adaboost implementation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "16dbada3-9b15-4303-856d-510becd9542b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_circles\n",
    "from sklearn.tree import plot_tree\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5521a0ca-b147-4012-8dbc-8e2ddbb018f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "x,y=make_circles( n_samples=1000,factor=0.1,noise=0.35,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "af318989-d8ab-405e-81d0-6f4ba4821916",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1661c5eeb70>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x[:,0],x[:,1],c=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "45fae671-bd52-45f9-a18f-41e23b11c9db",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6f09eb3d-5f69-4099-9d47-a0005e7ef740",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8240000000000001"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adaboost=AdaBoostClassifier()\n",
    "np.mean(cross_val_score(adaboost,x,y,scoring=\"accuracy\",cv=10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c07e05d5-ae1c-47e8-9863-375884ecd73b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ab=AdaBoostClassifier(n_estimators=500,learning_rate=0.1,algorithm='SAMME.R',)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "dd85b0c6-6108-45bb-a366-03acfa9466b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f147bde3-7a5f-4c99-be1c-8f6a42537661",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(learning_rate=0.1, n_estimators=500)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;AdaBoostClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.AdaBoostClassifier.html\">?<span>Documentation for AdaBoostClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>AdaBoostClassifier(learning_rate=0.1, n_estimators=500)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "AdaBoostClassifier(learning_rate=0.1, n_estimators=500)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ab.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0f4d056d-e90a-42c8-8da8-91b9d44079c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=ab.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2d915507-e1cf-4501-94a8-9d1806147021",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6f3b655c-fc2f-4629-b771-2f486a5499ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.86"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "903f24e8-448d-4a5f-9adb-c1404b63ccac",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.82, 0.88, 0.83, 0.87, 0.84, 0.85, 0.78, 0.83, 0.89, 0.86])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(ab,x,y,scoring=\"accuracy\",cv=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c6313af-80c4-474a-a80a-55574b0e2e42",
   "metadata": {},
   "source": [
    "<!-- Grid search cv -->"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "689c13f1-ed10-4c2d-8848-cd4affa8e0a5",
   "metadata": {},
   "source": [
    "# grid search cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "9ba3a2dd-48bd-43b8-b3bf-58d2f8067e8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "grid=dict()\n",
    "grid[\"n_estimators\"] = [10,50,100,500]\n",
    "grid[\"learning_rate\"] =[0.001,0.01,0.1]\n",
    "grid[\"algorithm\"] = [\"SAMME\",\"SAMME.R\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "bc001d02-dbdb-4e37-9749-c28f722783b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bets score wwe have: 0.849 The best parameters are: AdaBoostClassifier(learning_rate=0.1, n_estimators=100)\n"
     ]
    }
   ],
   "source": [
    "grid_search=GridSearchCV(estimator=AdaBoostClassifier(),param_grid=grid,n_jobs=1,cv=10,scoring=\"accuracy\")\n",
    "grid_result = grid_search.fit(x, y)\n",
    "print(\"bets score wwe have:\",grid_result.best_score_,\"The best parameters are:\",grid_result.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "7a8e7627-6fd9-42e2-bd96-014b70a68123",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'algorithm': 'SAMME.R', 'learning_rate': 0.1, 'n_estimators': 100}\n"
     ]
    }
   ],
   "source": [
    "print(grid_result.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72550fd4-5386-4267-839a-7889c1f66529",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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