'].items()), columns= ['Feature','rfe_importance'])\n",
    "rf_df = pd.DataFrame(list(ranks['RF'].items()), columns= ['Feature','alg_importance'])\n",
    "\n",
    "all_ranks = pd.merge(rfe_rf_df, rf_df, on=['Feature'])\n",
    "\n",
    "display(all_ranks.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Embedded Feature Selection: Selecting Features From a Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12 selected features\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "embeded_rf_selector = SelectFromModel(rf, max_features=200)\n",
    "embeded_rf_selector.fit(X, y)\n",
    "\n",
    "embeded_rf_support = embeded_rf_selector.get_support()\n",
    "embeded_rf_feature = X.loc[:,embeded_rf_support].columns.tolist()\n",
    "print(str(len(embeded_rf_feature)), 'selected features')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['LotArea', 'OverallQual', 'BsmtFinSF1', 'TotalBsmtSF', '1stFlrSF', 'GarageCars', 'TotalSF', 'YearBlRm', 'TotalQual', 'QualGr', 'QlLivArea', 'QualSFNg']\n"
     ]
    }
   ],
   "source": [
    "print(embeded_rf_feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Permutable Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.inspection import permutation_importance\n",
    "\n",
    "\n",
    "# Here's how you use permutation importance\n",
    "def get_permutation_importance(X, y, model) -> pd.DataFrame:\n",
    "    result = permutation_importance(model, X, y, n_repeats=1,\n",
    "                                random_state=0)\n",
    "    \n",
    "    # permutational importance results\n",
    "    result_df = pd.DataFrame(colnames,  columns=['Feature'])\n",
    "    result_df['permutation_importance'] = result.get('importances')\n",
    "    \n",
    "    return result_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
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      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    },
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       "        }</style><table id=\"T_0b624dca_c36e_11ea_8153_3024323c1625\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Feature</th>        <th class=\"col_heading level0 col1\" >permutation_importance</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row0\" class=\"row_heading level0 row0\" >54</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row0_col0\" class=\"data row0 col0\" >QualSFNg</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row0_col1\" class=\"data row0 col1\" >0.492013</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row1\" class=\"row_heading level0 row1\" >53</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row1_col0\" class=\"data row1 col0\" >QlLivArea</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row1_col1\" class=\"data row1 col1\" >0.0754758</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row2\" class=\"row_heading level0 row2\" >15</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row2_col0\" class=\"data row2 col0\" >TotalBsmtSF</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row2_col1\" class=\"data row2 col1\" >0.0206057</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row3\" class=\"row_heading level0 row3\" >48</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row3_col0\" class=\"data row3 col0\" >QualGr</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row3_col1\" class=\"data row3 col1\" >0.0196993</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row4\" class=\"row_heading level0 row4\" >40</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row4_col0\" class=\"data row4 col0\" >TotalSF</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row4_col1\" class=\"data row4 col1\" >0.0129972</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row5\" class=\"row_heading level0 row5\" >3</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row5_col0\" class=\"data row5 col0\" >OverallQual</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row5_col1\" class=\"data row5 col1\" >0.0124263</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row6\" class=\"row_heading level0 row6\" >47</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row6_col0\" class=\"data row6 col0\" >TotalQual</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row6_col1\" class=\"data row6 col1\" >0.0112324</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row7\" class=\"row_heading level0 row7\" >43</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row7_col0\" class=\"data row7 col0\" >YearBlRm</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row7_col1\" class=\"data row7 col1\" >0.00665126</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row8\" class=\"row_heading level0 row8\" >16</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row8_col0\" class=\"data row8 col0\" >1stFlrSF</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row8_col1\" class=\"data row8 col1\" >0.00640665</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row9\" class=\"row_heading level0 row9\" >1</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row9_col0\" class=\"data row9 col0\" >LotArea</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row9_col1\" class=\"data row9 col1\" >0.00564427</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row10\" class=\"row_heading level0 row10\" >30</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row10_col0\" class=\"data row10 col0\" >GarageCars</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row10_col1\" class=\"data row10 col1\" >0.00525041</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row11_col0\" class=\"data row11 col0\" >BsmtFinSF1</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row11_col1\" class=\"data row11 col1\" >0.00418135</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row12\" class=\"row_heading level0 row12\" >4</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row12_col0\" class=\"data row12 col0\" >OverallCond</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row12_col1\" class=\"data row12 col1\" >0.00410234</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row13\" class=\"row_heading level0 row13\" >6</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row13_col0\" class=\"data row13 col0\" >YearRemodAdd</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row13_col1\" class=\"data row13 col1\" >0.00377531</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row14\" class=\"row_heading level0 row14\" >50</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row14_col0\" class=\"data row14 col0\" >QualPorch</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row14_col1\" class=\"data row14 col1\" >0.00306544</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row15\" class=\"row_heading level0 row15\" >14</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row15_col0\" class=\"data row15 col0\" >BsmtUnfSF</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row15_col1\" class=\"data row15 col1\" >0.00297106</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row16\" class=\"row_heading level0 row16\" >31</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row16_col0\" class=\"data row16 col0\" >GarageArea</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row16_col1\" class=\"data row16 col1\" >0.00259859</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row17\" class=\"row_heading level0 row17\" >19</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row17_col0\" class=\"data row17 col0\" >GrLivArea</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row17_col1\" class=\"data row17 col1\" >0.00257277</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row18\" class=\"row_heading level0 row18\" >52</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row18_col0\" class=\"data row18 col0\" >QualGrg</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row18_col1\" class=\"data row18 col1\" >0.00256925</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row19\" class=\"row_heading level0 row19\" >5</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row19_col0\" class=\"data row19 col0\" >YearBuilt</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row19_col1\" class=\"data row19 col1\" >0.00253793</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row20\" class=\"row_heading level0 row20\" >42</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row20_col0\" class=\"data row20 col0\" >TotalPorchSF</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row20_col1\" class=\"data row20 col1\" >0.00216019</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row21\" class=\"row_heading level0 row21\" >49</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row21_col0\" class=\"data row21 col0\" >QualBsm</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row21_col1\" class=\"data row21 col1\" >0.00210693</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row22\" class=\"row_heading level0 row22\" >0</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row22_col0\" class=\"data row22 col0\" >LotFrontage</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row22_col1\" class=\"data row22 col1\" >0.00206073</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row23\" class=\"row_heading level0 row23\" >223</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row23_col0\" class=\"data row23 col0\" >SaleCondition_Family</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row23_col1\" class=\"data row23 col1\" >0.00195218</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row24\" class=\"row_heading level0 row24\" >34</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row24_col0\" class=\"data row24 col0\" >OpenPorchSF</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row24_col1\" class=\"data row24 col1\" >0.00180985</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row25\" class=\"row_heading level0 row25\" >29</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row25_col0\" class=\"data row25 col0\" >GarageYrBlt</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row25_col1\" class=\"data row25 col1\" >0.00173529</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row26\" class=\"row_heading level0 row26\" >44</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row26_col0\" class=\"data row26 col0\" >TotalExtQual</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row26_col1\" class=\"data row26 col1\" >0.00124045</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row27\" class=\"row_heading level0 row27\" >35</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row27_col0\" class=\"data row27 col0\" >EnclosedPorch</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row27_col1\" class=\"data row27 col1\" >0.00120948</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row28\" class=\"row_heading level0 row28\" >41</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row28_col0\" class=\"data row28 col0\" >TotalBathrooms</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row28_col1\" class=\"data row28 col1\" >0.0011782</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0b624dca_c36e_11ea_8153_3024323c1625level0_row29\" class=\"row_heading level0 row29\" >28</th>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row29_col0\" class=\"data row29 col0\" >FireplaceQu</td>\n",
       "                        <td id=\"T_0b624dca_c36e_11ea_8153_3024323c1625row29_col1\" class=\"data row29 col1\" >0.00106407</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x18c9cb868d0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "permutate_df = get_permutation_importance(X, y, rf)\n",
    "permutate_df.sort_values('permutation_importance', \n",
    "                   ascending=False)[\n",
    "                                    ['Feature','permutation_importance'\n",
    "                                    ]\n",
    "                                  ][:30].style.background_gradient(cmap='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Drop-Column Importance\n",
    "\n",
    "Drop-column importance can be done simply by dropping each column and re-training, which you already know how to do if you’ve trained a model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    }
   ],
   "source": [
    "from sklearn.base import clone \n",
    "\n",
    "def drop_col_feat_imp(model, X_train, y_train, random_state = 42):\n",
    "    \n",
    "    # clone the model to have the exact same specification as the one initially trained\n",
    "    model_clone = clone(model)\n",
    "    # set random_state for comparability\n",
    "    model_clone.random_state = random_state\n",
    "    # training and scoring the benchmark model\n",
    "    model_clone.fit(X_train, y_train)\n",
    "    benchmark_score = model_clone.score(X_train, y_train)\n",
    "    # list for storing feature importances\n",
    "    importances = []\n",
    "    \n",
    "    # iterating over all columns and storing feature importance (difference between benchmark and new model)\n",
    "    for col in X_train.columns:\n",
    "        model_clone = clone(model)\n",
    "        model_clone.random_state = random_state\n",
    "        model_clone.fit(X_train.drop(col, axis = 1), y_train)\n",
    "        drop_col_score = model_clone.score(X_train.drop(col, axis = 1), y_train)\n",
    "        importances.append( round( (benchmark_score - drop_col_score)/benchmark_score, 4) )\n",
    "    \n",
    "    importances_df = pd.DataFrame(X_train.columns, columns=['Feature'])\n",
    "    importances_df['drop_col_importance'] = importances\n",
    "    return importances_df\n",
    "\n",
    "drop_col_impt_df = drop_col_feat_imp(rf, X, y)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        }</style><table id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Feature</th>        <th class=\"col_heading level0 col1\" >drop_col_importance</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row0\" class=\"row_heading level0 row0\" >3</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row0_col0\" class=\"data row0 col0\" >OverallQual</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row0_col1\" class=\"data row0 col1\" >0.0004</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row1\" class=\"row_heading level0 row1\" >4</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row1_col0\" class=\"data row1 col0\" >OverallCond</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row1_col1\" class=\"data row1 col1\" >0.0004</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row2\" class=\"row_heading level0 row2\" >156</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row2_col0\" class=\"data row2 col0\" >Exterior2nd_Stucco</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row2_col1\" class=\"data row2 col1\" >0.0002</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row3\" class=\"row_heading level0 row3\" >155</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row3_col0\" class=\"data row3 col0\" >Exterior2nd_Plywood</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row3_col1\" class=\"data row3 col1\" >0.0002</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row4\" class=\"row_heading level0 row4\" >1</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row4_col0\" class=\"data row4 col0\" >LotArea</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row4_col1\" class=\"data row4 col1\" >0.0002</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row5\" class=\"row_heading level0 row5\" >30</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row5_col0\" class=\"data row5 col0\" >GarageCars</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row5_col1\" class=\"data row5 col1\" >0.0002</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row6\" class=\"row_heading level0 row6\" >181</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row6_col0\" class=\"data row6 col0\" >Electrical_FuseF</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row6_col1\" class=\"data row6 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row7\" class=\"row_heading level0 row7\" >180</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row7_col0\" class=\"data row7 col0\" >Electrical_FuseA</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row7_col1\" class=\"data row7 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row8\" class=\"row_heading level0 row8\" >111</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row8_col0\" class=\"data row8 col0\" >Condition2_Feedr</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row8_col1\" class=\"data row8 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row9\" class=\"row_heading level0 row9\" >176</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row9_col0\" class=\"data row9 col0\" >Heating_GasW</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row9_col1\" class=\"data row9 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row10\" class=\"row_heading level0 row10\" >138</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row10_col0\" class=\"data row10 col0\" >Exterior1st_CemntBd</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row10_col1\" class=\"data row10 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row11\" class=\"row_heading level0 row11\" >29</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row11_col0\" class=\"data row11 col0\" >GarageYrBlt</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row11_col1\" class=\"data row11 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row12\" class=\"row_heading level0 row12\" >81</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row12_col0\" class=\"data row12 col0\" >MSZoning_RM</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row12_col1\" class=\"data row12 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row13\" class=\"row_heading level0 row13\" >94</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row13_col0\" class=\"data row13 col0\" >LandContour_Lvl</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row13_col1\" class=\"data row13 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row14\" class=\"row_heading level0 row14\" >171</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row14_col0\" class=\"data row14 col0\" >BsmtExposure_Gd</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row14_col1\" class=\"data row14 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row15\" class=\"row_heading level0 row15\" >72</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row15_col0\" class=\"data row15 col0\" >MSSubClass_70</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row15_col1\" class=\"data row15 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row16\" class=\"row_heading level0 row16\" >166</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row16_col0\" class=\"data row16 col0\" >Foundation_PConc</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row16_col1\" class=\"data row16 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row17\" class=\"row_heading level0 row17\" >43</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row17_col0\" class=\"data row17 col0\" >YearBlRm</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row17_col1\" class=\"data row17 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row18\" class=\"row_heading level0 row18\" >126</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row18_col0\" class=\"data row18 col0\" >HouseStyle_SLvl</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row18_col1\" class=\"data row18 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row19\" class=\"row_heading level0 row19\" >154</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row19_col0\" class=\"data row19 col0\" >Exterior2nd_Other</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row19_col1\" class=\"data row19 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row20\" class=\"row_heading level0 row20\" >184</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row20_col0\" class=\"data row20 col0\" >Functional_Maj1</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row20_col1\" class=\"data row20 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row21\" class=\"row_heading level0 row21\" >11</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row21_col0\" class=\"data row21 col0\" >BsmtFinSF1</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row21_col1\" class=\"data row21 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row22\" class=\"row_heading level0 row22\" >12</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row22_col0\" class=\"data row22 col0\" >BsmtFinType2</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row22_col1\" class=\"data row22 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row23\" class=\"row_heading level0 row23\" >136</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row23_col0\" class=\"data row23 col0\" >Exterior1st_AsbShng</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row23_col1\" class=\"data row23 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row24\" class=\"row_heading level0 row24\" >62</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row24_col0\" class=\"data row24 col0\" >MSSubClass_150</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row24_col1\" class=\"data row24 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row25\" class=\"row_heading level0 row25\" >6</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row25_col0\" class=\"data row25 col0\" >YearRemodAdd</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row25_col1\" class=\"data row25 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row26\" class=\"row_heading level0 row26\" >211</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row26_col0\" class=\"data row26 col0\" >MiscFeature_Othr</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row26_col1\" class=\"data row26 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row27\" class=\"row_heading level0 row27\" >53</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row27_col0\" class=\"data row27 col0\" >QlLivArea</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row27_col1\" class=\"data row27 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row28\" class=\"row_heading level0 row28\" >185</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row28_col0\" class=\"data row28 col0\" >Functional_Min1</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row28_col1\" class=\"data row28 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625level0_row29\" class=\"row_heading level0 row29\" >41</th>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row29_col0\" class=\"data row29 col0\" >TotalBathrooms</td>\n",
       "                        <td id=\"T_6c5d47ba_c36e_11ea_b4d1_3024323c1625row29_col1\" class=\"data row29 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x18c9cd573c8>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_col_impt_df.sort_values('drop_col_importance', \n",
    "                   ascending=False)[\n",
    "                                    ['Feature','drop_col_importance'\n",
    "                                    ]\n",
    "                                  ][:30].style.background_gradient(cmap='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merging All Feature Importance Metrics Into a Single Results Dataframe\n",
    "\n",
    "Now we are going to add *permutation_importance* and *drop_col_importance* columns to *all_ranks* dataframe as well as calculate the average (mean) feature importance score across all of 4 methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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       "            color:  #000000;\n",
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       "            background-color:  #ffff66;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row219_col5 {\n",
       "            background-color:  #add666;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row220_col1 {\n",
       "            background-color:  #239166;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row220_col2 {\n",
       "            background-color:  #ffff66;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row220_col3 {\n",
       "            background-color:  #aad466;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row220_col4 {\n",
       "            background-color:  #ffff66;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row220_col5 {\n",
       "            background-color:  #a7d366;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row221_col1 {\n",
       "            background-color:  #fdfe66;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row221_col2 {\n",
       "            background-color:  #ffff66;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row221_col3 {\n",
       "            background-color:  #ffff66;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row221_col4 {\n",
       "            background-color:  #ffff66;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row221_col5 {\n",
       "            background-color:  #fefe66;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row222_col1 {\n",
       "            background-color:  #a8d366;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row222_col2 {\n",
       "            background-color:  #ffff66;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row222_col3 {\n",
       "            background-color:  #aad466;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row222_col4 {\n",
       "            background-color:  #ffff66;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row222_col5 {\n",
       "            background-color:  #ddee66;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row223_col1 {\n",
       "            background-color:  #2e9666;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row223_col2 {\n",
       "            background-color:  #ffff66;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row223_col3 {\n",
       "            background-color:  #aad466;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row223_col4 {\n",
       "            background-color:  #fefe66;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row223_col5 {\n",
       "            background-color:  #abd566;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row224_col1 {\n",
       "            background-color:  #389c66;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row224_col2 {\n",
       "            background-color:  #ffff66;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row224_col3 {\n",
       "            background-color:  #aad466;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row224_col4 {\n",
       "            background-color:  #ffff66;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row224_col5 {\n",
       "            background-color:  #afd766;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row225_col1 {\n",
       "            background-color:  #59ac66;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row225_col2 {\n",
       "            background-color:  #ffff66;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row225_col3 {\n",
       "            background-color:  #ffff66;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row225_col4 {\n",
       "            background-color:  #ffff66;\n",
       "            color:  #000000;\n",
       "        }    #T_6c7a4594_c36e_11ea_9162_3024323c1625row225_col5 {\n",
       "            background-color:  #bdde66;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Feature</th>        <th class=\"col_heading level0 col1\" >rfe_importance</th>        <th class=\"col_heading level0 col2\" >alg_importance</th>        <th class=\"col_heading level0 col3\" >drop_col_importance</th>        <th class=\"col_heading level0 col4\" >permutation_importance</th>        <th class=\"col_heading level0 col5\" >mean_feature_importance</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row0_col0\" class=\"data row0 col0\" >LotFrontage</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row0_col1\" class=\"data row0 col1\" >0.92</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row0_col2\" class=\"data row0 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row0_col3\" class=\"data row0 col3\" >-0.0002</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row0_col4\" class=\"data row0 col4\" >0.00206073</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row0_col5\" class=\"data row0 col5\" >0.230465</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row1_col0\" class=\"data row1 col0\" >LotArea</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row1_col1\" class=\"data row1 col1\" >0.97</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row1_col2\" class=\"data row1 col2\" >0.01</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row1_col3\" class=\"data row1 col3\" >0.0002</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row1_col4\" class=\"data row1 col4\" >0.00564427</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row1_col5\" class=\"data row1 col5\" >0.246461</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row2_col0\" class=\"data row2 col0\" >Neighborhood</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row2_col1\" class=\"data row2 col1\" >0.79</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row2_col2\" class=\"data row2 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row2_col3\" class=\"data row2 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row2_col4\" class=\"data row2 col4\" >0.000252279</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row2_col5\" class=\"data row2 col5\" >0.197563</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row3_col0\" class=\"data row3 col0\" >OverallQual</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row3_col1\" class=\"data row3 col1\" >0.98</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row3_col2\" class=\"data row3 col2\" >0.04</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row3_col3\" class=\"data row3 col3\" >0.0004</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row3_col4\" class=\"data row3 col4\" >0.0124263</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row3_col5\" class=\"data row3 col5\" >0.258207</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row4_col0\" class=\"data row4 col0\" >OverallCond</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row4_col1\" class=\"data row4 col1\" >0.94</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row4_col2\" class=\"data row4 col2\" >0.01</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row4_col3\" class=\"data row4 col3\" >0.0004</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row4_col4\" class=\"data row4 col4\" >0.00410234</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row4_col5\" class=\"data row4 col5\" >0.238626</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row5_col0\" class=\"data row5 col0\" >YearBuilt</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row5_col1\" class=\"data row5 col1\" >0.92</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row5_col2\" class=\"data row5 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row5_col3\" class=\"data row5 col3\" >-0.0002</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row5_col4\" class=\"data row5 col4\" >0.00253793</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row5_col5\" class=\"data row5 col5\" >0.230584</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row6_col0\" class=\"data row6 col0\" >YearRemodAdd</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row6_col1\" class=\"data row6 col1\" >0.93</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row6_col2\" class=\"data row6 col2\" >0.01</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row6_col3\" class=\"data row6 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row6_col4\" class=\"data row6 col4\" >0.00377531</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row6_col5\" class=\"data row6 col5\" >0.235969</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row7_col0\" class=\"data row7 col0\" >MasVnrArea</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row7_col1\" class=\"data row7 col1\" >0.84</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row7_col2\" class=\"data row7 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row7_col3\" class=\"data row7 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row7_col4\" class=\"data row7 col4\" >0.00061795</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row7_col5\" class=\"data row7 col5\" >0.210129</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row8_col0\" class=\"data row8 col0\" >ExterCond</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row8_col1\" class=\"data row8 col1\" >0.83</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row8_col2\" class=\"data row8 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row8_col3\" class=\"data row8 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row8_col4\" class=\"data row8 col4\" >0.000652019</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row8_col5\" class=\"data row8 col5\" >0.207638</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row9_col0\" class=\"data row9 col0\" >BsmtCond</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row9_col1\" class=\"data row9 col1\" >0.72</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row9_col2\" class=\"data row9 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row9_col3\" class=\"data row9 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row9_col4\" class=\"data row9 col4\" >0.000183477</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row9_col5\" class=\"data row9 col5\" >0.180046</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row10_col0\" class=\"data row10 col0\" >BsmtFinType1</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row10_col1\" class=\"data row10 col1\" >0.76</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row10_col2\" class=\"data row10 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row10_col3\" class=\"data row10 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row10_col4\" class=\"data row10 col4\" >0.000320937</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row10_col5\" class=\"data row10 col5\" >0.19008</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row11_col0\" class=\"data row11 col0\" >BsmtFinSF1</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row11_col1\" class=\"data row11 col1\" >0.94</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row11_col2\" class=\"data row11 col2\" >0.01</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row11_col3\" class=\"data row11 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row11_col4\" class=\"data row11 col4\" >0.00418135</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row11_col5\" class=\"data row11 col5\" >0.23857</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row12_col0\" class=\"data row12 col0\" >BsmtFinType2</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row12_col1\" class=\"data row12 col1\" >0.65</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row12_col2\" class=\"data row12 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row12_col3\" class=\"data row12 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row12_col4\" class=\"data row12 col4\" >0.00018613</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row12_col5\" class=\"data row12 col5\" >0.162572</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row13_col0\" class=\"data row13 col0\" >BsmtFinSF2</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row13_col1\" class=\"data row13 col1\" >0.77</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row13_col2\" class=\"data row13 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row13_col3\" class=\"data row13 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row13_col4\" class=\"data row13 col4\" >0.000201373</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row13_col5\" class=\"data row13 col5\" >0.19255</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row14_col0\" class=\"data row14 col0\" >BsmtUnfSF</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row14_col1\" class=\"data row14 col1\" >0.95</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row14_col2\" class=\"data row14 col2\" >0.01</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row14_col3\" class=\"data row14 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row14_col4\" class=\"data row14 col4\" >0.00297106</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row14_col5\" class=\"data row14 col5\" >0.240718</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row15_col0\" class=\"data row15 col0\" >TotalBsmtSF</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row15_col1\" class=\"data row15 col1\" >0.99</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row15_col2\" class=\"data row15 col2\" >0.03</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row15_col3\" class=\"data row15 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row15_col4\" class=\"data row15 col4\" >0.0206057</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row15_col5\" class=\"data row15 col5\" >0.260176</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row16_col0\" class=\"data row16 col0\" >1stFlrSF</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row16_col1\" class=\"data row16 col1\" >0.96</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row16_col2\" class=\"data row16 col2\" >0.01</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row16_col3\" class=\"data row16 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row16_col4\" class=\"data row16 col4\" >0.00640665</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row16_col5\" class=\"data row16 col5\" >0.244102</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row17_col0\" class=\"data row17 col0\" >2ndFlrSF</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row17_col1\" class=\"data row17 col1\" >0.88</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row17_col2\" class=\"data row17 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row17_col3\" class=\"data row17 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row17_col4\" class=\"data row17 col4\" >0.000733522</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row17_col5\" class=\"data row17 col5\" >0.220183</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row18_col0\" class=\"data row18 col0\" >LowQualFinSF</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row18_col1\" class=\"data row18 col1\" >0.3</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row18_col2\" class=\"data row18 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row18_col3\" class=\"data row18 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row18_col4\" class=\"data row18 col4\" >1.8706e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row18_col5\" class=\"data row18 col5\" >0.0749797</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row19_col0\" class=\"data row19 col0\" >GrLivArea</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row19_col1\" class=\"data row19 col1\" >0.92</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row19_col2\" class=\"data row19 col2\" >0.01</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row19_col3\" class=\"data row19 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row19_col4\" class=\"data row19 col4\" >0.00257277</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row19_col5\" class=\"data row19 col5\" >0.233118</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row20_col0\" class=\"data row20 col0\" >BsmtFullBath</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row20_col1\" class=\"data row20 col1\" >0.67</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row20_col2\" class=\"data row20 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row20_col3\" class=\"data row20 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row20_col4\" class=\"data row20 col4\" >0.000188868</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row20_col5\" class=\"data row20 col5\" >0.167547</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row21_col0\" class=\"data row21 col0\" >BsmtHalfBath</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row21_col1\" class=\"data row21 col1\" >0.47</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row21_col2\" class=\"data row21 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row21_col3\" class=\"data row21 col3\" >-0.0002</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row21_col4\" class=\"data row21 col4\" >4.93803e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row21_col5\" class=\"data row21 col5\" >0.117462</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row22_col0\" class=\"data row22 col0\" >FullBath</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row22_col1\" class=\"data row22 col1\" >0.73</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row22_col2\" class=\"data row22 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row22_col3\" class=\"data row22 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row22_col4\" class=\"data row22 col4\" >0.00017716</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row22_col5\" class=\"data row22 col5\" >0.182519</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row23_col0\" class=\"data row23 col0\" >HalfBath</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row23_col1\" class=\"data row23 col1\" >0.71</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row23_col2\" class=\"data row23 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row23_col3\" class=\"data row23 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row23_col4\" class=\"data row23 col4\" >0.000199511</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row23_col5\" class=\"data row23 col5\" >0.177525</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row24_col0\" class=\"data row24 col0\" >BedroomAbvGr</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row24_col1\" class=\"data row24 col1\" >0.83</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row24_col2\" class=\"data row24 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row24_col3\" class=\"data row24 col3\" >-0.0002</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row24_col4\" class=\"data row24 col4\" >0.000676715</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row24_col5\" class=\"data row24 col5\" >0.207619</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row25_col0\" class=\"data row25 col0\" >KitchenAbvGr</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row25_col1\" class=\"data row25 col1\" >0.74</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row25_col2\" class=\"data row25 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row25_col3\" class=\"data row25 col3\" >-0.0002</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row25_col4\" class=\"data row25 col4\" >0.000251297</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row25_col5\" class=\"data row25 col5\" >0.185013</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row26_col0\" class=\"data row26 col0\" >TotRmsAbvGrd</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row26_col1\" class=\"data row26 col1\" >0.87</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row26_col2\" class=\"data row26 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row26_col3\" class=\"data row26 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row26_col4\" class=\"data row26 col4\" >0.000754185</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row26_col5\" class=\"data row26 col5\" >0.217664</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row27_col0\" class=\"data row27 col0\" >Fireplaces</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row27_col1\" class=\"data row27 col1\" >0.81</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row27_col2\" class=\"data row27 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row27_col3\" class=\"data row27 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row27_col4\" class=\"data row27 col4\" >0.000486768</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row27_col5\" class=\"data row27 col5\" >0.202622</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row28_col0\" class=\"data row28 col0\" >FireplaceQu</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row28_col1\" class=\"data row28 col1\" >0.88</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row28_col2\" class=\"data row28 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row28_col3\" class=\"data row28 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row28_col4\" class=\"data row28 col4\" >0.00106407</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row28_col5\" class=\"data row28 col5\" >0.220266</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row29_col0\" class=\"data row29 col0\" >GarageYrBlt</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row29_col1\" class=\"data row29 col1\" >0.89</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row29_col2\" class=\"data row29 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row29_col3\" class=\"data row29 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row29_col4\" class=\"data row29 col4\" >0.00173529</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row29_col5\" class=\"data row29 col5\" >0.222959</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row30_col0\" class=\"data row30 col0\" >GarageCars</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row30_col1\" class=\"data row30 col1\" >0.93</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row30_col2\" class=\"data row30 col2\" >0.01</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row30_col3\" class=\"data row30 col3\" >0.0002</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row30_col4\" class=\"data row30 col4\" >0.00525041</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row30_col5\" class=\"data row30 col5\" >0.236363</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row31_col0\" class=\"data row31 col0\" >GarageArea</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row31_col1\" class=\"data row31 col1\" >0.91</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row31_col2\" class=\"data row31 col2\" >0.01</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row31_col3\" class=\"data row31 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row31_col4\" class=\"data row31 col4\" >0.00259859</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row31_col5\" class=\"data row31 col5\" >0.23065</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row32_col0\" class=\"data row32 col0\" >GarageCond</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row32_col1\" class=\"data row32 col1\" >0.4</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row32_col2\" class=\"data row32 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row32_col3\" class=\"data row32 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row32_col4\" class=\"data row32 col4\" >2.78445e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row32_col5\" class=\"data row32 col5\" >0.100007</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row33_col0\" class=\"data row33 col0\" >WoodDeckSF</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row33_col1\" class=\"data row33 col1\" >0.85</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row33_col2\" class=\"data row33 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row33_col3\" class=\"data row33 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row33_col4\" class=\"data row33 col4\" >0.000939519</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row33_col5\" class=\"data row33 col5\" >0.21271</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row34_col0\" class=\"data row34 col0\" >OpenPorchSF</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row34_col1\" class=\"data row34 col1\" >0.9</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row34_col2\" class=\"data row34 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row34_col3\" class=\"data row34 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row34_col4\" class=\"data row34 col4\" >0.00180985</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row34_col5\" class=\"data row34 col5\" >0.225427</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row35_col0\" class=\"data row35 col0\" >EnclosedPorch</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row35_col1\" class=\"data row35 col1\" >0.85</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row35_col2\" class=\"data row35 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row35_col3\" class=\"data row35 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row35_col4\" class=\"data row35 col4\" >0.00120948</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row35_col5\" class=\"data row35 col5\" >0.212777</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row36_col0\" class=\"data row36 col0\" >3SsnPorch</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row36_col1\" class=\"data row36 col1\" >0.42</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row36_col2\" class=\"data row36 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row36_col3\" class=\"data row36 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row36_col4\" class=\"data row36 col4\" >6.65237e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row36_col5\" class=\"data row36 col5\" >0.105017</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row37_col0\" class=\"data row37 col0\" >ScreenPorch</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row37_col1\" class=\"data row37 col1\" >0.68</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row37_col2\" class=\"data row37 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row37_col3\" class=\"data row37 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row37_col4\" class=\"data row37 col4\" >0.000234658</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row37_col5\" class=\"data row37 col5\" >0.170059</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row38_col0\" class=\"data row38 col0\" >PoolArea</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row38_col1\" class=\"data row38 col1\" >0.19</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row38_col2\" class=\"data row38 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row38_col3\" class=\"data row38 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row38_col4\" class=\"data row38 col4\" >5.31279e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row38_col5\" class=\"data row38 col5\" >0.0475013</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row39_col0\" class=\"data row39 col0\" >MiscVal</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row39_col1\" class=\"data row39 col1\" >0.48</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row39_col2\" class=\"data row39 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row39_col3\" class=\"data row39 col3\" >-0.0002</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row39_col4\" class=\"data row39 col4\" >3.03415e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row39_col5\" class=\"data row39 col5\" >0.119958</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row40_col0\" class=\"data row40 col0\" >TotalSF</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row40_col1\" class=\"data row40 col1\" >0.97</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row40_col2\" class=\"data row40 col2\" >0.02</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row40_col3\" class=\"data row40 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row40_col4\" class=\"data row40 col4\" >0.0129972</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row40_col5\" class=\"data row40 col5\" >0.250724</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row41_col0\" class=\"data row41 col0\" >TotalBathrooms</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row41_col1\" class=\"data row41 col1\" >0.87</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row41_col2\" class=\"data row41 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row41_col3\" class=\"data row41 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row41_col4\" class=\"data row41 col4\" >0.0011782</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row41_col5\" class=\"data row41 col5\" >0.21782</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row42_col0\" class=\"data row42 col0\" >TotalPorchSF</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row42_col1\" class=\"data row42 col1\" >0.9</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row42_col2\" class=\"data row42 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row42_col3\" class=\"data row42 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row42_col4\" class=\"data row42 col4\" >0.00216019</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row42_col5\" class=\"data row42 col5\" >0.22554</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row43_col0\" class=\"data row43 col0\" >YearBlRm</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row43_col1\" class=\"data row43 col1\" >0.98</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row43_col2\" class=\"data row43 col2\" >0.01</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row43_col3\" class=\"data row43 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row43_col4\" class=\"data row43 col4\" >0.00665126</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row43_col5\" class=\"data row43 col5\" >0.249188</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row44_col0\" class=\"data row44 col0\" >TotalExtQual</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row44_col1\" class=\"data row44 col1\" >0.89</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row44_col2\" class=\"data row44 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row44_col3\" class=\"data row44 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row44_col4\" class=\"data row44 col4\" >0.00124045</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row44_col5\" class=\"data row44 col5\" >0.222785</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row45_col0\" class=\"data row45 col0\" >TotalBsmQual</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row45_col1\" class=\"data row45 col1\" >0.84</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row45_col2\" class=\"data row45 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row45_col3\" class=\"data row45 col3\" >-0.0002</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row45_col4\" class=\"data row45 col4\" >0.000551423</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row45_col5\" class=\"data row45 col5\" >0.210088</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row46_col0\" class=\"data row46 col0\" >TotalGrgQual</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row46_col1\" class=\"data row46 col1\" >0.56</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row46_col2\" class=\"data row46 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row46_col3\" class=\"data row46 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row46_col4\" class=\"data row46 col4\" >0.000125245</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row46_col5\" class=\"data row46 col5\" >0.140031</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row47_col0\" class=\"data row47 col0\" >TotalQual</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row47_col1\" class=\"data row47 col1\" >0.96</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row47_col2\" class=\"data row47 col2\" >0.01</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row47_col3\" class=\"data row47 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row47_col4\" class=\"data row47 col4\" >0.0112324</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row47_col5\" class=\"data row47 col5\" >0.245308</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row48_col0\" class=\"data row48 col0\" >QualGr</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row48_col1\" class=\"data row48 col1\" >0.99</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row48_col2\" class=\"data row48 col2\" >0.05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row48_col3\" class=\"data row48 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row48_col4\" class=\"data row48 col4\" >0.0196993</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row48_col5\" class=\"data row48 col5\" >0.2649</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row49_col0\" class=\"data row49 col0\" >QualBsm</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row49_col1\" class=\"data row49 col1\" >0.91</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row49_col2\" class=\"data row49 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row49_col3\" class=\"data row49 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row49_col4\" class=\"data row49 col4\" >0.00210693</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row49_col5\" class=\"data row49 col5\" >0.228002</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row50_col0\" class=\"data row50 col0\" >QualPorch</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row50_col1\" class=\"data row50 col1\" >0.95</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row50_col2\" class=\"data row50 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row50_col3\" class=\"data row50 col3\" >-0.0002</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row50_col4\" class=\"data row50 col4\" >0.00306544</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row50_col5\" class=\"data row50 col5\" >0.238216</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row51_col0\" class=\"data row51 col0\" >QualExt</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row51_col1\" class=\"data row51 col1\" >0.88</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row51_col2\" class=\"data row51 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row51_col3\" class=\"data row51 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row51_col4\" class=\"data row51 col4\" >0.000768032</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row51_col5\" class=\"data row51 col5\" >0.220192</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row52_col0\" class=\"data row52 col0\" >QualGrg</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row52_col1\" class=\"data row52 col1\" >0.96</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row52_col2\" class=\"data row52 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row52_col3\" class=\"data row52 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row52_col4\" class=\"data row52 col4\" >0.00256925</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row52_col5\" class=\"data row52 col5\" >0.240617</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row53_col0\" class=\"data row53 col0\" >QlLivArea</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row53_col1\" class=\"data row53 col1\" >1</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row53_col2\" class=\"data row53 col2\" >0.11</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row53_col3\" class=\"data row53 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row53_col4\" class=\"data row53 col4\" >0.0754758</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row53_col5\" class=\"data row53 col5\" >0.296394</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row54_col0\" class=\"data row54 col0\" >QualSFNg</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row54_col1\" class=\"data row54 col1\" >1</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row54_col2\" class=\"data row54 col2\" >1</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row54_col3\" class=\"data row54 col3\" >-0.0002</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row54_col4\" class=\"data row54 col4\" >0.492013</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row54_col5\" class=\"data row54 col5\" >0.622953</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row55_col0\" class=\"data row55 col0\" >HasPool</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row55_col1\" class=\"data row55 col1\" >0.12</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row55_col2\" class=\"data row55 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row55_col3\" class=\"data row55 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row55_col4\" class=\"data row55 col4\" >5.24357e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row55_col5\" class=\"data row55 col5\" >0.0299763</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row56\" class=\"row_heading level0 row56\" >56</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row56_col0\" class=\"data row56 col0\" >Has2ndFloor</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row56_col1\" class=\"data row56 col1\" >0.39</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row56_col2\" class=\"data row56 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row56_col3\" class=\"data row56 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row56_col4\" class=\"data row56 col4\" >7.08648e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row56_col5\" class=\"data row56 col5\" >0.0974927</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row57\" class=\"row_heading level0 row57\" >57</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row57_col0\" class=\"data row57 col0\" >HasGarage</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row57_col1\" class=\"data row57 col1\" >0.2</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row57_col2\" class=\"data row57 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row57_col3\" class=\"data row57 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row57_col4\" class=\"data row57 col4\" >1.46594e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row57_col5\" class=\"data row57 col5\" >0.0500037</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row58\" class=\"row_heading level0 row58\" >58</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row58_col0\" class=\"data row58 col0\" >HasBsmt</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row58_col1\" class=\"data row58 col1\" >0.34</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row58_col2\" class=\"data row58 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row58_col3\" class=\"data row58 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row58_col4\" class=\"data row58 col4\" >2.21207e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row58_col5\" class=\"data row58 col5\" >0.0850055</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row59\" class=\"row_heading level0 row59\" >59</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row59_col0\" class=\"data row59 col0\" >HasFireplace</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row59_col1\" class=\"data row59 col1\" >0.64</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row59_col2\" class=\"data row59 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row59_col3\" class=\"data row59 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row59_col4\" class=\"data row59 col4\" >0.000283133</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row59_col5\" class=\"data row59 col5\" >0.160046</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row60\" class=\"row_heading level0 row60\" >60</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row60_col0\" class=\"data row60 col0\" >HasPorch</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row60_col1\" class=\"data row60 col1\" >0.49</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row60_col2\" class=\"data row60 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row60_col3\" class=\"data row60 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row60_col4\" class=\"data row60 col4\" >5.87184e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row60_col5\" class=\"data row60 col5\" >0.122515</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row61\" class=\"row_heading level0 row61\" >61</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row61_col0\" class=\"data row61 col0\" >MSSubClass_120</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row61_col1\" class=\"data row61 col1\" >0.28</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row61_col2\" class=\"data row61 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row61_col3\" class=\"data row61 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row61_col4\" class=\"data row61 col4\" >9.06432e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row61_col5\" class=\"data row61 col5\" >0.0700023</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row62\" class=\"row_heading level0 row62\" >62</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row62_col0\" class=\"data row62 col0\" >MSSubClass_150</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row62_col1\" class=\"data row62 col1\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row62_col2\" class=\"data row62 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row62_col3\" class=\"data row62 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row62_col4\" class=\"data row62 col4\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row62_col5\" class=\"data row62 col5\" >2.5e-05</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row63\" class=\"row_heading level0 row63\" >63</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row63_col0\" class=\"data row63 col0\" >MSSubClass_160</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row63_col1\" class=\"data row63 col1\" >0.32</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row63_col2\" class=\"data row63 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row63_col3\" class=\"data row63 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row63_col4\" class=\"data row63 col4\" >2.28661e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row63_col5\" class=\"data row63 col5\" >0.0800057</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row64\" class=\"row_heading level0 row64\" >64</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row64_col0\" class=\"data row64 col0\" >MSSubClass_180</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row64_col1\" class=\"data row64 col1\" >0.02</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row64_col2\" class=\"data row64 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row64_col3\" class=\"data row64 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row64_col4\" class=\"data row64 col4\" >-5.10419e-07</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row64_col5\" class=\"data row64 col5\" >0.00499987</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row65\" class=\"row_heading level0 row65\" >65</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row65_col0\" class=\"data row65 col0\" >MSSubClass_190</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row65_col1\" class=\"data row65 col1\" >0.28</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row65_col2\" class=\"data row65 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row65_col3\" class=\"data row65 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row65_col4\" class=\"data row65 col4\" >1.58435e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row65_col5\" class=\"data row65 col5\" >0.069979</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row66\" class=\"row_heading level0 row66\" >66</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row66_col0\" class=\"data row66 col0\" >MSSubClass_20</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row66_col1\" class=\"data row66 col1\" >0.75</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row66_col2\" class=\"data row66 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row66_col3\" class=\"data row66 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row66_col4\" class=\"data row66 col4\" >0.000228165</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row66_col5\" class=\"data row66 col5\" >0.187557</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row67\" class=\"row_heading level0 row67\" >67</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row67_col0\" class=\"data row67 col0\" >MSSubClass_30</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row67_col1\" class=\"data row67 col1\" >0.58</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row67_col2\" class=\"data row67 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row67_col3\" class=\"data row67 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row67_col4\" class=\"data row67 col4\" >9.56003e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row67_col5\" class=\"data row67 col5\" >0.145024</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row68\" class=\"row_heading level0 row68\" >68</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row68_col0\" class=\"data row68 col0\" >MSSubClass_40</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row68_col1\" class=\"data row68 col1\" >0.05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row68_col2\" class=\"data row68 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row68_col3\" class=\"data row68 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row68_col4\" class=\"data row68 col4\" >2.58312e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row68_col5\" class=\"data row68 col5\" >0.0124756</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row69\" class=\"row_heading level0 row69\" >69</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row69_col0\" class=\"data row69 col0\" >MSSubClass_45</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row69_col1\" class=\"data row69 col1\" >0.13</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row69_col2\" class=\"data row69 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row69_col3\" class=\"data row69 col3\" >-0.0002</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row69_col4\" class=\"data row69 col4\" >4.88613e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row69_col5\" class=\"data row69 col5\" >0.0324512</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row70\" class=\"row_heading level0 row70\" >70</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row70_col0\" class=\"data row70 col0\" >MSSubClass_50</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row70_col1\" class=\"data row70 col1\" >0.57</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row70_col2\" class=\"data row70 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row70_col3\" class=\"data row70 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row70_col4\" class=\"data row70 col4\" >8.10266e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row70_col5\" class=\"data row70 col5\" >0.14252</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row71\" class=\"row_heading level0 row71\" >71</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row71_col0\" class=\"data row71 col0\" >MSSubClass_60</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row71_col1\" class=\"data row71 col1\" >0.38</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row71_col2\" class=\"data row71 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row71_col3\" class=\"data row71 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row71_col4\" class=\"data row71 col4\" >5.59396e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row71_col5\" class=\"data row71 col5\" >0.094989</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row72\" class=\"row_heading level0 row72\" >72</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row72_col0\" class=\"data row72 col0\" >MSSubClass_70</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row72_col1\" class=\"data row72 col1\" >0.36</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row72_col2\" class=\"data row72 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row72_col3\" class=\"data row72 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row72_col4\" class=\"data row72 col4\" >2.99191e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row72_col5\" class=\"data row72 col5\" >0.0900325</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row73\" class=\"row_heading level0 row73\" >73</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row73_col0\" class=\"data row73 col0\" >MSSubClass_75</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row73_col1\" class=\"data row73 col1\" >0.18</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row73_col2\" class=\"data row73 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row73_col3\" class=\"data row73 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row73_col4\" class=\"data row73 col4\" >2.04403e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row73_col5\" class=\"data row73 col5\" >0.0449801</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row74\" class=\"row_heading level0 row74\" >74</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row74_col0\" class=\"data row74 col0\" >MSSubClass_80</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row74_col1\" class=\"data row74 col1\" >0.29</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row74_col2\" class=\"data row74 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row74_col3\" class=\"data row74 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row74_col4\" class=\"data row74 col4\" >2.65901e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row74_col5\" class=\"data row74 col5\" >0.0724816</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row75\" class=\"row_heading level0 row75\" >75</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row75_col0\" class=\"data row75 col0\" >MSSubClass_85</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row75_col1\" class=\"data row75 col1\" >0.11</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row75_col2\" class=\"data row75 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row75_col3\" class=\"data row75 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row75_col4\" class=\"data row75 col4\" >5.11753e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row75_col5\" class=\"data row75 col5\" >0.0275013</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row76\" class=\"row_heading level0 row76\" >76</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row76_col0\" class=\"data row76 col0\" >MSSubClass_90</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row76_col1\" class=\"data row76 col1\" >0.31</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row76_col2\" class=\"data row76 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row76_col3\" class=\"data row76 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row76_col4\" class=\"data row76 col4\" >4.55745e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row76_col5\" class=\"data row76 col5\" >0.0774864</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row77\" class=\"row_heading level0 row77\" >77</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row77_col0\" class=\"data row77 col0\" >MSZoning_C (all)</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row77_col1\" class=\"data row77 col1\" >0.84</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row77_col2\" class=\"data row77 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row77_col3\" class=\"data row77 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row77_col4\" class=\"data row77 col4\" >0.000752375</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row77_col5\" class=\"data row77 col5\" >0.210188</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row78\" class=\"row_heading level0 row78\" >78</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row78_col0\" class=\"data row78 col0\" >MSZoning_FV</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row78_col1\" class=\"data row78 col1\" >0.24</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row78_col2\" class=\"data row78 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row78_col3\" class=\"data row78 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row78_col4\" class=\"data row78 col4\" >8.2119e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row78_col5\" class=\"data row78 col5\" >0.0600021</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row79\" class=\"row_heading level0 row79\" >79</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row79_col0\" class=\"data row79 col0\" >MSZoning_RH</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row79_col1\" class=\"data row79 col1\" >0.33</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row79_col2\" class=\"data row79 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row79_col3\" class=\"data row79 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row79_col4\" class=\"data row79 col4\" >2.84857e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row79_col5\" class=\"data row79 col5\" >0.0825071</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row80\" class=\"row_heading level0 row80\" >80</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row80_col0\" class=\"data row80 col0\" >MSZoning_RL</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row80_col1\" class=\"data row80 col1\" >0.62</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row80_col2\" class=\"data row80 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row80_col3\" class=\"data row80 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row80_col4\" class=\"data row80 col4\" >0.00012937</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row80_col5\" class=\"data row80 col5\" >0.155007</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row81\" class=\"row_heading level0 row81\" >81</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row81_col0\" class=\"data row81 col0\" >MSZoning_RM</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row81_col1\" class=\"data row81 col1\" >0.69</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row81_col2\" class=\"data row81 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row81_col3\" class=\"data row81 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row81_col4\" class=\"data row81 col4\" >0.000283862</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row81_col5\" class=\"data row81 col5\" >0.172596</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row82\" class=\"row_heading level0 row82\" >82</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row82_col0\" class=\"data row82 col0\" >Street_Grvl</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row82_col1\" class=\"data row82 col1\" >0.06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row82_col2\" class=\"data row82 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row82_col3\" class=\"data row82 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row82_col4\" class=\"data row82 col4\" >7.56644e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row82_col5\" class=\"data row82 col5\" >0.0150019</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row83\" class=\"row_heading level0 row83\" >83</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row83_col0\" class=\"data row83 col0\" >Street_Pave</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row83_col1\" class=\"data row83 col1\" >0.12</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row83_col2\" class=\"data row83 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row83_col3\" class=\"data row83 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row83_col4\" class=\"data row83 col4\" >4.9792e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row83_col5\" class=\"data row83 col5\" >0.0300012</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row84\" class=\"row_heading level0 row84\" >84</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row84_col0\" class=\"data row84 col0\" >Alley_Grvl</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row84_col1\" class=\"data row84 col1\" >0.61</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row84_col2\" class=\"data row84 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row84_col3\" class=\"data row84 col3\" >-0.0002</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row84_col4\" class=\"data row84 col4\" >0.000149838</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row84_col5\" class=\"data row84 col5\" >0.152487</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row85\" class=\"row_heading level0 row85\" >85</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row85_col0\" class=\"data row85 col0\" >Alley_None</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row85_col1\" class=\"data row85 col1\" >0.5</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row85_col2\" class=\"data row85 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row85_col3\" class=\"data row85 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row85_col4\" class=\"data row85 col4\" >6.09793e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row85_col5\" class=\"data row85 col5\" >0.125015</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row86\" class=\"row_heading level0 row86\" >86</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row86_col0\" class=\"data row86 col0\" >Alley_Pave</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row86_col1\" class=\"data row86 col1\" >0.43</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row86_col2\" class=\"data row86 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row86_col3\" class=\"data row86 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row86_col4\" class=\"data row86 col4\" >0.000143408</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row86_col5\" class=\"data row86 col5\" >0.107536</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row87\" class=\"row_heading level0 row87\" >87</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row87_col0\" class=\"data row87 col0\" >LotShape_IR1</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row87_col1\" class=\"data row87 col1\" >0.67</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row87_col2\" class=\"data row87 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row87_col3\" class=\"data row87 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row87_col4\" class=\"data row87 col4\" >0.000183968</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row87_col5\" class=\"data row87 col5\" >0.167546</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row88\" class=\"row_heading level0 row88\" >88</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row88_col0\" class=\"data row88 col0\" >LotShape_IR2</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row88_col1\" class=\"data row88 col1\" >0.25</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row88_col2\" class=\"data row88 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row88_col3\" class=\"data row88 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row88_col4\" class=\"data row88 col4\" >1.67881e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row88_col5\" class=\"data row88 col5\" >0.0625042</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row89\" class=\"row_heading level0 row89\" >89</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row89_col0\" class=\"data row89 col0\" >LotShape_IR3</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row89_col1\" class=\"data row89 col1\" >0.12</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row89_col2\" class=\"data row89 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row89_col3\" class=\"data row89 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row89_col4\" class=\"data row89 col4\" >2.42229e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row89_col5\" class=\"data row89 col5\" >0.0299756</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row90\" class=\"row_heading level0 row90\" >90</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row90_col0\" class=\"data row90 col0\" >LotShape_Reg</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row90_col1\" class=\"data row90 col1\" >0.76</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row90_col2\" class=\"data row90 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row90_col3\" class=\"data row90 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row90_col4\" class=\"data row90 col4\" >0.000223082</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row90_col5\" class=\"data row90 col5\" >0.190056</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row91\" class=\"row_heading level0 row91\" >91</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row91_col0\" class=\"data row91 col0\" >LandContour_Bnk</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row91_col1\" class=\"data row91 col1\" >0.43</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row91_col2\" class=\"data row91 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row91_col3\" class=\"data row91 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row91_col4\" class=\"data row91 col4\" >6.1897e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row91_col5\" class=\"data row91 col5\" >0.107515</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row92\" class=\"row_heading level0 row92\" >92</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row92_col0\" class=\"data row92 col0\" >LandContour_HLS</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row92_col1\" class=\"data row92 col1\" >0.54</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row92_col2\" class=\"data row92 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row92_col3\" class=\"data row92 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row92_col4\" class=\"data row92 col4\" >0.0001024</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row92_col5\" class=\"data row92 col5\" >0.135026</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row93\" class=\"row_heading level0 row93\" >93</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row93_col0\" class=\"data row93 col0\" >LandContour_Low</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row93_col1\" class=\"data row93 col1\" >0.38</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row93_col2\" class=\"data row93 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row93_col3\" class=\"data row93 col3\" >-0.0002</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row93_col4\" class=\"data row93 col4\" >2.82889e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row93_col5\" class=\"data row93 col5\" >0.0949571</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row94\" class=\"row_heading level0 row94\" >94</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row94_col0\" class=\"data row94 col0\" >LandContour_Lvl</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row94_col1\" class=\"data row94 col1\" >0.72</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row94_col2\" class=\"data row94 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row94_col3\" class=\"data row94 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row94_col4\" class=\"data row94 col4\" >0.000189579</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row94_col5\" class=\"data row94 col5\" >0.180072</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row95\" class=\"row_heading level0 row95\" >95</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row95_col0\" class=\"data row95 col0\" >LotConfig_Corner</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row95_col1\" class=\"data row95 col1\" >0.6</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row95_col2\" class=\"data row95 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row95_col3\" class=\"data row95 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row95_col4\" class=\"data row95 col4\" >0.000120462</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row95_col5\" class=\"data row95 col5\" >0.15003</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row96\" class=\"row_heading level0 row96\" >96</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row96_col0\" class=\"data row96 col0\" >LotConfig_CulDSac</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row96_col1\" class=\"data row96 col1\" >0.44</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row96_col2\" class=\"data row96 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row96_col3\" class=\"data row96 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row96_col4\" class=\"data row96 col4\" >6.22331e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row96_col5\" class=\"data row96 col5\" >0.110016</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row97\" class=\"row_heading level0 row97\" >97</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row97_col0\" class=\"data row97 col0\" >LotConfig_FR2</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row97_col1\" class=\"data row97 col1\" >0.41</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row97_col2\" class=\"data row97 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row97_col3\" class=\"data row97 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row97_col4\" class=\"data row97 col4\" >6.40335e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row97_col5\" class=\"data row97 col5\" >0.102491</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row98\" class=\"row_heading level0 row98\" >98</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row98_col0\" class=\"data row98 col0\" >LotConfig_FR3</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row98_col1\" class=\"data row98 col1\" >0.15</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row98_col2\" class=\"data row98 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row98_col3\" class=\"data row98 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row98_col4\" class=\"data row98 col4\" >1.06919e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row98_col5\" class=\"data row98 col5\" >0.0375027</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row99\" class=\"row_heading level0 row99\" >99</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row99_col0\" class=\"data row99 col0\" >LotConfig_Inside</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row99_col1\" class=\"data row99 col1\" >0.7</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row99_col2\" class=\"data row99 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row99_col3\" class=\"data row99 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row99_col4\" class=\"data row99 col4\" >0.000275387</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row99_col5\" class=\"data row99 col5\" >0.175069</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row100\" class=\"row_heading level0 row100\" >100</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row100_col0\" class=\"data row100 col0\" >LandSlope_Gtl</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row100_col1\" class=\"data row100 col1\" >0.57</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row100_col2\" class=\"data row100 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row100_col3\" class=\"data row100 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row100_col4\" class=\"data row100 col4\" >0.000140898</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row100_col5\" class=\"data row100 col5\" >0.142535</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row101\" class=\"row_heading level0 row101\" >101</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row101_col0\" class=\"data row101 col0\" >LandSlope_Mod</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row101_col1\" class=\"data row101 col1\" >0.45</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row101_col2\" class=\"data row101 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row101_col3\" class=\"data row101 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row101_col4\" class=\"data row101 col4\" >4.70063e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row101_col5\" class=\"data row101 col5\" >0.112487</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row102\" class=\"row_heading level0 row102\" >102</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row102_col0\" class=\"data row102 col0\" >LandSlope_Sev</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row102_col1\" class=\"data row102 col1\" >0.14</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row102_col2\" class=\"data row102 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row102_col3\" class=\"data row102 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row102_col4\" class=\"data row102 col4\" >2.42422e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row102_col5\" class=\"data row102 col5\" >0.0350061</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row103\" class=\"row_heading level0 row103\" >103</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row103_col0\" class=\"data row103 col0\" >Condition1_Artery</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row103_col1\" class=\"data row103 col1\" >0.66</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row103_col2\" class=\"data row103 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row103_col3\" class=\"data row103 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row103_col4\" class=\"data row103 col4\" >0.00015038</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row103_col5\" class=\"data row103 col5\" >0.165063</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row104\" class=\"row_heading level0 row104\" >104</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row104_col0\" class=\"data row104 col0\" >Condition1_Feedr</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row104_col1\" class=\"data row104 col1\" >0.52</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row104_col2\" class=\"data row104 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row104_col3\" class=\"data row104 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row104_col4\" class=\"data row104 col4\" >0.000101273</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row104_col5\" class=\"data row104 col5\" >0.130025</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row105\" class=\"row_heading level0 row105\" >105</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row105_col0\" class=\"data row105 col0\" >Condition1_Norm</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row105_col1\" class=\"data row105 col1\" >0.71</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row105_col2\" class=\"data row105 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row105_col3\" class=\"data row105 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row105_col4\" class=\"data row105 col4\" >0.000140553</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row105_col5\" class=\"data row105 col5\" >0.17751</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row106\" class=\"row_heading level0 row106\" >106</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row106_col0\" class=\"data row106 col0\" >Condition1_Other</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row106_col1\" class=\"data row106 col1\" >0.06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row106_col2\" class=\"data row106 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row106_col3\" class=\"data row106 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row106_col4\" class=\"data row106 col4\" >8.06264e-07</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row106_col5\" class=\"data row106 col5\" >0.0150002</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row107\" class=\"row_heading level0 row107\" >107</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row107_col0\" class=\"data row107 col0\" >Condition1_PosA</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row107_col1\" class=\"data row107 col1\" >0.07</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row107_col2\" class=\"data row107 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row107_col3\" class=\"data row107 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row107_col4\" class=\"data row107 col4\" >1.42437e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row107_col5\" class=\"data row107 col5\" >0.0175036</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row108\" class=\"row_heading level0 row108\" >108</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row108_col0\" class=\"data row108 col0\" >Condition1_PosN</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row108_col1\" class=\"data row108 col1\" >0.2</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row108_col2\" class=\"data row108 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row108_col3\" class=\"data row108 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row108_col4\" class=\"data row108 col4\" >6.59309e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row108_col5\" class=\"data row108 col5\" >0.0500266</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row109\" class=\"row_heading level0 row109\" >109</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row109_col0\" class=\"data row109 col0\" >Condition1_RRAe</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row109_col1\" class=\"data row109 col1\" >0.16</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row109_col2\" class=\"data row109 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row109_col3\" class=\"data row109 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row109_col4\" class=\"data row109 col4\" >5.13323e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row109_col5\" class=\"data row109 col5\" >0.0400013</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row110\" class=\"row_heading level0 row110\" >110</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row110_col0\" class=\"data row110 col0\" >Condition1_RRAn</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row110_col1\" class=\"data row110 col1\" >0.27</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row110_col2\" class=\"data row110 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row110_col3\" class=\"data row110 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row110_col4\" class=\"data row110 col4\" >1.04351e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row110_col5\" class=\"data row110 col5\" >0.0675026</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row111\" class=\"row_heading level0 row111\" >111</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row111_col0\" class=\"data row111 col0\" >Condition2_Feedr</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row111_col1\" class=\"data row111 col1\" >0.03</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row111_col2\" class=\"data row111 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row111_col3\" class=\"data row111 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row111_col4\" class=\"data row111 col4\" >5.15229e-07</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row111_col5\" class=\"data row111 col5\" >0.00752513</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row112\" class=\"row_heading level0 row112\" >112</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row112_col0\" class=\"data row112 col0\" >Condition2_Norm</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row112_col1\" class=\"data row112 col1\" >0.27</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row112_col2\" class=\"data row112 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row112_col3\" class=\"data row112 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row112_col4\" class=\"data row112 col4\" >1.77479e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row112_col5\" class=\"data row112 col5\" >0.0674794</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row113\" class=\"row_heading level0 row113\" >113</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row113_col0\" class=\"data row113 col0\" >Condition2_Other</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row113_col1\" class=\"data row113 col1\" >0.16</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row113_col2\" class=\"data row113 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row113_col3\" class=\"data row113 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row113_col4\" class=\"data row113 col4\" >8.23445e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row113_col5\" class=\"data row113 col5\" >0.0400021</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row114\" class=\"row_heading level0 row114\" >114</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row114_col0\" class=\"data row114 col0\" >BldgType_1Fam</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row114_col1\" class=\"data row114 col1\" >0.72</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row114_col2\" class=\"data row114 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row114_col3\" class=\"data row114 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row114_col4\" class=\"data row114 col4\" >0.000159022</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row114_col5\" class=\"data row114 col5\" >0.180015</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row115\" class=\"row_heading level0 row115\" >115</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row115_col0\" class=\"data row115 col0\" >BldgType_2fmCon</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row115_col1\" class=\"data row115 col1\" >0.24</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row115_col2\" class=\"data row115 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row115_col3\" class=\"data row115 col3\" >-0.0002</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row115_col4\" class=\"data row115 col4\" >7.9746e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row115_col5\" class=\"data row115 col5\" >0.059952</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row116\" class=\"row_heading level0 row116\" >116</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row116_col0\" class=\"data row116 col0\" >BldgType_Duplex</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row116_col1\" class=\"data row116 col1\" >0.37</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row116_col2\" class=\"data row116 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row116_col3\" class=\"data row116 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row116_col4\" class=\"data row116 col4\" >4.34129e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row116_col5\" class=\"data row116 col5\" >0.0924859</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row117\" class=\"row_heading level0 row117\" >117</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row117_col0\" class=\"data row117 col0\" >BldgType_Twnhs</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row117_col1\" class=\"data row117 col1\" >0.21</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row117_col2\" class=\"data row117 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row117_col3\" class=\"data row117 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row117_col4\" class=\"data row117 col4\" >1.5852e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row117_col5\" class=\"data row117 col5\" >0.052504</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row118\" class=\"row_heading level0 row118\" >118</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row118_col0\" class=\"data row118 col0\" >BldgType_TwnhsE</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row118_col1\" class=\"data row118 col1\" >0.3</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row118_col2\" class=\"data row118 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row118_col3\" class=\"data row118 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row118_col4\" class=\"data row118 col4\" >1.81124e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row118_col5\" class=\"data row118 col5\" >0.0750045</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row119\" class=\"row_heading level0 row119\" >119</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row119_col0\" class=\"data row119 col0\" >HouseStyle_1.5Fin</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row119_col1\" class=\"data row119 col1\" >0.75</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row119_col2\" class=\"data row119 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row119_col3\" class=\"data row119 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row119_col4\" class=\"data row119 col4\" >0.000178989</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row119_col5\" class=\"data row119 col5\" >0.187545</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row120\" class=\"row_heading level0 row120\" >120</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row120_col0\" class=\"data row120 col0\" >HouseStyle_1.5Unf</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row120_col1\" class=\"data row120 col1\" >0.18</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row120_col2\" class=\"data row120 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row120_col3\" class=\"data row120 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row120_col4\" class=\"data row120 col4\" >8.47415e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row120_col5\" class=\"data row120 col5\" >0.0450021</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row121\" class=\"row_heading level0 row121\" >121</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row121_col0\" class=\"data row121 col0\" >HouseStyle_1Story</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row121_col1\" class=\"data row121 col1\" >0.5</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row121_col2\" class=\"data row121 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row121_col3\" class=\"data row121 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row121_col4\" class=\"data row121 col4\" >9.78524e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row121_col5\" class=\"data row121 col5\" >0.124999</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row122\" class=\"row_heading level0 row122\" >122</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row122_col0\" class=\"data row122 col0\" >HouseStyle_2.5Fin</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row122_col1\" class=\"data row122 col1\" >0.1</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row122_col2\" class=\"data row122 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row122_col3\" class=\"data row122 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row122_col4\" class=\"data row122 col4\" >2.55305e-07</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row122_col5\" class=\"data row122 col5\" >0.0249751</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row123\" class=\"row_heading level0 row123\" >123</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row123_col0\" class=\"data row123 col0\" >HouseStyle_2.5Unf</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row123_col1\" class=\"data row123 col1\" >0.11</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row123_col2\" class=\"data row123 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row123_col3\" class=\"data row123 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row123_col4\" class=\"data row123 col4\" >1.926e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row123_col5\" class=\"data row123 col5\" >0.0274755</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row124\" class=\"row_heading level0 row124\" >124</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row124_col0\" class=\"data row124 col0\" >HouseStyle_2Story</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row124_col1\" class=\"data row124 col1\" >0.47</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row124_col2\" class=\"data row124 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row124_col3\" class=\"data row124 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row124_col4\" class=\"data row124 col4\" >7.72253e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row124_col5\" class=\"data row124 col5\" >0.117519</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row125\" class=\"row_heading level0 row125\" >125</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row125_col0\" class=\"data row125 col0\" >HouseStyle_SFoyer</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row125_col1\" class=\"data row125 col1\" >0.19</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row125_col2\" class=\"data row125 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row125_col3\" class=\"data row125 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row125_col4\" class=\"data row125 col4\" >1.46583e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row125_col5\" class=\"data row125 col5\" >0.0475004</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row126\" class=\"row_heading level0 row126\" >126</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row126_col0\" class=\"data row126 col0\" >HouseStyle_SLvl</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row126_col1\" class=\"data row126 col1\" >0.32</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row126_col2\" class=\"data row126 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row126_col3\" class=\"data row126 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row126_col4\" class=\"data row126 col4\" >7.27119e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row126_col5\" class=\"data row126 col5\" >0.0800268</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row127\" class=\"row_heading level0 row127\" >127</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row127_col0\" class=\"data row127 col0\" >RoofStyle_Flat</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row127_col1\" class=\"data row127 col1\" >0.09</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row127_col2\" class=\"data row127 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row127_col3\" class=\"data row127 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row127_col4\" class=\"data row127 col4\" >8.31903e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row127_col5\" class=\"data row127 col5\" >0.0225021</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row128\" class=\"row_heading level0 row128\" >128</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row128_col0\" class=\"data row128 col0\" >RoofStyle_Gable</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row128_col1\" class=\"data row128 col1\" >0.78</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row128_col2\" class=\"data row128 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row128_col3\" class=\"data row128 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row128_col4\" class=\"data row128 col4\" >0.000135025</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row128_col5\" class=\"data row128 col5\" >0.195009</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row129\" class=\"row_heading level0 row129\" >129</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row129_col0\" class=\"data row129 col0\" >RoofStyle_Gambrel</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row129_col1\" class=\"data row129 col1\" >0.51</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row129_col2\" class=\"data row129 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row129_col3\" class=\"data row129 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row129_col4\" class=\"data row129 col4\" >0.000133983</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row129_col5\" class=\"data row129 col5\" >0.127533</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row130\" class=\"row_heading level0 row130\" >130</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row130_col0\" class=\"data row130 col0\" >RoofStyle_Hip</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row130_col1\" class=\"data row130 col1\" >0.64</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row130_col2\" class=\"data row130 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row130_col3\" class=\"data row130 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row130_col4\" class=\"data row130 col4\" >9.6623e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row130_col5\" class=\"data row130 col5\" >0.160024</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row131\" class=\"row_heading level0 row131\" >131</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row131_col0\" class=\"data row131 col0\" >RoofStyle_Mansard</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row131_col1\" class=\"data row131 col1\" >0.04</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row131_col2\" class=\"data row131 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row131_col3\" class=\"data row131 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row131_col4\" class=\"data row131 col4\" >-9.98456e-07</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row131_col5\" class=\"data row131 col5\" >0.00999975</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row132\" class=\"row_heading level0 row132\" >132</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row132_col0\" class=\"data row132 col0\" >RoofStyle_Shed</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row132_col1\" class=\"data row132 col1\" >0.02</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row132_col2\" class=\"data row132 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row132_col3\" class=\"data row132 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row132_col4\" class=\"data row132 col4\" >2.8962e-07</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row132_col5\" class=\"data row132 col5\" >0.00500007</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row133\" class=\"row_heading level0 row133\" >133</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row133_col0\" class=\"data row133 col0\" >RoofMatl_CompShg</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row133_col1\" class=\"data row133 col1\" >0.45</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row133_col2\" class=\"data row133 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row133_col3\" class=\"data row133 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row133_col4\" class=\"data row133 col4\" >4.80061e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row133_col5\" class=\"data row133 col5\" >0.112512</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row134\" class=\"row_heading level0 row134\" >134</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row134_col0\" class=\"data row134 col0\" >RoofMatl_Other</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row134_col1\" class=\"data row134 col1\" >0.14</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row134_col2\" class=\"data row134 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row134_col3\" class=\"data row134 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row134_col4\" class=\"data row134 col4\" >5.06766e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row134_col5\" class=\"data row134 col5\" >0.0349877</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row135\" class=\"row_heading level0 row135\" >135</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row135_col0\" class=\"data row135 col0\" >RoofMatl_Tar&Grv</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row135_col1\" class=\"data row135 col1\" >0.23</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row135_col2\" class=\"data row135 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row135_col3\" class=\"data row135 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row135_col4\" class=\"data row135 col4\" >4.12606e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row135_col5\" class=\"data row135 col5\" >0.0575103</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row136\" class=\"row_heading level0 row136\" >136</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row136_col0\" class=\"data row136 col0\" >Exterior1st_AsbShng</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row136_col1\" class=\"data row136 col1\" >0.13</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row136_col2\" class=\"data row136 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row136_col3\" class=\"data row136 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row136_col4\" class=\"data row136 col4\" >2.44838e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row136_col5\" class=\"data row136 col5\" >0.0325311</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row137\" class=\"row_heading level0 row137\" >137</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row137_col0\" class=\"data row137 col0\" >Exterior1st_BrkFace</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row137_col1\" class=\"data row137 col1\" >0.64</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row137_col2\" class=\"data row137 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row137_col3\" class=\"data row137 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row137_col4\" class=\"data row137 col4\" >0.000172503</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row137_col5\" class=\"data row137 col5\" >0.160043</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row138\" class=\"row_heading level0 row138\" >138</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row138_col0\" class=\"data row138 col0\" >Exterior1st_CemntBd</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row138_col1\" class=\"data row138 col1\" >0.4</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row138_col2\" class=\"data row138 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row138_col3\" class=\"data row138 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row138_col4\" class=\"data row138 col4\" >2.98914e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row138_col5\" class=\"data row138 col5\" >0.100032</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row139\" class=\"row_heading level0 row139\" >139</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row139_col0\" class=\"data row139 col0\" >Exterior1st_HdBoard</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row139_col1\" class=\"data row139 col1\" >0.6</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row139_col2\" class=\"data row139 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row139_col3\" class=\"data row139 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row139_col4\" class=\"data row139 col4\" >5.99967e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row139_col5\" class=\"data row139 col5\" >0.150015</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row140\" class=\"row_heading level0 row140\" >140</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row140_col0\" class=\"data row140 col0\" >Exterior1st_MetalSd</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row140_col1\" class=\"data row140 col1\" >0.7</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row140_col2\" class=\"data row140 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row140_col3\" class=\"data row140 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row140_col4\" class=\"data row140 col4\" >9.05185e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row140_col5\" class=\"data row140 col5\" >0.175023</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row141\" class=\"row_heading level0 row141\" >141</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row141_col0\" class=\"data row141 col0\" >Exterior1st_Other</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row141_col1\" class=\"data row141 col1\" >0.08</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row141_col2\" class=\"data row141 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row141_col3\" class=\"data row141 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row141_col4\" class=\"data row141 col4\" >1.89454e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row141_col5\" class=\"data row141 col5\" >0.0199797</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row142\" class=\"row_heading level0 row142\" >142</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row142_col0\" class=\"data row142 col0\" >Exterior1st_Plywood</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row142_col1\" class=\"data row142 col1\" >0.36</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row142_col2\" class=\"data row142 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row142_col3\" class=\"data row142 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row142_col4\" class=\"data row142 col4\" >6.53299e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row142_col5\" class=\"data row142 col5\" >0.0899913</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row143\" class=\"row_heading level0 row143\" >143</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row143_col0\" class=\"data row143 col0\" >Exterior1st_Stucco</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row143_col1\" class=\"data row143 col1\" >0.25</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row143_col2\" class=\"data row143 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row143_col3\" class=\"data row143 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row143_col4\" class=\"data row143 col4\" >1.1846e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row143_col5\" class=\"data row143 col5\" >0.062503</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row144\" class=\"row_heading level0 row144\" >144</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row144_col0\" class=\"data row144 col0\" >Exterior1st_VinylSd</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row144_col1\" class=\"data row144 col1\" >0.61</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row144_col2\" class=\"data row144 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row144_col3\" class=\"data row144 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row144_col4\" class=\"data row144 col4\" >0.000164714</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row144_col5\" class=\"data row144 col5\" >0.152516</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row145\" class=\"row_heading level0 row145\" >145</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row145_col0\" class=\"data row145 col0\" >Exterior1st_Wd Sdng</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row145_col1\" class=\"data row145 col1\" >0.73</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row145_col2\" class=\"data row145 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row145_col3\" class=\"data row145 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row145_col4\" class=\"data row145 col4\" >0.000299934</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row145_col5\" class=\"data row145 col5\" >0.18255</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row146\" class=\"row_heading level0 row146\" >146</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row146_col0\" class=\"data row146 col0\" >Exterior1st_WdShing</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row146_col1\" class=\"data row146 col1\" >0.32</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row146_col2\" class=\"data row146 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row146_col3\" class=\"data row146 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row146_col4\" class=\"data row146 col4\" >1.09024e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row146_col5\" class=\"data row146 col5\" >0.0800027</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row147\" class=\"row_heading level0 row147\" >147</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row147_col0\" class=\"data row147 col0\" >Exterior2nd_AsbShng</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row147_col1\" class=\"data row147 col1\" >0.28</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row147_col2\" class=\"data row147 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row147_col3\" class=\"data row147 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row147_col4\" class=\"data row147 col4\" >1.38876e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row147_col5\" class=\"data row147 col5\" >0.0700035</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row148\" class=\"row_heading level0 row148\" >148</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row148_col0\" class=\"data row148 col0\" >Exterior2nd_Brk Cmn</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row148_col1\" class=\"data row148 col1\" >0.08</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row148_col2\" class=\"data row148 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row148_col3\" class=\"data row148 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row148_col4\" class=\"data row148 col4\" >1.49342e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row148_col5\" class=\"data row148 col5\" >0.0199787</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row149\" class=\"row_heading level0 row149\" >149</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row149_col0\" class=\"data row149 col0\" >Exterior2nd_BrkFace</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row149_col1\" class=\"data row149 col1\" >0.15</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row149_col2\" class=\"data row149 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row149_col3\" class=\"data row149 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row149_col4\" class=\"data row149 col4\" >0.000119525</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row149_col5\" class=\"data row149 col5\" >0.0375299</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row150\" class=\"row_heading level0 row150\" >150</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row150_col0\" class=\"data row150 col0\" >Exterior2nd_CmentBd</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row150_col1\" class=\"data row150 col1\" >0.33</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row150_col2\" class=\"data row150 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row150_col3\" class=\"data row150 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row150_col4\" class=\"data row150 col4\" >1.96578e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row150_col5\" class=\"data row150 col5\" >0.0824799</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row151\" class=\"row_heading level0 row151\" >151</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row151_col0\" class=\"data row151 col0\" >Exterior2nd_HdBoard</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row151_col1\" class=\"data row151 col1\" >0.52</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row151_col2\" class=\"data row151 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row151_col3\" class=\"data row151 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row151_col4\" class=\"data row151 col4\" >0.000131646</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row151_col5\" class=\"data row151 col5\" >0.130008</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row152\" class=\"row_heading level0 row152\" >152</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row152_col0\" class=\"data row152 col0\" >Exterior2nd_ImStucc</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row152_col1\" class=\"data row152 col1\" >0.07</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row152_col2\" class=\"data row152 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row152_col3\" class=\"data row152 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row152_col4\" class=\"data row152 col4\" >2.11556e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row152_col5\" class=\"data row152 col5\" >0.0174755</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row153\" class=\"row_heading level0 row153\" >153</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row153_col0\" class=\"data row153 col0\" >Exterior2nd_MetalSd</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row153_col1\" class=\"data row153 col1\" >0.59</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row153_col2\" class=\"data row153 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row153_col3\" class=\"data row153 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row153_col4\" class=\"data row153 col4\" >0.000113205</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row153_col5\" class=\"data row153 col5\" >0.147528</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row154\" class=\"row_heading level0 row154\" >154</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row154_col0\" class=\"data row154 col0\" >Exterior2nd_Other</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row154_col1\" class=\"data row154 col1\" >0.16</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row154_col2\" class=\"data row154 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row154_col3\" class=\"data row154 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row154_col4\" class=\"data row154 col4\" >4.07308e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row154_col5\" class=\"data row154 col5\" >0.040026</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row155\" class=\"row_heading level0 row155\" >155</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row155_col0\" class=\"data row155 col0\" >Exterior2nd_Plywood</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row155_col1\" class=\"data row155 col1\" >0.48</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row155_col2\" class=\"data row155 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row155_col3\" class=\"data row155 col3\" >0.0002</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row155_col4\" class=\"data row155 col4\" >8.4078e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row155_col5\" class=\"data row155 col5\" >0.120071</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row156\" class=\"row_heading level0 row156\" >156</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row156_col0\" class=\"data row156 col0\" >Exterior2nd_Stucco</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row156_col1\" class=\"data row156 col1\" >0.55</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row156_col2\" class=\"data row156 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row156_col3\" class=\"data row156 col3\" >0.0002</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row156_col4\" class=\"data row156 col4\" >6.82027e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row156_col5\" class=\"data row156 col5\" >0.137567</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row157\" class=\"row_heading level0 row157\" >157</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row157_col0\" class=\"data row157 col0\" >Exterior2nd_VinylSd</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row157_col1\" class=\"data row157 col1\" >0.77</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row157_col2\" class=\"data row157 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row157_col3\" class=\"data row157 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row157_col4\" class=\"data row157 col4\" >0.000350466</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row157_col5\" class=\"data row157 col5\" >0.192588</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row158\" class=\"row_heading level0 row158\" >158</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row158_col0\" class=\"data row158 col0\" >Exterior2nd_Wd Sdng</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row158_col1\" class=\"data row158 col1\" >0.56</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row158_col2\" class=\"data row158 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row158_col3\" class=\"data row158 col3\" >-0.0002</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row158_col4\" class=\"data row158 col4\" >0.000112296</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row158_col5\" class=\"data row158 col5\" >0.139978</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row159\" class=\"row_heading level0 row159\" >159</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row159_col0\" class=\"data row159 col0\" >Exterior2nd_Wd Shng</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row159_col1\" class=\"data row159 col1\" >0.44</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row159_col2\" class=\"data row159 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row159_col3\" class=\"data row159 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row159_col4\" class=\"data row159 col4\" >4.34343e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row159_col5\" class=\"data row159 col5\" >0.110011</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row160\" class=\"row_heading level0 row160\" >160</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row160_col0\" class=\"data row160 col0\" >MasVnrType_BrkCmn</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row160_col1\" class=\"data row160 col1\" >0.53</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row160_col2\" class=\"data row160 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row160_col3\" class=\"data row160 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row160_col4\" class=\"data row160 col4\" >8.24226e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row160_col5\" class=\"data row160 col5\" >0.132521</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row161\" class=\"row_heading level0 row161\" >161</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row161_col0\" class=\"data row161 col0\" >MasVnrType_BrkFace</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row161_col1\" class=\"data row161 col1\" >0.59</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row161_col2\" class=\"data row161 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row161_col3\" class=\"data row161 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row161_col4\" class=\"data row161 col4\" >0.000200856</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row161_col5\" class=\"data row161 col5\" >0.147525</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row162\" class=\"row_heading level0 row162\" >162</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row162_col0\" class=\"data row162 col0\" >MasVnrType_None</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row162_col1\" class=\"data row162 col1\" >0.42</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row162_col2\" class=\"data row162 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row162_col3\" class=\"data row162 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row162_col4\" class=\"data row162 col4\" >7.55439e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row162_col5\" class=\"data row162 col5\" >0.104994</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row163\" class=\"row_heading level0 row163\" >163</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row163_col0\" class=\"data row163 col0\" >MasVnrType_Stone</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row163_col1\" class=\"data row163 col1\" >0.63</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row163_col2\" class=\"data row163 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row163_col3\" class=\"data row163 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row163_col4\" class=\"data row163 col4\" >0.000129306</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row163_col5\" class=\"data row163 col5\" >0.157532</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row164\" class=\"row_heading level0 row164\" >164</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row164_col0\" class=\"data row164 col0\" >Foundation_BrkTil</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row164_col1\" class=\"data row164 col1\" >0.56</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row164_col2\" class=\"data row164 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row164_col3\" class=\"data row164 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row164_col4\" class=\"data row164 col4\" >7.21503e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row164_col5\" class=\"data row164 col5\" >0.139993</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row165\" class=\"row_heading level0 row165\" >165</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row165_col0\" class=\"data row165 col0\" >Foundation_CBlock</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row165_col1\" class=\"data row165 col1\" >0.53</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row165_col2\" class=\"data row165 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row165_col3\" class=\"data row165 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row165_col4\" class=\"data row165 col4\" >7.57148e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row165_col5\" class=\"data row165 col5\" >0.132519</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row166\" class=\"row_heading level0 row166\" >166</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row166_col0\" class=\"data row166 col0\" >Foundation_PConc</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row166_col1\" class=\"data row166 col1\" >0.66</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row166_col2\" class=\"data row166 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row166_col3\" class=\"data row166 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row166_col4\" class=\"data row166 col4\" >0.000143433</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row166_col5\" class=\"data row166 col5\" >0.165061</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row167\" class=\"row_heading level0 row167\" >167</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row167_col0\" class=\"data row167 col0\" >Foundation_Slab</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row167_col1\" class=\"data row167 col1\" >0.17</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row167_col2\" class=\"data row167 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row167_col3\" class=\"data row167 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row167_col4\" class=\"data row167 col4\" >-3.99836e-07</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row167_col5\" class=\"data row167 col5\" >0.0424999</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row168\" class=\"row_heading level0 row168\" >168</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row168_col0\" class=\"data row168 col0\" >Foundation_Stone</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row168_col1\" class=\"data row168 col1\" >0.04</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row168_col2\" class=\"data row168 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row168_col3\" class=\"data row168 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row168_col4\" class=\"data row168 col4\" >1.00768e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row168_col5\" class=\"data row168 col5\" >0.0100025</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row169\" class=\"row_heading level0 row169\" >169</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row169_col0\" class=\"data row169 col0\" >Foundation_Wood</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row169_col1\" class=\"data row169 col1\" >0.05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row169_col2\" class=\"data row169 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row169_col3\" class=\"data row169 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row169_col4\" class=\"data row169 col4\" >1.3987e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row169_col5\" class=\"data row169 col5\" >0.0124753</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row170\" class=\"row_heading level0 row170\" >170</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row170_col0\" class=\"data row170 col0\" >BsmtExposure_Av</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row170_col1\" class=\"data row170 col1\" >0.48</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row170_col2\" class=\"data row170 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row170_col3\" class=\"data row170 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row170_col4\" class=\"data row170 col4\" >3.4214e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row170_col5\" class=\"data row170 col5\" >0.120009</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row171\" class=\"row_heading level0 row171\" >171</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row171_col0\" class=\"data row171 col0\" >BsmtExposure_Gd</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row171_col1\" class=\"data row171 col1\" >0.79</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row171_col2\" class=\"data row171 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row171_col3\" class=\"data row171 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row171_col4\" class=\"data row171 col4\" >0.000494945</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row171_col5\" class=\"data row171 col5\" >0.197649</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row172\" class=\"row_heading level0 row172\" >172</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row172_col0\" class=\"data row172 col0\" >BsmtExposure_Mn</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row172_col1\" class=\"data row172 col1\" >0.51</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row172_col2\" class=\"data row172 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row172_col3\" class=\"data row172 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row172_col4\" class=\"data row172 col4\" >7.53377e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row172_col5\" class=\"data row172 col5\" >0.127519</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row173\" class=\"row_heading level0 row173\" >173</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row173_col0\" class=\"data row173 col0\" >BsmtExposure_No</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row173_col1\" class=\"data row173 col1\" >0.74</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row173_col2\" class=\"data row173 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row173_col3\" class=\"data row173 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row173_col4\" class=\"data row173 col4\" >0.000353913</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row173_col5\" class=\"data row173 col5\" >0.185088</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row174\" class=\"row_heading level0 row174\" >174</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row174_col0\" class=\"data row174 col0\" >BsmtExposure_None</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row174_col1\" class=\"data row174 col1\" >0.2</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row174_col2\" class=\"data row174 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row174_col3\" class=\"data row174 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row174_col4\" class=\"data row174 col4\" >5.89027e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row174_col5\" class=\"data row174 col5\" >0.0500015</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row175\" class=\"row_heading level0 row175\" >175</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row175_col0\" class=\"data row175 col0\" >Heating_GasA</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row175_col1\" class=\"data row175 col1\" >0.4</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row175_col2\" class=\"data row175 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row175_col3\" class=\"data row175 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row175_col4\" class=\"data row175 col4\" >4.36431e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row175_col5\" class=\"data row175 col5\" >0.100011</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row176\" class=\"row_heading level0 row176\" >176</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row176_col0\" class=\"data row176 col0\" >Heating_GasW</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row176_col1\" class=\"data row176 col1\" >0.22</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row176_col2\" class=\"data row176 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row176_col3\" class=\"data row176 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row176_col4\" class=\"data row176 col4\" >2.96381e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row176_col5\" class=\"data row176 col5\" >0.0550324</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row177\" class=\"row_heading level0 row177\" >177</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row177_col0\" class=\"data row177 col0\" >Heating_Other</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row177_col1\" class=\"data row177 col1\" >0.17</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row177_col2\" class=\"data row177 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row177_col3\" class=\"data row177 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row177_col4\" class=\"data row177 col4\" >1.45606e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row177_col5\" class=\"data row177 col5\" >0.0425036</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row178\" class=\"row_heading level0 row178\" >178</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row178_col0\" class=\"data row178 col0\" >CentralAir_N</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row178_col1\" class=\"data row178 col1\" >0.6</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row178_col2\" class=\"data row178 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row178_col3\" class=\"data row178 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row178_col4\" class=\"data row178 col4\" >0.000538145</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row178_col5\" class=\"data row178 col5\" >0.15011</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row179\" class=\"row_heading level0 row179\" >179</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row179_col0\" class=\"data row179 col0\" >CentralAir_Y</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row179_col1\" class=\"data row179 col1\" >0.86</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row179_col2\" class=\"data row179 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row179_col3\" class=\"data row179 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row179_col4\" class=\"data row179 col4\" >0.000237253</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row179_col5\" class=\"data row179 col5\" >0.215034</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row180\" class=\"row_heading level0 row180\" >180</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row180_col0\" class=\"data row180 col0\" >Electrical_FuseA</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row180_col1\" class=\"data row180 col1\" >0.58</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row180_col2\" class=\"data row180 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row180_col3\" class=\"data row180 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row180_col4\" class=\"data row180 col4\" >0.000205948</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row180_col5\" class=\"data row180 col5\" >0.145076</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row181\" class=\"row_heading level0 row181\" >181</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row181_col0\" class=\"data row181 col0\" >Electrical_FuseF</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row181_col1\" class=\"data row181 col1\" >0.41</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row181_col2\" class=\"data row181 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row181_col3\" class=\"data row181 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row181_col4\" class=\"data row181 col4\" >2.44693e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row181_col5\" class=\"data row181 col5\" >0.102531</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row182\" class=\"row_heading level0 row182\" >182</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row182_col0\" class=\"data row182 col0\" >Electrical_Other</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row182_col1\" class=\"data row182 col1\" >0.08</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row182_col2\" class=\"data row182 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row182_col3\" class=\"data row182 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row182_col4\" class=\"data row182 col4\" >1.9304e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row182_col5\" class=\"data row182 col5\" >0.0200005</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row183\" class=\"row_heading level0 row183\" >183</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row183_col0\" class=\"data row183 col0\" >Electrical_SBrkr</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row183_col1\" class=\"data row183 col1\" >0.8</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row183_col2\" class=\"data row183 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row183_col3\" class=\"data row183 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row183_col4\" class=\"data row183 col4\" >0.000533749</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row183_col5\" class=\"data row183 col5\" >0.200133</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row184\" class=\"row_heading level0 row184\" >184</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row184_col0\" class=\"data row184 col0\" >Functional_Maj1</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row184_col1\" class=\"data row184 col1\" >0.22</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row184_col2\" class=\"data row184 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row184_col3\" class=\"data row184 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row184_col4\" class=\"data row184 col4\" >9.61888e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row184_col5\" class=\"data row184 col5\" >0.0550274</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row185\" class=\"row_heading level0 row185\" >185</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row185_col0\" class=\"data row185 col0\" >Functional_Min1</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row185_col1\" class=\"data row185 col1\" >0.26</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row185_col2\" class=\"data row185 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row185_col3\" class=\"data row185 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row185_col4\" class=\"data row185 col4\" >3.28862e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row185_col5\" class=\"data row185 col5\" >0.0650332</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row186\" class=\"row_heading level0 row186\" >186</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row186_col0\" class=\"data row186 col0\" >Functional_Min2</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row186_col1\" class=\"data row186 col1\" >0.36</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row186_col2\" class=\"data row186 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row186_col3\" class=\"data row186 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row186_col4\" class=\"data row186 col4\" >3.11645e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row186_col5\" class=\"data row186 col5\" >0.0900078</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row187\" class=\"row_heading level0 row187\" >187</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row187_col0\" class=\"data row187 col0\" >Functional_Mod</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row187_col1\" class=\"data row187 col1\" >0.35</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row187_col2\" class=\"data row187 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row187_col3\" class=\"data row187 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row187_col4\" class=\"data row187 col4\" >1.91379e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row187_col5\" class=\"data row187 col5\" >0.0875048</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row188\" class=\"row_heading level0 row188\" >188</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row188_col0\" class=\"data row188 col0\" >Functional_Other</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row188_col1\" class=\"data row188 col1\" >0.68</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row188_col2\" class=\"data row188 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row188_col3\" class=\"data row188 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row188_col4\" class=\"data row188 col4\" >8.89426e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row188_col5\" class=\"data row188 col5\" >0.170047</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row189\" class=\"row_heading level0 row189\" >189</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row189_col0\" class=\"data row189 col0\" >Functional_Typ</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row189_col1\" class=\"data row189 col1\" >0.81</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row189_col2\" class=\"data row189 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row189_col3\" class=\"data row189 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row189_col4\" class=\"data row189 col4\" >0.000408121</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row189_col5\" class=\"data row189 col5\" >0.202602</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row190\" class=\"row_heading level0 row190\" >190</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row190_col0\" class=\"data row190 col0\" >GarageType_2Types</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row190_col1\" class=\"data row190 col1\" >0.03</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row190_col2\" class=\"data row190 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row190_col3\" class=\"data row190 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row190_col4\" class=\"data row190 col4\" >3.03366e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row190_col5\" class=\"data row190 col5\" >0.00750076</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row191\" class=\"row_heading level0 row191\" >191</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row191_col0\" class=\"data row191 col0\" >GarageType_Attchd</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row191_col1\" class=\"data row191 col1\" >0.82</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row191_col2\" class=\"data row191 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row191_col3\" class=\"data row191 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row191_col4\" class=\"data row191 col4\" >0.000733942</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row191_col5\" class=\"data row191 col5\" >0.205158</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row192\" class=\"row_heading level0 row192\" >192</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row192_col0\" class=\"data row192 col0\" >GarageType_Basment</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row192_col1\" class=\"data row192 col1\" >0.24</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row192_col2\" class=\"data row192 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row192_col3\" class=\"data row192 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row192_col4\" class=\"data row192 col4\" >1.97044e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row192_col5\" class=\"data row192 col5\" >0.0599799</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row193\" class=\"row_heading level0 row193\" >193</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row193_col0\" class=\"data row193 col0\" >GarageType_BuiltIn</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row193_col1\" class=\"data row193 col1\" >0.44</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row193_col2\" class=\"data row193 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row193_col3\" class=\"data row193 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row193_col4\" class=\"data row193 col4\" >5.66844e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row193_col5\" class=\"data row193 col5\" >0.109989</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row194\" class=\"row_heading level0 row194\" >194</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row194_col0\" class=\"data row194 col0\" >GarageType_CarPort</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row194_col1\" class=\"data row194 col1\" >0.54</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row194_col2\" class=\"data row194 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row194_col3\" class=\"data row194 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row194_col4\" class=\"data row194 col4\" >0.000102029</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row194_col5\" class=\"data row194 col5\" >0.135001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row195\" class=\"row_heading level0 row195\" >195</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row195_col0\" class=\"data row195 col0\" >GarageType_Detchd</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row195_col1\" class=\"data row195 col1\" >0.76</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row195_col2\" class=\"data row195 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row195_col3\" class=\"data row195 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row195_col4\" class=\"data row195 col4\" >0.00029509</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row195_col5\" class=\"data row195 col5\" >0.190074</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row196\" class=\"row_heading level0 row196\" >196</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row196_col0\" class=\"data row196 col0\" >GarageType_None</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row196_col1\" class=\"data row196 col1\" >0.31</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row196_col2\" class=\"data row196 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row196_col3\" class=\"data row196 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row196_col4\" class=\"data row196 col4\" >1.68397e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row196_col5\" class=\"data row196 col5\" >0.0774792</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row197\" class=\"row_heading level0 row197\" >197</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row197_col0\" class=\"data row197 col0\" >GarageFinish_Fin</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row197_col1\" class=\"data row197 col1\" >0.63</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row197_col2\" class=\"data row197 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row197_col3\" class=\"data row197 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row197_col4\" class=\"data row197 col4\" >0.000180663</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row197_col5\" class=\"data row197 col5\" >0.15752</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row198\" class=\"row_heading level0 row198\" >198</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row198_col0\" class=\"data row198 col0\" >GarageFinish_None</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row198_col1\" class=\"data row198 col1\" >0.26</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row198_col2\" class=\"data row198 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row198_col3\" class=\"data row198 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row198_col4\" class=\"data row198 col4\" >4.7679e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row198_col5\" class=\"data row198 col5\" >0.0650119</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row199\" class=\"row_heading level0 row199\" >199</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row199_col0\" class=\"data row199 col0\" >GarageFinish_RFn</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row199_col1\" class=\"data row199 col1\" >0.69</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row199_col2\" class=\"data row199 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row199_col3\" class=\"data row199 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row199_col4\" class=\"data row199 col4\" >0.000199188</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row199_col5\" class=\"data row199 col5\" >0.172525</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row200\" class=\"row_heading level0 row200\" >200</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row200_col0\" class=\"data row200 col0\" >GarageFinish_Unf</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row200_col1\" class=\"data row200 col1\" >0.8</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row200_col2\" class=\"data row200 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row200_col3\" class=\"data row200 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row200_col4\" class=\"data row200 col4\" >0.000503222</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row200_col5\" class=\"data row200 col5\" >0.200126</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row201\" class=\"row_heading level0 row201\" >201</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row201_col0\" class=\"data row201 col0\" >PavedDrive_N</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row201_col1\" class=\"data row201 col1\" >0.49</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row201_col2\" class=\"data row201 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row201_col3\" class=\"data row201 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row201_col4\" class=\"data row201 col4\" >7.19559e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row201_col5\" class=\"data row201 col5\" >0.122493</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row202\" class=\"row_heading level0 row202\" >202</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row202_col0\" class=\"data row202 col0\" >PavedDrive_P</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row202_col1\" class=\"data row202 col1\" >0.35</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row202_col2\" class=\"data row202 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row202_col3\" class=\"data row202 col3\" >-0.0002</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row202_col4\" class=\"data row202 col4\" >1.22577e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row202_col5\" class=\"data row202 col5\" >0.0874531</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row203\" class=\"row_heading level0 row203\" >203</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row203_col0\" class=\"data row203 col0\" >PavedDrive_Y</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row203_col1\" class=\"data row203 col1\" >0.62</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row203_col2\" class=\"data row203 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row203_col3\" class=\"data row203 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row203_col4\" class=\"data row203 col4\" >9.17914e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row203_col5\" class=\"data row203 col5\" >0.155023</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row204\" class=\"row_heading level0 row204\" >204</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row204_col0\" class=\"data row204 col0\" >Fence_GdPrv</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row204_col1\" class=\"data row204 col1\" >0.46</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row204_col2\" class=\"data row204 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row204_col3\" class=\"data row204 col3\" >-0.0002</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row204_col4\" class=\"data row204 col4\" >6.53032e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row204_col5\" class=\"data row204 col5\" >0.114966</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row205\" class=\"row_heading level0 row205\" >205</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row205_col0\" class=\"data row205 col0\" >Fence_GdWo</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row205_col1\" class=\"data row205 col1\" >0.55</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row205_col2\" class=\"data row205 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row205_col3\" class=\"data row205 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row205_col4\" class=\"data row205 col4\" >0.000365306</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row205_col5\" class=\"data row205 col5\" >0.137591</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row206\" class=\"row_heading level0 row206\" >206</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row206_col0\" class=\"data row206 col0\" >Fence_MnPrv</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row206_col1\" class=\"data row206 col1\" >0.46</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row206_col2\" class=\"data row206 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row206_col3\" class=\"data row206 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row206_col4\" class=\"data row206 col4\" >5.40799e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row206_col5\" class=\"data row206 col5\" >0.115014</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row207\" class=\"row_heading level0 row207\" >207</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row207_col0\" class=\"data row207 col0\" >Fence_MnWw</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row207_col1\" class=\"data row207 col1\" >0.09</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row207_col2\" class=\"data row207 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row207_col3\" class=\"data row207 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row207_col4\" class=\"data row207 col4\" >8.9661e-07</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row207_col5\" class=\"data row207 col5\" >0.0224752</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row208\" class=\"row_heading level0 row208\" >208</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row208_col0\" class=\"data row208 col0\" >Fence_None</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row208_col1\" class=\"data row208 col1\" >0.68</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row208_col2\" class=\"data row208 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row208_col3\" class=\"data row208 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row208_col4\" class=\"data row208 col4\" >0.000144757</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row208_col5\" class=\"data row208 col5\" >0.170036</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row209\" class=\"row_heading level0 row209\" >209</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row209_col0\" class=\"data row209 col0\" >MiscFeature_Gar2</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row209_col1\" class=\"data row209 col1\" >0.01</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row209_col2\" class=\"data row209 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row209_col3\" class=\"data row209 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row209_col4\" class=\"data row209 col4\" >1.71068e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row209_col5\" class=\"data row209 col5\" >0.00247543</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row210\" class=\"row_heading level0 row210\" >210</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row210_col0\" class=\"data row210 col0\" >MiscFeature_None</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row210_col1\" class=\"data row210 col1\" >0.29</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row210_col2\" class=\"data row210 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row210_col3\" class=\"data row210 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row210_col4\" class=\"data row210 col4\" >2.82852e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row210_col5\" class=\"data row210 col5\" >0.0724821</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row211\" class=\"row_heading level0 row211\" >211</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row211_col0\" class=\"data row211 col0\" >MiscFeature_Othr</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row211_col1\" class=\"data row211 col1\" >0.04</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row211_col2\" class=\"data row211 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row211_col3\" class=\"data row211 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row211_col4\" class=\"data row211 col4\" >1.15552e-07</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row211_col5\" class=\"data row211 col5\" >0.010025</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row212\" class=\"row_heading level0 row212\" >212</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row212_col0\" class=\"data row212 col0\" >MiscFeature_Shed</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row212_col1\" class=\"data row212 col1\" >0.21</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row212_col2\" class=\"data row212 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row212_col3\" class=\"data row212 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row212_col4\" class=\"data row212 col4\" >1.43993e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row212_col5\" class=\"data row212 col5\" >0.0525004</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row213\" class=\"row_heading level0 row213\" >213</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row213_col0\" class=\"data row213 col0\" >MiscFeature_TenC</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row213_col1\" class=\"data row213 col1\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row213_col2\" class=\"data row213 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row213_col3\" class=\"data row213 col3\" >-0.0002</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row213_col4\" class=\"data row213 col4\" >1.18057e-07</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row213_col5\" class=\"data row213 col5\" >-4.99705e-05</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row214\" class=\"row_heading level0 row214\" >214</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row214_col0\" class=\"data row214 col0\" >SaleType_COD</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row214_col1\" class=\"data row214 col1\" >0.39</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row214_col2\" class=\"data row214 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row214_col3\" class=\"data row214 col3\" >-0.0002</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row214_col4\" class=\"data row214 col4\" >4.94119e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row214_col5\" class=\"data row214 col5\" >0.0974624</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row215\" class=\"row_heading level0 row215\" >215</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row215_col0\" class=\"data row215 col0\" >SaleType_CWD</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row215_col1\" class=\"data row215 col1\" >0.1</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row215_col2\" class=\"data row215 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row215_col3\" class=\"data row215 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row215_col4\" class=\"data row215 col4\" >9.78201e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row215_col5\" class=\"data row215 col5\" >0.0249774</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row216\" class=\"row_heading level0 row216\" >216</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row216_col0\" class=\"data row216 col0\" >SaleType_ConLD</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row216_col1\" class=\"data row216 col1\" >0.37</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row216_col2\" class=\"data row216 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row216_col3\" class=\"data row216 col3\" >-0.0002</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row216_col4\" class=\"data row216 col4\" >9.4564e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row216_col5\" class=\"data row216 col5\" >0.0924736</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row217\" class=\"row_heading level0 row217\" >217</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row217_col0\" class=\"data row217 col0\" >SaleType_New</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row217_col1\" class=\"data row217 col1\" >0.52</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row217_col2\" class=\"data row217 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row217_col3\" class=\"data row217 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row217_col4\" class=\"data row217 col4\" >8.91348e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row217_col5\" class=\"data row217 col5\" >0.130022</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row218\" class=\"row_heading level0 row218\" >218</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row218_col0\" class=\"data row218 col0\" >SaleType_Other</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row218_col1\" class=\"data row218 col1\" >0.23</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row218_col2\" class=\"data row218 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row218_col3\" class=\"data row218 col3\" >0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row218_col4\" class=\"data row218 col4\" >7.21392e-06</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row218_col5\" class=\"data row218 col5\" >0.0575268</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row219\" class=\"row_heading level0 row219\" >219</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row219_col0\" class=\"data row219 col0\" >SaleType_WD</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row219_col1\" class=\"data row219 col1\" >0.8</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row219_col2\" class=\"data row219 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row219_col3\" class=\"data row219 col3\" >-0.0001</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row219_col4\" class=\"data row219 col4\" >0.000217187</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row219_col5\" class=\"data row219 col5\" >0.200029</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row220\" class=\"row_heading level0 row220\" >220</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row220_col0\" class=\"data row220 col0\" >SaleCondition_Abnorml</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row220_col1\" class=\"data row220 col1\" >0.86</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row220_col2\" class=\"data row220 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row220_col3\" class=\"data row220 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row220_col4\" class=\"data row220 col4\" >0.000869491</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row220_col5\" class=\"data row220 col5\" >0.215217</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row221\" class=\"row_heading level0 row221\" >221</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row221_col0\" class=\"data row221 col0\" >SaleCondition_AdjLand</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row221_col1\" class=\"data row221 col1\" >0.01</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row221_col2\" class=\"data row221 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row221_col3\" class=\"data row221 col3\" >-0.0002</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row221_col4\" class=\"data row221 col4\" >2.79465e-07</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row221_col5\" class=\"data row221 col5\" >0.00245007</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row222\" class=\"row_heading level0 row222\" >222</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row222_col0\" class=\"data row222 col0\" >SaleCondition_Alloca</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row222_col1\" class=\"data row222 col1\" >0.34</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row222_col2\" class=\"data row222 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row222_col3\" class=\"data row222 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row222_col4\" class=\"data row222 col4\" >1.83618e-05</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row222_col5\" class=\"data row222 col5\" >0.0850046</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row223\" class=\"row_heading level0 row223\" >223</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row223_col0\" class=\"data row223 col0\" >SaleCondition_Family</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row223_col1\" class=\"data row223 col1\" >0.82</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row223_col2\" class=\"data row223 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row223_col3\" class=\"data row223 col3\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row223_col4\" class=\"data row223 col4\" >0.00195218</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row223_col5\" class=\"data row223 col5\" >0.205488</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row224\" class=\"row_heading level0 row224\" >224</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row224_col0\" class=\"data row224 col0\" >SaleCondition_Normal</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row224_col1\" class=\"data row224 col1\" >0.78</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row224_col2\" class=\"data row224 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row224_col3\" class=\"data row224 col3\" >-0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row224_col4\" class=\"data row224 col4\" >0.000439182</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row224_col5\" class=\"data row224 col5\" >0.19511</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625level0_row225\" class=\"row_heading level0 row225\" >225</th>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row225_col0\" class=\"data row225 col0\" >SaleCondition_Partial</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row225_col1\" class=\"data row225 col1\" >0.65</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row225_col2\" class=\"data row225 col2\" >0</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row225_col3\" class=\"data row225 col3\" >-0.0002</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row225_col4\" class=\"data row225 col4\" >0.000106959</td>\n",
       "                        <td id=\"T_6c7a4594_c36e_11ea_9162_3024323c1625row225_col5\" class=\"data row225 col5\" >0.162477</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x18c9caf9c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# merge drop_col_impt_df\n",
    "all_ranks = pd.merge(all_ranks, drop_col_impt_df, on=['Feature'])\n",
    "\n",
    "# merge permutate_df\n",
    "all_ranks = pd.merge(all_ranks, permutate_df, on=['Feature'])\n",
    "\n",
    "# calculate average feature importance\n",
    "average_fi_pipeline = pdp.PdPipeline([\n",
    "    pdp.ApplyToRows(\n",
    "        lambda row: (row['drop_col_importance'] + row['permutation_importance'] + row['rfe_importance'] + row['alg_importance'])/4, \n",
    "        colname='mean_feature_importance') # 'mean_feature_importance\n",
    "])\n",
    "\n",
    "all_ranks = average_fi_pipeline.apply(all_ranks)\n",
    "\n",
    "display(all_ranks.reset_index().drop(['index'], axis=1).style.background_gradient(cmap='summer_r'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting Model Experiments\n",
    "\n",
    "Based on the feature selection experiments above, we are going to train a bunch of different Random Forests (RF) models (using different feature sets and number of trees to train) to see which one performs better.\n",
    "\n",
    "In the course of the activities down the road, we will have to repeat two core activities below\n",
    "\n",
    "- subset top N features from the entire set of available features, based on a certain feature importance metrics\n",
    "- measure an individual model performace as well as output it in a format ready to aggregate in a single dataframe (to compare each model with its counterparts  as well as identify the one with the best performance)\n",
    "\n",
    "So we are going to automate it with two auxiliary functions below\n",
    "- *get_top_features_by_rank*\n",
    "- *model_check*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_top_features_by_rank(metric_col_name: str, feature_number: int):\n",
    "    features_df = all_ranks.copy()\n",
    "    \n",
    "    # features_df = features_df.sort_values(by=['feature_number'])\n",
    "    \n",
    "    # TODO: [:feature_number]\n",
    "    \n",
    "    # top n rows ordered by multiple columns\n",
    "    features_df = features_df.nlargest(feature_number, [metric_col_name])\n",
    "    \n",
    "    result_list = list(features_df['Feature'])\n",
    "    return result_list\n",
    "\n",
    "def model_check(X, y, estimator, model_name, model_description, cv):\n",
    "    model_table = pd.DataFrame()\n",
    "\n",
    "    cv_results = cross_validate(estimator,\n",
    "                                X,\n",
    "                                y,\n",
    "                                cv=cv,\n",
    "                                scoring='neg_root_mean_squared_error',\n",
    "                                return_train_score=True,\n",
    "                                n_jobs=-1)\n",
    "\n",
    "    train_rmse = -cv_results['train_score'].mean()\n",
    "    test_rmse = -cv_results['test_score'].mean()\n",
    "    test_std = cv_results['test_score'].std()\n",
    "    fit_time = cv_results['fit_time'].mean()\n",
    "\n",
    "    attributes = {\n",
    "        'model_name': model_name,\n",
    "        'train_score': train_rmse,\n",
    "        'test_score': test_rmse,\n",
    "        'test_std': test_std,\n",
    "        'fit_time': fit_time,\n",
    "        'description': model_description,\n",
    "    }\n",
    "    \n",
    "    model_table = pd.DataFrame(data=[attributes])\n",
    "    return model_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of all, we are going to train a baseline RF model. It is going to be rather simple and not likely to perform in a good fashion. However, it will provide as the reasonable indication on how the more advanced models improve their performace vs. the baseline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check the baseline RF\n",
    "baseline = model_check(X, y, rf, 'Baseline RF', \"Baseline RF (100 trees, all features)\", kf)\n",
    "result_df = baseline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After that, we are going to train a set of RF models, using the entire feature set we have after the  feature engineering above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_estimators = [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000]\n",
    "for n in n_estimators:\n",
    "    rf2 = RandomForestRegressor(n_jobs=-1, n_estimators=n, verbose=1)\n",
    "    description = \"RF with n_trees = {}\".format(n)\n",
    "    model_check_df = model_check(X, y, rf2, 'RF - All Features', description, kf)\n",
    "    \n",
    "    # concatenate\n",
    "    frames = [result_df, model_check_df]\n",
    "    result_df = pd.concat(frames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next series of experiemnts, we are going to train a number of RF models that use only top 50 features as ranked by **RFE** feature importance/feature selection algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subset of features selected by RFE feature importance\n",
    "top_rfe_features = 50\n",
    "rfe_features = get_top_features_by_rank('rfe_importance', top_rfe_features)\n",
    "X_important_features = X[rfe_features]\n",
    "n_estimators = [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000]\n",
    "for n in n_estimators:\n",
    "    rf2 = RandomForestRegressor(n_jobs=-1, n_estimators=n, verbose=1)\n",
    "    description = \"RF with top {} RFE features, n_trees = {}\".format(top_rfe_features, n)\n",
    "    model_check_df = model_check(X_important_features, y, rf2, 'RFE Features', description, kf)\n",
    "    \n",
    "    # concatenate\n",
    "    frames = [result_df, model_check_df]\n",
    "    result_df = pd.concat(frames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As our next step, we are going to train a set of RF models that utilize only the tiny subset of features selected from the model via embedded feature selection algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subset of features selected by RF embedded Feature Selection\n",
    "X_embedded_features = X[embeded_rf_feature]\n",
    "n_estimators = [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000]\n",
    "for n in n_estimators:\n",
    "    rf2 = RandomForestRegressor(n_jobs=-1, n_estimators=n, verbose=1)\n",
    "    description = \"RF witn n_trees = {}\".format(n)\n",
    "    model_check_df = model_check(X_embedded_features, y, rf2, 'RF - Embedded Features', description, kf)\n",
    "    \n",
    "    # concatenate\n",
    "    frames = [result_df, model_check_df]\n",
    "    result_df = pd.concat(frames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are ready to train one more set of RF models that use top 50 features selected by **permutation method**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train RF with the top importance feautres selected via the permutation method\n",
    "top_features = 50\n",
    "important_features = get_top_features_by_rank('permutation_importance', top_features)\n",
    "X_important_features = X[important_features]\n",
    "\n",
    "n_estimators = [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000]\n",
    "for n in n_estimators:\n",
    "    rf2 = RandomForestRegressor(n_jobs=-1, n_estimators=n, verbose=1)\n",
    "    description = \"RF with top {} permutatively important features, n_trees = {}\".format(top_rfe_features, n)\n",
    "    model_check_df = model_check(X_important_features, y, rf2, 'RF - Permutatively Important Features', description, kf)\n",
    "    \n",
    "    # concatenate\n",
    "    frames = [result_df, model_check_df]\n",
    "    result_df = pd.concat(frames)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a final step in our experiments, we are going to train a set of RF models with the top 50 features selected by **drop-column method**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train RF with the top importance feautres selected via the drop-column method\n",
    "top_features = 50\n",
    "important_features = get_top_features_by_rank('drop_col_importance', top_features)\n",
    "X_important_features = X[important_features]\n",
    "\n",
    "n_estimators = [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000]\n",
    "for n in n_estimators:\n",
    "    rf2 = RandomForestRegressor(n_jobs=-1, n_estimators=n, verbose=1)\n",
    "    description = \"RF with top {} drop-col-important features, n_trees = {}\".format(top_rfe_features, n)\n",
    "    model_check_df = model_check(X_important_features, y, rf2, 'RF - Drop-Column Important Features', description, kf)\n",
    "    \n",
    "    # concatenate\n",
    "    frames = [result_df, model_check_df]\n",
    "    result_df = pd.concat(frames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to display and compare the results of scoring each of the trained model in the tabular view below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
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       "            background-color:  #178b66;\n",
       "            color:  #000000;\n",
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       "            background-color:  #eef666;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row10_col2 {\n",
       "            background-color:  #ecf666;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row10_col3 {\n",
       "            background-color:  #a1d066;\n",
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       "            background-color:  #008066;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #90c866;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f9fc66;\n",
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       "            background-color:  #badc66;\n",
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       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row11_col4 {\n",
       "            background-color:  #fcfe66;\n",
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       "            background-color:  #deee66;\n",
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       "            background-color:  #b1d866;\n",
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       "            background-color:  #f7fb66;\n",
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       "            background-color:  #f0f866;\n",
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       "            background-color:  #d0e866;\n",
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       "            background-color:  #b5da66;\n",
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       "            background-color:  #edf666;\n",
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       "            background-color:  #ebf566;\n",
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       "            background-color:  #e6f266;\n",
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       "            background-color:  #e9f466;\n",
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       "            background-color:  #f4fa66;\n",
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       "            background-color:  #f0f866;\n",
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       "            background-color:  #d8ec66;\n",
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       "            background-color:  #f7fb66;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row38_col3 {\n",
       "            background-color:  #abd566;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row38_col4 {\n",
       "            background-color:  #cce666;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row39_col1 {\n",
       "            background-color:  #f6fa66;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row39_col2 {\n",
       "            background-color:  #f9fc66;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row39_col3 {\n",
       "            background-color:  #add666;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row39_col4 {\n",
       "            background-color:  #c3e166;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row40_col1 {\n",
       "            background-color:  #fdfe66;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row40_col2 {\n",
       "            background-color:  #f6fa66;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row40_col3 {\n",
       "            background-color:  #aed666;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row40_col4 {\n",
       "            background-color:  #bdde66;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row41_col1 {\n",
       "            background-color:  #afd766;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row41_col2 {\n",
       "            background-color:  #f1f866;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row41_col3 {\n",
       "            background-color:  #088466;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row41_col4 {\n",
       "            background-color:  #fefe66;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row42_col1 {\n",
       "            background-color:  #d5ea66;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row42_col2 {\n",
       "            background-color:  #f6fa66;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row42_col3 {\n",
       "            background-color:  #138966;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row42_col4 {\n",
       "            background-color:  #fbfd66;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row43_col1 {\n",
       "            background-color:  #daec66;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row43_col2 {\n",
       "            background-color:  #eaf466;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row43_col3 {\n",
       "            background-color:  #2b9566;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row43_col4 {\n",
       "            background-color:  #f7fb66;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row44_col1 {\n",
       "            background-color:  #ecf666;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row44_col2 {\n",
       "            background-color:  #eef666;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row44_col3 {\n",
       "            background-color:  #008066;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row44_col4 {\n",
       "            background-color:  #f1f866;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row45_col1 {\n",
       "            background-color:  #f6fa66;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row45_col2 {\n",
       "            background-color:  #e6f266;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row45_col3 {\n",
       "            background-color:  #168b66;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row45_col4 {\n",
       "            background-color:  #eef666;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row46_col1 {\n",
       "            background-color:  #f5fa66;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row46_col2 {\n",
       "            background-color:  #fafc66;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row46_col3 {\n",
       "            background-color:  #098466;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row46_col4 {\n",
       "            background-color:  #eaf466;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row47_col1 {\n",
       "            background-color:  #f0f866;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row47_col2 {\n",
       "            background-color:  #ffff66;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row47_col3 {\n",
       "            background-color:  #1f8f66;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row47_col4 {\n",
       "            background-color:  #e4f266;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row48_col1 {\n",
       "            background-color:  #fafc66;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row48_col2 {\n",
       "            background-color:  #fafc66;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row48_col3 {\n",
       "            background-color:  #198c66;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row48_col4 {\n",
       "            background-color:  #e0f066;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row49_col1 {\n",
       "            background-color:  #fdfe66;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row49_col2 {\n",
       "            background-color:  #f6fa66;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row49_col3 {\n",
       "            background-color:  #0e8666;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row49_col4 {\n",
       "            background-color:  #dbed66;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row50_col1 {\n",
       "            background-color:  #f6fa66;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row50_col2 {\n",
       "            background-color:  #f6fa66;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row50_col3 {\n",
       "            background-color:  #168b66;\n",
       "            color:  #000000;\n",
       "        }    #T_95cb94fa_c370_11ea_b2d2_3024323c1625row50_col4 {\n",
       "            background-color:  #d7eb66;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >model_name</th>        <th class=\"col_heading level0 col1\" >train_score</th>        <th class=\"col_heading level0 col2\" >test_score</th>        <th class=\"col_heading level0 col3\" >test_std</th>        <th class=\"col_heading level0 col4\" >fit_time</th>        <th class=\"col_heading level0 col5\" >description</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row0_col0\" class=\"data row0 col0\" >Baseline RF</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row0_col1\" class=\"data row0 col1\" >0.0485876</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row0_col2\" class=\"data row0 col2\" >0.129235</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row0_col3\" class=\"data row0 col3\" >0.0207149</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row0_col4\" class=\"data row0 col4\" >3.2876</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row0_col5\" class=\"data row0 col5\" >Baseline RF (100 trees, all features)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row1_col0\" class=\"data row1 col0\" >RF - All Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row1_col1\" class=\"data row1 col1\" >0.0481726</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row1_col2\" class=\"data row1 col2\" >0.128278</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row1_col3\" class=\"data row1 col3\" >0.0206636</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row1_col4\" class=\"data row1 col4\" >5.5646</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row1_col5\" class=\"data row1 col5\" >RF with n_trees = 200</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row2_col0\" class=\"data row2 col0\" >RF - All Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row2_col1\" class=\"data row2 col1\" >0.0477492</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row2_col2\" class=\"data row2 col2\" >0.128291</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row2_col3\" class=\"data row2 col3\" >0.0207393</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row2_col4\" class=\"data row2 col4\" >16.1621</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row2_col5\" class=\"data row2 col5\" >RF with n_trees = 400</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row3_col0\" class=\"data row3 col0\" >RF - All Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row3_col1\" class=\"data row3 col1\" >0.0476427</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row3_col2\" class=\"data row3 col2\" >0.12822</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row3_col3\" class=\"data row3 col3\" >0.0205502</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row3_col4\" class=\"data row3 col4\" >23.7572</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row3_col5\" class=\"data row3 col5\" >RF with n_trees = 600</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row4_col0\" class=\"data row4 col0\" >RF - All Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row4_col1\" class=\"data row4 col1\" >0.0474943</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row4_col2\" class=\"data row4 col2\" >0.127917</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row4_col3\" class=\"data row4 col3\" >0.0207544</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row4_col4\" class=\"data row4 col4\" >33.8056</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row4_col5\" class=\"data row4 col5\" >RF with n_trees = 800</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row5_col0\" class=\"data row5 col0\" >RF - All Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row5_col1\" class=\"data row5 col1\" >0.0475538</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row5_col2\" class=\"data row5 col2\" >0.127724</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row5_col3\" class=\"data row5 col3\" >0.0204829</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row5_col4\" class=\"data row5 col4\" >42.1269</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row5_col5\" class=\"data row5 col5\" >RF with n_trees = 1000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row6_col0\" class=\"data row6 col0\" >RF - All Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row6_col1\" class=\"data row6 col1\" >0.0475771</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row6_col2\" class=\"data row6 col2\" >0.127831</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row6_col3\" class=\"data row6 col3\" >0.020635</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row6_col4\" class=\"data row6 col4\" >50.9815</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row6_col5\" class=\"data row6 col5\" >RF with n_trees = 1200</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row7_col0\" class=\"data row7 col0\" >RF - All Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row7_col1\" class=\"data row7 col1\" >0.0476243</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row7_col2\" class=\"data row7 col2\" >0.128024</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row7_col3\" class=\"data row7 col3\" >0.0209589</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row7_col4\" class=\"data row7 col4\" >58.4966</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row7_col5\" class=\"data row7 col5\" >RF with n_trees = 1400</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row8_col0\" class=\"data row8 col0\" >RF - All Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row8_col1\" class=\"data row8 col1\" >0.0475172</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row8_col2\" class=\"data row8 col2\" >0.12819</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row8_col3\" class=\"data row8 col3\" >0.0206624</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row8_col4\" class=\"data row8 col4\" >67.375</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row8_col5\" class=\"data row8 col5\" >RF with n_trees = 1600</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row9_col0\" class=\"data row9 col0\" >RF - All Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row9_col1\" class=\"data row9 col1\" >0.0475164</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row9_col2\" class=\"data row9 col2\" >0.12793</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row9_col3\" class=\"data row9 col3\" >0.0206333</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row9_col4\" class=\"data row9 col4\" >76.8426</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row9_col5\" class=\"data row9 col5\" >RF with n_trees = 1800</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row10_col0\" class=\"data row10 col0\" >RF - All Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row10_col1\" class=\"data row10 col1\" >0.0476035</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row10_col2\" class=\"data row10 col2\" >0.12788</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row10_col3\" class=\"data row10 col3\" >0.0205963</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row10_col4\" class=\"data row10 col4\" >84.5899</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row10_col5\" class=\"data row10 col5\" >RF with n_trees = 2000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row11_col0\" class=\"data row11 col0\" >RFE Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row11_col1\" class=\"data row11 col1\" >0.0486766</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row11_col2\" class=\"data row11 col2\" >0.127596</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row11_col3\" class=\"data row11 col3\" >0.0201222</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row11_col4\" class=\"data row11 col4\" >2.10099</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row11_col5\" class=\"data row11 col5\" >RF with top 50 RFE features, n_trees = 100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row12_col0\" class=\"data row12 col0\" >RFE Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row12_col1\" class=\"data row12 col1\" >0.0477879</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row12_col2\" class=\"data row12 col2\" >0.127741</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row12_col3\" class=\"data row12 col3\" >0.0202877</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row12_col4\" class=\"data row12 col4\" >3.79819</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row12_col5\" class=\"data row12 col5\" >RF with top 50 RFE features, n_trees = 200</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row13_col0\" class=\"data row13 col0\" >RFE Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row13_col1\" class=\"data row13 col1\" >0.0475786</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row13_col2\" class=\"data row13 col2\" >0.128456</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row13_col3\" class=\"data row13 col3\" >0.020212</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row13_col4\" class=\"data row13 col4\" >6.9335</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row13_col5\" class=\"data row13 col5\" >RF with top 50 RFE features, n_trees = 300</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row14_col0\" class=\"data row14 col0\" >RFE Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row14_col1\" class=\"data row14 col1\" >0.0476376</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row14_col2\" class=\"data row14 col2\" >0.127578</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row14_col3\" class=\"data row14 col3\" >0.0200974</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row14_col4\" class=\"data row14 col4\" >9.2787</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row14_col5\" class=\"data row14 col5\" >RF with top 50 RFE features, n_trees = 400</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row15_col0\" class=\"data row15 col0\" >RFE Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row15_col1\" class=\"data row15 col1\" >0.0476609</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row15_col2\" class=\"data row15 col2\" >0.12783</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row15_col3\" class=\"data row15 col3\" >0.020315</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row15_col4\" class=\"data row15 col4\" >10.9547</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row15_col5\" class=\"data row15 col5\" >RF with top 50 RFE features, n_trees = 500</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row16_col0\" class=\"data row16 col0\" >RFE Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row16_col1\" class=\"data row16 col1\" >0.0475322</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row16_col2\" class=\"data row16 col2\" >0.127783</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row16_col3\" class=\"data row16 col3\" >0.0198011</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row16_col4\" class=\"data row16 col4\" >13.7259</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row16_col5\" class=\"data row16 col5\" >RF with top 50 RFE features, n_trees = 600</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row17_col0\" class=\"data row17 col0\" >RFE Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row17_col1\" class=\"data row17 col1\" >0.047469</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row17_col2\" class=\"data row17 col2\" >0.127908</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row17_col3\" class=\"data row17 col3\" >0.02045</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row17_col4\" class=\"data row17 col4\" >16.1407</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row17_col5\" class=\"data row17 col5\" >RF with top 50 RFE features, n_trees = 700</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row18_col0\" class=\"data row18 col0\" >RFE Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row18_col1\" class=\"data row18 col1\" >0.0474025</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row18_col2\" class=\"data row18 col2\" >0.127509</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row18_col3\" class=\"data row18 col3\" >0.0200654</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row18_col4\" class=\"data row18 col4\" >18.402</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row18_col5\" class=\"data row18 col5\" >RF with top 50 RFE features, n_trees = 800</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row19_col0\" class=\"data row19 col0\" >RFE Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row19_col1\" class=\"data row19 col1\" >0.0474459</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row19_col2\" class=\"data row19 col2\" >0.12784</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row19_col3\" class=\"data row19 col3\" >0.0199338</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row19_col4\" class=\"data row19 col4\" >20.3282</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row19_col5\" class=\"data row19 col5\" >RF with top 50 RFE features, n_trees = 900</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row20_col0\" class=\"data row20 col0\" >RFE Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row20_col1\" class=\"data row20 col1\" >0.0475439</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row20_col2\" class=\"data row20 col2\" >0.127804</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row20_col3\" class=\"data row20 col3\" >0.0201745</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row20_col4\" class=\"data row20 col4\" >22.731</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row20_col5\" class=\"data row20 col5\" >RF with top 50 RFE features, n_trees = 1000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row21_col0\" class=\"data row21 col0\" >RF - Embedded Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row21_col1\" class=\"data row21 col1\" >0.0503343</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row21_col2\" class=\"data row21 col2\" >0.132821</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row21_col3\" class=\"data row21 col3\" >0.0187813</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row21_col4\" class=\"data row21 col4\" >0.858997</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row21_col5\" class=\"data row21 col5\" >RF witn n_trees = 100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row22_col0\" class=\"data row22 col0\" >RF - Embedded Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row22_col1\" class=\"data row22 col1\" >0.0496971</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row22_col2\" class=\"data row22 col2\" >0.1325</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row22_col3\" class=\"data row22 col3\" >0.019518</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row22_col4\" class=\"data row22 col4\" >1.56239</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row22_col5\" class=\"data row22 col5\" >RF witn n_trees = 200</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row23_col0\" class=\"data row23 col0\" >RF - Embedded Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row23_col1\" class=\"data row23 col1\" >0.0498297</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row23_col2\" class=\"data row23 col2\" >0.132624</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row23_col3\" class=\"data row23 col3\" >0.0191001</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row23_col4\" class=\"data row23 col4\" >2.58879</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row23_col5\" class=\"data row23 col5\" >RF witn n_trees = 300</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row24_col0\" class=\"data row24 col0\" >RF - Embedded Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row24_col1\" class=\"data row24 col1\" >0.0494146</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row24_col2\" class=\"data row24 col2\" >0.132846</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row24_col3\" class=\"data row24 col3\" >0.0193458</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row24_col4\" class=\"data row24 col4\" >3.4817</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row24_col5\" class=\"data row24 col5\" >RF witn n_trees = 400</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row25_col0\" class=\"data row25 col0\" >RF - Embedded Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row25_col1\" class=\"data row25 col1\" >0.0493676</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row25_col2\" class=\"data row25 col2\" >0.132689</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row25_col3\" class=\"data row25 col3\" >0.0196634</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row25_col4\" class=\"data row25 col4\" >4.22729</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row25_col5\" class=\"data row25 col5\" >RF witn n_trees = 500</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row26_col0\" class=\"data row26 col0\" >RF - Embedded Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row26_col1\" class=\"data row26 col1\" >0.0493939</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row26_col2\" class=\"data row26 col2\" >0.132353</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row26_col3\" class=\"data row26 col3\" >0.0193965</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row26_col4\" class=\"data row26 col4\" >4.89019</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row26_col5\" class=\"data row26 col5\" >RF witn n_trees = 600</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row27_col0\" class=\"data row27 col0\" >RF - Embedded Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row27_col1\" class=\"data row27 col1\" >0.0494067</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row27_col2\" class=\"data row27 col2\" >0.132456</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row27_col3\" class=\"data row27 col3\" >0.0191191</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row27_col4\" class=\"data row27 col4\" >5.54749</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row27_col5\" class=\"data row27 col5\" >RF witn n_trees = 700</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row28_col0\" class=\"data row28 col0\" >RF - Embedded Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row28_col1\" class=\"data row28 col1\" >0.0493566</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row28_col2\" class=\"data row28 col2\" >0.132626</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row28_col3\" class=\"data row28 col3\" >0.0197042</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row28_col4\" class=\"data row28 col4\" >6.4019</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row28_col5\" class=\"data row28 col5\" >RF witn n_trees = 800</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row29_col0\" class=\"data row29 col0\" >RF - Embedded Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row29_col1\" class=\"data row29 col1\" >0.0493415</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row29_col2\" class=\"data row29 col2\" >0.132429</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row29_col3\" class=\"data row29 col3\" >0.0190381</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row29_col4\" class=\"data row29 col4\" >7.86379</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row29_col5\" class=\"data row29 col5\" >RF witn n_trees = 900</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row30_col0\" class=\"data row30 col0\" >RF - Embedded Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row30_col1\" class=\"data row30 col1\" >0.0492797</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row30_col2\" class=\"data row30 col2\" >0.13271</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row30_col3\" class=\"data row30 col3\" >0.0194308</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row30_col4\" class=\"data row30 col4\" >8.1359</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row30_col5\" class=\"data row30 col5\" >RF witn n_trees = 1000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row31_col0\" class=\"data row31 col0\" >RF - Permutatively Important Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row31_col1\" class=\"data row31 col1\" >0.0490009</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row31_col2\" class=\"data row31 col2\" >0.128775</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row31_col3\" class=\"data row31 col3\" >0.0209574</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row31_col4\" class=\"data row31 col4\" >1.94764</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row31_col5\" class=\"data row31 col5\" >RF with top 50 permutatively important features, n_trees = 100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row32_col0\" class=\"data row32 col0\" >RF - Permutatively Important Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row32_col1\" class=\"data row32 col1\" >0.0481935</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row32_col2\" class=\"data row32 col2\" >0.128109</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row32_col3\" class=\"data row32 col3\" >0.0206333</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row32_col4\" class=\"data row32 col4\" >4.14439</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row32_col5\" class=\"data row32 col5\" >RF with top 50 permutatively important features, n_trees = 200</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row33_col0\" class=\"data row33 col0\" >RF - Permutatively Important Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row33_col1\" class=\"data row33 col1\" >0.047753</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row33_col2\" class=\"data row33 col2\" >0.127902</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row33_col3\" class=\"data row33 col3\" >0.0202305</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row33_col4\" class=\"data row33 col4\" >6.81899</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row33_col5\" class=\"data row33 col5\" >RF with top 50 permutatively important features, n_trees = 300</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row34_col0\" class=\"data row34 col0\" >RF - Permutatively Important Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row34_col1\" class=\"data row34 col1\" >0.0476447</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row34_col2\" class=\"data row34 col2\" >0.128092</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row34_col3\" class=\"data row34 col3\" >0.0201328</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row34_col4\" class=\"data row34 col4\" >8.3179</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row34_col5\" class=\"data row34 col5\" >RF with top 50 permutatively important features, n_trees = 400</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row35_col0\" class=\"data row35 col0\" >RF - Permutatively Important Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row35_col1\" class=\"data row35 col1\" >0.0477073</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row35_col2\" class=\"data row35 col2\" >0.127714</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row35_col3\" class=\"data row35 col3\" >0.0208532</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row35_col4\" class=\"data row35 col4\" >10.9785</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row35_col5\" class=\"data row35 col5\" >RF with top 50 permutatively important features, n_trees = 500</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row36_col0\" class=\"data row36 col0\" >RF - Permutatively Important Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row36_col1\" class=\"data row36 col1\" >0.0475774</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row36_col2\" class=\"data row36 col2\" >0.12771</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row36_col3\" class=\"data row36 col3\" >0.0205905</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row36_col4\" class=\"data row36 col4\" >14.1342</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row36_col5\" class=\"data row36 col5\" >RF with top 50 permutatively important features, n_trees = 600</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row37_col0\" class=\"data row37 col0\" >RF - Permutatively Important Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row37_col1\" class=\"data row37 col1\" >0.0474922</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row37_col2\" class=\"data row37 col2\" >0.127779</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row37_col3\" class=\"data row37 col3\" >0.0203979</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row37_col4\" class=\"data row37 col4\" >15.3123</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row37_col5\" class=\"data row37 col5\" >RF with top 50 permutatively important features, n_trees = 700</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row38_col0\" class=\"data row38 col0\" >RF - Permutatively Important Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row38_col1\" class=\"data row38 col1\" >0.0474227</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row38_col2\" class=\"data row38 col2\" >0.127639</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row38_col3\" class=\"data row38 col3\" >0.0204028</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row38_col4\" class=\"data row38 col4\" >17.7956</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row38_col5\" class=\"data row38 col5\" >RF with top 50 permutatively important features, n_trees = 800</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row39_col0\" class=\"data row39 col0\" >RF - Permutatively Important Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row39_col1\" class=\"data row39 col1\" >0.0475128</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row39_col2\" class=\"data row39 col2\" >0.1276</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row39_col3\" class=\"data row39 col3\" >0.0203627</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row39_col4\" class=\"data row39 col4\" >20.5236</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row39_col5\" class=\"data row39 col5\" >RF with top 50 permutatively important features, n_trees = 900</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row40_col0\" class=\"data row40 col0\" >RF - Permutatively Important Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row40_col1\" class=\"data row40 col1\" >0.0474283</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row40_col2\" class=\"data row40 col2\" >0.12765</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row40_col3\" class=\"data row40 col3\" >0.0203444</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row40_col4\" class=\"data row40 col4\" >22.6157</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row40_col5\" class=\"data row40 col5\" >RF with top 50 permutatively important features, n_trees = 1000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row41_col0\" class=\"data row41 col0\" >RF - Drop-Column Important Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row41_col1\" class=\"data row41 col1\" >0.0483252</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row41_col2\" class=\"data row41 col2\" >0.127769</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row41_col3\" class=\"data row41 col3\" >0.0235441</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row41_col4\" class=\"data row41 col4\" >1.2952</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row41_col5\" class=\"data row41 col5\" >RF with top 50 drop-col-important features, n_trees = 100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row42_col0\" class=\"data row42 col0\" >RF - Drop-Column Important Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row42_col1\" class=\"data row42 col1\" >0.0478871</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row42_col2\" class=\"data row42 col2\" >0.127667</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row42_col3\" class=\"data row42 col3\" >0.0233294</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row42_col4\" class=\"data row42 col4\" >2.43779</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row42_col5\" class=\"data row42 col5\" >RF with top 50 drop-col-important features, n_trees = 200</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row43_col0\" class=\"data row43 col0\" >RF - Drop-Column Important Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row43_col1\" class=\"data row43 col1\" >0.0478322</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row43_col2\" class=\"data row43 col2\" >0.127911</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row43_col3\" class=\"data row43 col3\" >0.0228792</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row43_col4\" class=\"data row43 col4\" >3.7876</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row43_col5\" class=\"data row43 col5\" >RF with top 50 drop-col-important features, n_trees = 300</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row44_col0\" class=\"data row44 col0\" >RF - Drop-Column Important Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row44_col1\" class=\"data row44 col1\" >0.0476236</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row44_col2\" class=\"data row44 col2\" >0.127832</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row44_col3\" class=\"data row44 col3\" >0.0237106</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row44_col4\" class=\"data row44 col4\" >5.6378</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row44_col5\" class=\"data row44 col5\" >RF with top 50 drop-col-important features, n_trees = 400</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row45_col0\" class=\"data row45 col0\" >RF - Drop-Column Important Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row45_col1\" class=\"data row45 col1\" >0.0475137</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row45_col2\" class=\"data row45 col2\" >0.127988</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row45_col3\" class=\"data row45 col3\" >0.0232843</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row45_col4\" class=\"data row45 col4\" >6.57909</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row45_col5\" class=\"data row45 col5\" >RF with top 50 drop-col-important features, n_trees = 500</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row46_col0\" class=\"data row46 col0\" >RF - Drop-Column Important Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row46_col1\" class=\"data row46 col1\" >0.0475228</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row46_col2\" class=\"data row46 col2\" >0.127581</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row46_col3\" class=\"data row46 col3\" >0.0235327</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row46_col4\" class=\"data row46 col4\" >7.8267</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row46_col5\" class=\"data row46 col5\" >RF with top 50 drop-col-important features, n_trees = 600</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row47_col0\" class=\"data row47 col0\" >RF - Drop-Column Important Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row47_col1\" class=\"data row47 col1\" >0.0475762</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row47_col2\" class=\"data row47 col2\" >0.12746</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row47_col3\" class=\"data row47 col3\" >0.0231122</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row47_col4\" class=\"data row47 col4\" >9.73029</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row47_col5\" class=\"data row47 col5\" >RF with top 50 drop-col-important features, n_trees = 700</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row48_col0\" class=\"data row48 col0\" >RF - Drop-Column Important Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row48_col1\" class=\"data row48 col1\" >0.0474601</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row48_col2\" class=\"data row48 col2\" >0.127567</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row48_col3\" class=\"data row48 col3\" >0.0232217</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row48_col4\" class=\"data row48 col4\" >11.2516</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row48_col5\" class=\"data row48 col5\" >RF with top 50 drop-col-important features, n_trees = 800</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row49_col0\" class=\"data row49 col0\" >RF - Drop-Column Important Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row49_col1\" class=\"data row49 col1\" >0.0474259</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row49_col2\" class=\"data row49 col2\" >0.12767</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row49_col3\" class=\"data row49 col3\" >0.0234329</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row49_col4\" class=\"data row49 col4\" >12.8316</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row49_col5\" class=\"data row49 col5\" >RF with top 50 drop-col-important features, n_trees = 900</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625level0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row50_col0\" class=\"data row50 col0\" >RF - Drop-Column Important Features</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row50_col1\" class=\"data row50 col1\" >0.0475121</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row50_col2\" class=\"data row50 col2\" >0.127651</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row50_col3\" class=\"data row50 col3\" >0.0232719</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row50_col4\" class=\"data row50 col4\" >14.1022</td>\n",
       "                        <td id=\"T_95cb94fa_c370_11ea_b2d2_3024323c1625row50_col5\" class=\"data row50 col5\" >RF with top 50 drop-col-important features, n_trees = 1000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x18c9cf6a160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(result_df.reset_index().drop(['index'], axis=1).style.background_gradient(cmap='summer_r'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will find that\n",
    "\n",
    "- almost every model we trained (except for the set of models to use the tiny subset of features selected  via embedded feature  selection method)\n",
    "- feature importance scores calculated by RF algorithm directly are really misleading, and they do not reflect the actual feature importance for the model trained\n",
    "- the reason why embedded feature selection led to a poor result (see the chart above) can be explained by the fact RF needs reasonable variance in the feature space (to train diverse set of poor predictors - individual decision tree model estimators - to represent the  regression problem  complexity in a proper way)\n",
    "- among the bunch of models we trained, the best performance on the CV testing sets (withe testing score of 0.127509) was demonstrated by the model that used top 50 RFE features and n_trees = 800\n",
    "- in this case, the appropriate feature selection not only improved the interpretability of our model but also added the edge in its forecasting performance (vs. the set of RF models that used the entire set of features in training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References\n",
    "\n",
    "You can refer to the blog posts below if you like to undertake the deeper dive into industrial feature selection/feature importance calculation techniques\n",
    "\n",
    "- Feature Selection with pandas - https://towardsdatascience.com/feature-selection-with-pandas-e3690ad8504b \n",
    "- 5 feature selection methods every data scientist need to know - https://towardsdatascience.com/the-5-feature-selection-algorithms-every-data-scientist-need-to-know-3a6b566efd2 \n",
    "- Feature Importance May Be Lying to You - https://towardsdatascience.com/feature-importance-may-be-lying-to-you-3247cafa7ee7 \n",
    "- Beware Default Random Forest Importance - https://explained.ai/rf-importance/ \n",
    "\n"
   ]
  }
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