2_pycaret_test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic dataset - Binary classification\n",
    "\n",
    "### Pycaret test\n",
    "\n",
    "Mainly following the pycaret documentation [example](https://github.com/pycaret/pycaret/blob/master/tutorials/Tutorial%20-%20Binary%20Classification.ipynb)\n",
    "\n",
    "The Titanic dataset present a binary classification problem, where the goal is to predict whether a passenger survived or not.   \n",
    "In this notebook, I will build a simple Random Forest model to predict the survival of passengers.\n",
    "\n",
    "Will import the data cleaned in the previous step, with the following features:\n",
    "\n",
    "| Variable | Definition                          | Value                        |\n",
    "|----------|-------------------------------------|----------------------------|\n",
    "| survival | Survival                            | 0=No, 1=Yes            |\n",
    "| pclass   | Ticket class                        | 1=1st, 2=2nd, 3=3rd  |\n",
    "| sex      | Sex                                 | 0=Female, 1=Male                           |\n",
    "| Age      | Age in years                        |                            |\n",
    "| sibsp    | # of siblings / spouses aboard the Titanic |                    |\n",
    "| parch    | # of parents / children aboard the Titanic |                    |\n",
    "| fare     | Passenger fare                      |                            |\n",
    "| cabin    | Cabin number                        | 0=NaN/Unidentified, 1=Yes/Valid Cabin nr                           |\n",
    "| embarked | Port of Embarkation                 | 0=Cherbourg, 1=Queenstown, 2=Southampton |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: 2024-05-28T20:07:12.438317+02:00\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.11.6\n",
      "IPython version      : 8.24.0\n",
      "\n",
      "Compiler    : MSC v.1935 64 bit (AMD64)\n",
      "OS          : Windows\n",
      "Release     : 10\n",
      "Machine     : AMD64\n",
      "Processor   : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel\n",
      "CPU cores   : 12\n",
      "Architecture: 64bit\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%reload_ext watermark\n",
    "%watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Watermark: 2.4.3\n",
      "\n",
      "numpy     : 1.26.4\n",
      "pycaret   : 3.3.2\n",
      "matplotlib: 3.7.5\n",
      "seaborn   : 0.13.2\n",
      "pandas    : 2.1.4\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pycaret\n",
    "\n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
    "%watermark -w\n",
    "%watermark -iv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and inspect data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dataset shape (rows, columns): (891, 12)\n",
      "Test dataset shape (rows, columns): (418, 11)\n"
     ]
    },
    {
     "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Sex_1</th>\n",
       "      <th>Cabin_1</th>\n",
       "      <th>Embarked_1</th>\n",
       "      <th>Embarked_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>0.48</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.48</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived   Age  Fare  SibSp  Parch  Pclass_2  Pclass_3  Sex_1  \\\n",
       "0            1         0 -0.58 -0.50   0.48  -0.44     False      True   True   \n",
       "1            2         1  0.66  0.73   0.48  -0.44     False     False  False   \n",
       "2            3         1 -0.27 -0.49  -0.48  -0.44     False      True  False   \n",
       "\n",
       "   Cabin_1  Embarked_1  Embarked_2  \n",
       "0    False       False        True  \n",
       "1     True       False       False  \n",
       "2    False       False        True  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load cleaned data\n",
    "train_data = pd.read_csv('data/train_clean_allFeatures.csv')\n",
    "test_data = pd.read_csv('data/test_clean_allFeatures.csv')\n",
    "\n",
    "# print the shape of the data\n",
    "print('Train dataset shape (rows, columns):', train_data.shape)\n",
    "print('Test dataset shape (rows, columns):', test_data.shape)\n",
    "train_data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pycaret setup\n",
    "\n",
    "Will use the Pycaret library to quickly compare different models and tune hyperparameters.   \n",
    "Pycaret is a low-code machine learning library that automates the machine learning workflow. It is an open-source project that is built on top of popular machine learning libraries, including scikit-learn, XGBoost, and LightGBM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Sex_1</th>\n",
       "      <th>Cabin_1</th>\n",
       "      <th>Embarked_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>0.48</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.48</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age  Fare  SibSp  Pclass_3  Sex_1  Cabin_1  Embarked_2\n",
       "0         0 -0.58 -0.50   0.48      True   True    False        True\n",
       "1         1  0.66  0.73   0.48     False  False     True       False\n",
       "2         1 -0.27 -0.49  -0.48      True  False    False        True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train_data minus 'PassengerId' and low-importance features\n",
    "train_data = train_data.drop(['PassengerId', 'Parch', 'Embarked_1', 'Pclass_2'], axis=1)\n",
    "train_data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_f83ae_row8_col1 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_f83ae\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_f83ae_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
       "      <th id=\"T_f83ae_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_f83ae_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_f83ae_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
       "      <td id=\"T_f83ae_row0_col1\" class=\"data row0 col1\" >123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f83ae_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_f83ae_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "      <td id=\"T_f83ae_row1_col1\" class=\"data row1 col1\" >Survived</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f83ae_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_f83ae_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
       "      <td id=\"T_f83ae_row2_col1\" class=\"data row2 col1\" >Binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f83ae_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_f83ae_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
       "      <td id=\"T_f83ae_row3_col1\" class=\"data row3 col1\" >(891, 8)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f83ae_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_f83ae_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
       "      <td id=\"T_f83ae_row4_col1\" class=\"data row4 col1\" >(891, 8)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f83ae_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_f83ae_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
       "      <td id=\"T_f83ae_row5_col1\" class=\"data row5 col1\" >(623, 8)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f83ae_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_f83ae_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
       "      <td id=\"T_f83ae_row6_col1\" class=\"data row6 col1\" >(268, 8)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f83ae_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_f83ae_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
       "      <td id=\"T_f83ae_row7_col1\" class=\"data row7 col1\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f83ae_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_f83ae_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
       "      <td id=\"T_f83ae_row8_col1\" class=\"data row8 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f83ae_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_f83ae_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
       "      <td id=\"T_f83ae_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f83ae_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_f83ae_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
       "      <td id=\"T_f83ae_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f83ae_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_f83ae_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
       "      <td id=\"T_f83ae_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f83ae_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_f83ae_row12_col0\" class=\"data row12 col0\" >Fold Generator</td>\n",
       "      <td id=\"T_f83ae_row12_col1\" class=\"data row12 col1\" >StratifiedKFold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f83ae_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_f83ae_row13_col0\" class=\"data row13 col0\" >Fold Number</td>\n",
       "      <td id=\"T_f83ae_row13_col1\" class=\"data row13 col1\" >10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f83ae_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_f83ae_row14_col0\" class=\"data row14 col0\" >CPU Jobs</td>\n",
       "      <td id=\"T_f83ae_row14_col1\" class=\"data row14 col1\" >-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f83ae_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_f83ae_row15_col0\" class=\"data row15 col0\" >Use GPU</td>\n",
       "      <td id=\"T_f83ae_row15_col1\" class=\"data row15 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f83ae_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_f83ae_row16_col0\" class=\"data row16 col0\" >Log Experiment</td>\n",
       "      <td id=\"T_f83ae_row16_col1\" class=\"data row16 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f83ae_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_f83ae_row17_col0\" class=\"data row17 col0\" >Experiment Name</td>\n",
       "      <td id=\"T_f83ae_row17_col1\" class=\"data row17 col1\" >clf-default-name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f83ae_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_f83ae_row18_col0\" class=\"data row18 col0\" >USI</td>\n",
       "      <td id=\"T_f83ae_row18_col1\" class=\"data row18 col1\" >81c8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1bfb3f0dd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import pycaret classification and init setup\n",
    "from pycaret.classification import *\n",
    "\n",
    "# setup the environment\n",
    "s = setup(data=train_data, target='Survived', session_id=123)\n",
    "\n",
    "# s = setup(data=train_data, target='Survived', session_id=123, silent=True, \n",
    "#           train_size=0.8, normalize=True, transformation=True, fix_imbalance=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pycaret.classification.oop.ClassificationExperiment"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import ClassificationExperiment and init the class\n",
    "from pycaret.classification import ClassificationExperiment\n",
    "exp = ClassificationExperiment()\n",
    "type(exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_743f4_row8_col1 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_743f4\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_743f4_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
       "      <th id=\"T_743f4_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_743f4_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_743f4_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
       "      <td id=\"T_743f4_row0_col1\" class=\"data row0 col1\" >123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_743f4_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_743f4_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "      <td id=\"T_743f4_row1_col1\" class=\"data row1 col1\" >Survived</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_743f4_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_743f4_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
       "      <td id=\"T_743f4_row2_col1\" class=\"data row2 col1\" >Binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_743f4_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_743f4_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
       "      <td id=\"T_743f4_row3_col1\" class=\"data row3 col1\" >(891, 8)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_743f4_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_743f4_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
       "      <td id=\"T_743f4_row4_col1\" class=\"data row4 col1\" >(891, 8)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_743f4_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_743f4_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
       "      <td id=\"T_743f4_row5_col1\" class=\"data row5 col1\" >(623, 8)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_743f4_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_743f4_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
       "      <td id=\"T_743f4_row6_col1\" class=\"data row6 col1\" >(268, 8)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_743f4_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_743f4_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
       "      <td id=\"T_743f4_row7_col1\" class=\"data row7 col1\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_743f4_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_743f4_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
       "      <td id=\"T_743f4_row8_col1\" class=\"data row8 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_743f4_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_743f4_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
       "      <td id=\"T_743f4_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_743f4_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_743f4_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
       "      <td id=\"T_743f4_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_743f4_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_743f4_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
       "      <td id=\"T_743f4_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_743f4_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_743f4_row12_col0\" class=\"data row12 col0\" >Fold Generator</td>\n",
       "      <td id=\"T_743f4_row12_col1\" class=\"data row12 col1\" >StratifiedKFold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_743f4_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_743f4_row13_col0\" class=\"data row13 col0\" >Fold Number</td>\n",
       "      <td id=\"T_743f4_row13_col1\" class=\"data row13 col1\" >10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_743f4_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_743f4_row14_col0\" class=\"data row14 col0\" >CPU Jobs</td>\n",
       "      <td id=\"T_743f4_row14_col1\" class=\"data row14 col1\" >-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_743f4_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_743f4_row15_col0\" class=\"data row15 col0\" >Use GPU</td>\n",
       "      <td id=\"T_743f4_row15_col1\" class=\"data row15 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_743f4_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_743f4_row16_col0\" class=\"data row16 col0\" >Log Experiment</td>\n",
       "      <td id=\"T_743f4_row16_col1\" class=\"data row16 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_743f4_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_743f4_row17_col0\" class=\"data row17 col0\" >Experiment Name</td>\n",
       "      <td id=\"T_743f4_row17_col1\" class=\"data row17 col1\" >clf-default-name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_743f4_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_743f4_row18_col0\" class=\"data row18 col0\" >USI</td>\n",
       "      <td id=\"T_743f4_row18_col1\" class=\"data row18 col1\" >15a0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1e480c5fd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<pycaret.classification.oop.ClassificationExperiment at 0x1e48061e5d0>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# init setup on exp\n",
    "exp.setup(data=train_data, target='Survived', session_id=123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare models\n",
    "This function trains and evaluates the performance of all the estimators available in the model library using cross-validation. The output of this function is a scoring grid with average cross-validated scores. Metrics evaluated during CV can be accessed using the get_metrics function. Custom metrics can be added or removed using ``add_metric`` and ``remove_metric`` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_94280 th {\n",
       "  text-align: left;\n",
       "}\n",
       "#T_94280_row0_col0, #T_94280_row0_col2, #T_94280_row0_col3, #T_94280_row1_col0, #T_94280_row1_col1, #T_94280_row1_col2, #T_94280_row1_col3, #T_94280_row1_col4, #T_94280_row1_col5, #T_94280_row1_col6, #T_94280_row1_col7, #T_94280_row2_col0, #T_94280_row2_col1, #T_94280_row2_col2, #T_94280_row2_col3, #T_94280_row2_col4, #T_94280_row2_col5, #T_94280_row2_col6, #T_94280_row2_col7, #T_94280_row3_col0, #T_94280_row3_col1, #T_94280_row3_col2, #T_94280_row3_col3, #T_94280_row3_col4, #T_94280_row3_col5, #T_94280_row3_col6, #T_94280_row3_col7, #T_94280_row4_col0, #T_94280_row4_col1, #T_94280_row4_col2, #T_94280_row4_col3, #T_94280_row4_col4, #T_94280_row4_col5, #T_94280_row4_col6, #T_94280_row4_col7, #T_94280_row5_col0, #T_94280_row5_col1, #T_94280_row5_col2, #T_94280_row5_col3, #T_94280_row5_col4, #T_94280_row5_col5, #T_94280_row5_col6, #T_94280_row5_col7, #T_94280_row6_col0, #T_94280_row6_col1, #T_94280_row6_col3, #T_94280_row6_col4, #T_94280_row6_col5, #T_94280_row6_col6, #T_94280_row6_col7, #T_94280_row7_col0, #T_94280_row7_col1, #T_94280_row7_col2, #T_94280_row7_col3, #T_94280_row7_col4, #T_94280_row7_col5, #T_94280_row7_col6, #T_94280_row7_col7, #T_94280_row8_col0, #T_94280_row8_col1, #T_94280_row8_col2, #T_94280_row8_col3, #T_94280_row8_col4, #T_94280_row8_col5, #T_94280_row8_col6, #T_94280_row8_col7, #T_94280_row9_col0, #T_94280_row9_col1, #T_94280_row9_col2, #T_94280_row9_col3, #T_94280_row9_col4, #T_94280_row9_col5, #T_94280_row9_col6, #T_94280_row9_col7, #T_94280_row10_col0, #T_94280_row10_col1, #T_94280_row10_col2, #T_94280_row10_col3, #T_94280_row10_col4, #T_94280_row10_col5, #T_94280_row10_col6, #T_94280_row10_col7, #T_94280_row11_col0, #T_94280_row11_col1, #T_94280_row11_col2, #T_94280_row11_col3, #T_94280_row11_col4, #T_94280_row11_col5, #T_94280_row11_col6, #T_94280_row11_col7, #T_94280_row12_col0, #T_94280_row12_col1, #T_94280_row12_col2, #T_94280_row12_col4, #T_94280_row12_col5, #T_94280_row12_col6, #T_94280_row12_col7, #T_94280_row13_col0, #T_94280_row13_col1, #T_94280_row13_col2, #T_94280_row13_col3, #T_94280_row13_col4, #T_94280_row13_col5, #T_94280_row13_col6, #T_94280_row13_col7, #T_94280_row14_col0, #T_94280_row14_col1, #T_94280_row14_col2, #T_94280_row14_col3, #T_94280_row14_col4, #T_94280_row14_col5, #T_94280_row14_col6, #T_94280_row14_col7 {\n",
       "  text-align: left;\n",
       "}\n",
       "#T_94280_row0_col1, #T_94280_row0_col4, #T_94280_row0_col5, #T_94280_row0_col6, #T_94280_row0_col7, #T_94280_row6_col2, #T_94280_row12_col3 {\n",
       "  text-align: left;\n",
       "  background-color: yellow;\n",
       "}\n",
       "#T_94280_row0_col8, #T_94280_row1_col8, #T_94280_row2_col8, #T_94280_row3_col8, #T_94280_row4_col8, #T_94280_row5_col8, #T_94280_row6_col8, #T_94280_row7_col8, #T_94280_row9_col8, #T_94280_row10_col8, #T_94280_row11_col8, #T_94280_row12_col8, #T_94280_row14_col8 {\n",
       "  text-align: left;\n",
       "  background-color: lightgrey;\n",
       "}\n",
       "#T_94280_row8_col8, #T_94280_row13_col8 {\n",
       "  text-align: left;\n",
       "  background-color: yellow;\n",
       "  background-color: lightgrey;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_94280\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_94280_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_94280_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th id=\"T_94280_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
       "      <th id=\"T_94280_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th id=\"T_94280_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
       "      <th id=\"T_94280_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
       "      <th id=\"T_94280_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
       "      <th id=\"T_94280_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
       "      <th id=\"T_94280_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_94280_level0_row0\" class=\"row_heading level0 row0\" >gbc</th>\n",
       "      <td id=\"T_94280_row0_col0\" class=\"data row0 col0\" >Gradient Boosting Classifier</td>\n",
       "      <td id=\"T_94280_row0_col1\" class=\"data row0 col1\" >0.8250</td>\n",
       "      <td id=\"T_94280_row0_col2\" class=\"data row0 col2\" >0.8439</td>\n",
       "      <td id=\"T_94280_row0_col3\" class=\"data row0 col3\" >0.7279</td>\n",
       "      <td id=\"T_94280_row0_col4\" class=\"data row0 col4\" >0.7980</td>\n",
       "      <td id=\"T_94280_row0_col5\" class=\"data row0 col5\" >0.7590</td>\n",
       "      <td id=\"T_94280_row0_col6\" class=\"data row0 col6\" >0.6225</td>\n",
       "      <td id=\"T_94280_row0_col7\" class=\"data row0 col7\" >0.6262</td>\n",
       "      <td id=\"T_94280_row0_col8\" class=\"data row0 col8\" >0.0300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94280_level0_row1\" class=\"row_heading level0 row1\" >lightgbm</th>\n",
       "      <td id=\"T_94280_row1_col0\" class=\"data row1 col0\" >Light Gradient Boosting Machine</td>\n",
       "      <td id=\"T_94280_row1_col1\" class=\"data row1 col1\" >0.8202</td>\n",
       "      <td id=\"T_94280_row1_col2\" class=\"data row1 col2\" >0.8423</td>\n",
       "      <td id=\"T_94280_row1_col3\" class=\"data row1 col3\" >0.7362</td>\n",
       "      <td id=\"T_94280_row1_col4\" class=\"data row1 col4\" >0.7862</td>\n",
       "      <td id=\"T_94280_row1_col5\" class=\"data row1 col5\" >0.7565</td>\n",
       "      <td id=\"T_94280_row1_col6\" class=\"data row1 col6\" >0.6147</td>\n",
       "      <td id=\"T_94280_row1_col7\" class=\"data row1 col7\" >0.6191</td>\n",
       "      <td id=\"T_94280_row1_col8\" class=\"data row1 col8\" >0.0980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94280_level0_row2\" class=\"row_heading level0 row2\" >rf</th>\n",
       "      <td id=\"T_94280_row2_col0\" class=\"data row2 col0\" >Random Forest Classifier</td>\n",
       "      <td id=\"T_94280_row2_col1\" class=\"data row2 col1\" >0.8169</td>\n",
       "      <td id=\"T_94280_row2_col2\" class=\"data row2 col2\" >0.8454</td>\n",
       "      <td id=\"T_94280_row2_col3\" class=\"data row2 col3\" >0.7236</td>\n",
       "      <td id=\"T_94280_row2_col4\" class=\"data row2 col4\" >0.7846</td>\n",
       "      <td id=\"T_94280_row2_col5\" class=\"data row2 col5\" >0.7502</td>\n",
       "      <td id=\"T_94280_row2_col6\" class=\"data row2 col6\" >0.6065</td>\n",
       "      <td id=\"T_94280_row2_col7\" class=\"data row2 col7\" >0.6101</td>\n",
       "      <td id=\"T_94280_row2_col8\" class=\"data row2 col8\" >0.0590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94280_level0_row3\" class=\"row_heading level0 row3\" >ada</th>\n",
       "      <td id=\"T_94280_row3_col0\" class=\"data row3 col0\" >Ada Boost Classifier</td>\n",
       "      <td id=\"T_94280_row3_col1\" class=\"data row3 col1\" >0.8105</td>\n",
       "      <td id=\"T_94280_row3_col2\" class=\"data row3 col2\" >0.8439</td>\n",
       "      <td id=\"T_94280_row3_col3\" class=\"data row3 col3\" >0.7533</td>\n",
       "      <td id=\"T_94280_row3_col4\" class=\"data row3 col4\" >0.7551</td>\n",
       "      <td id=\"T_94280_row3_col5\" class=\"data row3 col5\" >0.7524</td>\n",
       "      <td id=\"T_94280_row3_col6\" class=\"data row3 col6\" >0.5991</td>\n",
       "      <td id=\"T_94280_row3_col7\" class=\"data row3 col7\" >0.6010</td>\n",
       "      <td id=\"T_94280_row3_col8\" class=\"data row3 col8\" >0.0240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94280_level0_row4\" class=\"row_heading level0 row4\" >xgboost</th>\n",
       "      <td id=\"T_94280_row4_col0\" class=\"data row4 col0\" >Extreme Gradient Boosting</td>\n",
       "      <td id=\"T_94280_row4_col1\" class=\"data row4 col1\" >0.8091</td>\n",
       "      <td id=\"T_94280_row4_col2\" class=\"data row4 col2\" >0.8373</td>\n",
       "      <td id=\"T_94280_row4_col3\" class=\"data row4 col3\" >0.7154</td>\n",
       "      <td id=\"T_94280_row4_col4\" class=\"data row4 col4\" >0.7682</td>\n",
       "      <td id=\"T_94280_row4_col5\" class=\"data row4 col5\" >0.7378</td>\n",
       "      <td id=\"T_94280_row4_col6\" class=\"data row4 col6\" >0.5887</td>\n",
       "      <td id=\"T_94280_row4_col7\" class=\"data row4 col7\" >0.5921</td>\n",
       "      <td id=\"T_94280_row4_col8\" class=\"data row4 col8\" >0.0290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94280_level0_row5\" class=\"row_heading level0 row5\" >lda</th>\n",
       "      <td id=\"T_94280_row5_col0\" class=\"data row5 col0\" >Linear Discriminant Analysis</td>\n",
       "      <td id=\"T_94280_row5_col1\" class=\"data row5 col1\" >0.8088</td>\n",
       "      <td id=\"T_94280_row5_col2\" class=\"data row5 col2\" >0.8608</td>\n",
       "      <td id=\"T_94280_row5_col3\" class=\"data row5 col3\" >0.7281</td>\n",
       "      <td id=\"T_94280_row5_col4\" class=\"data row5 col4\" >0.7642</td>\n",
       "      <td id=\"T_94280_row5_col5\" class=\"data row5 col5\" >0.7429</td>\n",
       "      <td id=\"T_94280_row5_col6\" class=\"data row5 col6\" >0.5913</td>\n",
       "      <td id=\"T_94280_row5_col7\" class=\"data row5 col7\" >0.5944</td>\n",
       "      <td id=\"T_94280_row5_col8\" class=\"data row5 col8\" >0.0140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94280_level0_row6\" class=\"row_heading level0 row6\" >lr</th>\n",
       "      <td id=\"T_94280_row6_col0\" class=\"data row6 col0\" >Logistic Regression</td>\n",
       "      <td id=\"T_94280_row6_col1\" class=\"data row6 col1\" >0.8071</td>\n",
       "      <td id=\"T_94280_row6_col2\" class=\"data row6 col2\" >0.8624</td>\n",
       "      <td id=\"T_94280_row6_col3\" class=\"data row6 col3\" >0.7239</td>\n",
       "      <td id=\"T_94280_row6_col4\" class=\"data row6 col4\" >0.7642</td>\n",
       "      <td id=\"T_94280_row6_col5\" class=\"data row6 col5\" >0.7395</td>\n",
       "      <td id=\"T_94280_row6_col6\" class=\"data row6 col6\" >0.5873</td>\n",
       "      <td id=\"T_94280_row6_col7\" class=\"data row6 col7\" >0.5914</td>\n",
       "      <td id=\"T_94280_row6_col8\" class=\"data row6 col8\" >0.5040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94280_level0_row7\" class=\"row_heading level0 row7\" >ridge</th>\n",
       "      <td id=\"T_94280_row7_col0\" class=\"data row7 col0\" >Ridge Classifier</td>\n",
       "      <td id=\"T_94280_row7_col1\" class=\"data row7 col1\" >0.8056</td>\n",
       "      <td id=\"T_94280_row7_col2\" class=\"data row7 col2\" >0.8610</td>\n",
       "      <td id=\"T_94280_row7_col3\" class=\"data row7 col3\" >0.7197</td>\n",
       "      <td id=\"T_94280_row7_col4\" class=\"data row7 col4\" >0.7610</td>\n",
       "      <td id=\"T_94280_row7_col5\" class=\"data row7 col5\" >0.7364</td>\n",
       "      <td id=\"T_94280_row7_col6\" class=\"data row7 col6\" >0.5833</td>\n",
       "      <td id=\"T_94280_row7_col7\" class=\"data row7 col7\" >0.5868</td>\n",
       "      <td id=\"T_94280_row7_col8\" class=\"data row7 col8\" >0.0090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94280_level0_row8\" class=\"row_heading level0 row8\" >qda</th>\n",
       "      <td id=\"T_94280_row8_col0\" class=\"data row8 col0\" >Quadratic Discriminant Analysis</td>\n",
       "      <td id=\"T_94280_row8_col1\" class=\"data row8 col1\" >0.7977</td>\n",
       "      <td id=\"T_94280_row8_col2\" class=\"data row8 col2\" >0.8487</td>\n",
       "      <td id=\"T_94280_row8_col3\" class=\"data row8 col3\" >0.7196</td>\n",
       "      <td id=\"T_94280_row8_col4\" class=\"data row8 col4\" >0.7528</td>\n",
       "      <td id=\"T_94280_row8_col5\" class=\"data row8 col5\" >0.7296</td>\n",
       "      <td id=\"T_94280_row8_col6\" class=\"data row8 col6\" >0.5691</td>\n",
       "      <td id=\"T_94280_row8_col7\" class=\"data row8 col7\" >0.5754</td>\n",
       "      <td id=\"T_94280_row8_col8\" class=\"data row8 col8\" >0.0070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94280_level0_row9\" class=\"row_heading level0 row9\" >et</th>\n",
       "      <td id=\"T_94280_row9_col0\" class=\"data row9 col0\" >Extra Trees Classifier</td>\n",
       "      <td id=\"T_94280_row9_col1\" class=\"data row9 col1\" >0.7944</td>\n",
       "      <td id=\"T_94280_row9_col2\" class=\"data row9 col2\" >0.8317</td>\n",
       "      <td id=\"T_94280_row9_col3\" class=\"data row9 col3\" >0.7067</td>\n",
       "      <td id=\"T_94280_row9_col4\" class=\"data row9 col4\" >0.7478</td>\n",
       "      <td id=\"T_94280_row9_col5\" class=\"data row9 col5\" >0.7250</td>\n",
       "      <td id=\"T_94280_row9_col6\" class=\"data row9 col6\" >0.5611</td>\n",
       "      <td id=\"T_94280_row9_col7\" class=\"data row9 col7\" >0.5632</td>\n",
       "      <td id=\"T_94280_row9_col8\" class=\"data row9 col8\" >0.0530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94280_level0_row10\" class=\"row_heading level0 row10\" >knn</th>\n",
       "      <td id=\"T_94280_row10_col0\" class=\"data row10 col0\" >K Neighbors Classifier</td>\n",
       "      <td id=\"T_94280_row10_col1\" class=\"data row10 col1\" >0.7912</td>\n",
       "      <td id=\"T_94280_row10_col2\" class=\"data row10 col2\" >0.8442</td>\n",
       "      <td id=\"T_94280_row10_col3\" class=\"data row10 col3\" >0.6944</td>\n",
       "      <td id=\"T_94280_row10_col4\" class=\"data row10 col4\" >0.7487</td>\n",
       "      <td id=\"T_94280_row10_col5\" class=\"data row10 col5\" >0.7134</td>\n",
       "      <td id=\"T_94280_row10_col6\" class=\"data row10 col6\" >0.5509</td>\n",
       "      <td id=\"T_94280_row10_col7\" class=\"data row10 col7\" >0.5583</td>\n",
       "      <td id=\"T_94280_row10_col8\" class=\"data row10 col8\" >0.2240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94280_level0_row11\" class=\"row_heading level0 row11\" >svm</th>\n",
       "      <td id=\"T_94280_row11_col0\" class=\"data row11 col0\" >SVM - Linear Kernel</td>\n",
       "      <td id=\"T_94280_row11_col1\" class=\"data row11 col1\" >0.7815</td>\n",
       "      <td id=\"T_94280_row11_col2\" class=\"data row11 col2\" >0.8287</td>\n",
       "      <td id=\"T_94280_row11_col3\" class=\"data row11 col3\" >0.7027</td>\n",
       "      <td id=\"T_94280_row11_col4\" class=\"data row11 col4\" >0.7318</td>\n",
       "      <td id=\"T_94280_row11_col5\" class=\"data row11 col5\" >0.7091</td>\n",
       "      <td id=\"T_94280_row11_col6\" class=\"data row11 col6\" >0.5358</td>\n",
       "      <td id=\"T_94280_row11_col7\" class=\"data row11 col7\" >0.5438</td>\n",
       "      <td id=\"T_94280_row11_col8\" class=\"data row11 col8\" >0.0080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94280_level0_row12\" class=\"row_heading level0 row12\" >nb</th>\n",
       "      <td id=\"T_94280_row12_col0\" class=\"data row12 col0\" >Naive Bayes</td>\n",
       "      <td id=\"T_94280_row12_col1\" class=\"data row12 col1\" >0.7753</td>\n",
       "      <td id=\"T_94280_row12_col2\" class=\"data row12 col2\" >0.8305</td>\n",
       "      <td id=\"T_94280_row12_col3\" class=\"data row12 col3\" >0.7574</td>\n",
       "      <td id=\"T_94280_row12_col4\" class=\"data row12 col4\" >0.6928</td>\n",
       "      <td id=\"T_94280_row12_col5\" class=\"data row12 col5\" >0.7197</td>\n",
       "      <td id=\"T_94280_row12_col6\" class=\"data row12 col6\" >0.5335</td>\n",
       "      <td id=\"T_94280_row12_col7\" class=\"data row12 col7\" >0.5400</td>\n",
       "      <td id=\"T_94280_row12_col8\" class=\"data row12 col8\" >0.0080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94280_level0_row13\" class=\"row_heading level0 row13\" >dt</th>\n",
       "      <td id=\"T_94280_row13_col0\" class=\"data row13 col0\" >Decision Tree Classifier</td>\n",
       "      <td id=\"T_94280_row13_col1\" class=\"data row13 col1\" >0.7737</td>\n",
       "      <td id=\"T_94280_row13_col2\" class=\"data row13 col2\" >0.7521</td>\n",
       "      <td id=\"T_94280_row13_col3\" class=\"data row13 col3\" >0.7029</td>\n",
       "      <td id=\"T_94280_row13_col4\" class=\"data row13 col4\" >0.7076</td>\n",
       "      <td id=\"T_94280_row13_col5\" class=\"data row13 col5\" >0.7035</td>\n",
       "      <td id=\"T_94280_row13_col6\" class=\"data row13 col6\" >0.5207</td>\n",
       "      <td id=\"T_94280_row13_col7\" class=\"data row13 col7\" >0.5224</td>\n",
       "      <td id=\"T_94280_row13_col8\" class=\"data row13 col8\" >0.0070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94280_level0_row14\" class=\"row_heading level0 row14\" >dummy</th>\n",
       "      <td id=\"T_94280_row14_col0\" class=\"data row14 col0\" >Dummy Classifier</td>\n",
       "      <td id=\"T_94280_row14_col1\" class=\"data row14 col1\" >0.6164</td>\n",
       "      <td id=\"T_94280_row14_col2\" class=\"data row14 col2\" >0.5000</td>\n",
       "      <td id=\"T_94280_row14_col3\" class=\"data row14 col3\" >0.0000</td>\n",
       "      <td id=\"T_94280_row14_col4\" class=\"data row14 col4\" >0.0000</td>\n",
       "      <td id=\"T_94280_row14_col5\" class=\"data row14 col5\" >0.0000</td>\n",
       "      <td id=\"T_94280_row14_col6\" class=\"data row14 col6\" >0.0000</td>\n",
       "      <td id=\"T_94280_row14_col7\" class=\"data row14 col7\" >0.0000</td>\n",
       "      <td id=\"T_94280_row14_col8\" class=\"data row14 col8\" >0.0090</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1e4fa9b3f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare baseline models\n",
    "best = compare_models()\n",
    "# exp.compare_models() # compare models using OOP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 480x330 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot confusion matrix\n",
    "plot_model(best, plot = 'confusion_matrix', scale=0.6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``pycaret.classification.plot_model()``  \n",
    "See more available plot options from [documentation](https://pycaret.readthedocs.io/en/stable/api/classification.html#pycaret.classification.plot_model)   \n",
    "E.g. `pipeline`, `auc`, `confusion_matrix`, `parameter`, `pr`, `threshold`,  `boundary`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot AUC\n",
    "plot_model(best, plot = 'pr', scale=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot feature importance\n",
    "plot_model(best, plot = 'feature')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An alternate to plot_model function is ``evaluate_model``. It is interactive with many plots, BUT can only be used in Notebook since it uses ipywidget."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cb0cb78d1785424c890e31d3b6cf5ed6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Pipeline Plot', 'pipelin…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model(best)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tune best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_47054_row10_col0, #T_47054_row10_col1, #T_47054_row10_col2, #T_47054_row10_col3, #T_47054_row10_col4, #T_47054_row10_col5, #T_47054_row10_col6 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_47054\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_47054_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th id=\"T_47054_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th id=\"T_47054_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th id=\"T_47054_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th id=\"T_47054_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th id=\"T_47054_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th id=\"T_47054_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_47054_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_47054_row0_col0\" class=\"data row0 col0\" >0.8571</td>\n",
       "      <td id=\"T_47054_row0_col1\" class=\"data row0 col1\" >0.8681</td>\n",
       "      <td id=\"T_47054_row0_col2\" class=\"data row0 col2\" >0.7917</td>\n",
       "      <td id=\"T_47054_row0_col3\" class=\"data row0 col3\" >0.8261</td>\n",
       "      <td id=\"T_47054_row0_col4\" class=\"data row0 col4\" >0.8085</td>\n",
       "      <td id=\"T_47054_row0_col5\" class=\"data row0 col5\" >0.6947</td>\n",
       "      <td id=\"T_47054_row0_col6\" class=\"data row0 col6\" >0.6951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_47054_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_47054_row1_col0\" class=\"data row1 col0\" >0.8571</td>\n",
       "      <td id=\"T_47054_row1_col1\" class=\"data row1 col1\" >0.7997</td>\n",
       "      <td id=\"T_47054_row1_col2\" class=\"data row1 col2\" >0.7917</td>\n",
       "      <td id=\"T_47054_row1_col3\" class=\"data row1 col3\" >0.8261</td>\n",
       "      <td id=\"T_47054_row1_col4\" class=\"data row1 col4\" >0.8085</td>\n",
       "      <td id=\"T_47054_row1_col5\" class=\"data row1 col5\" >0.6947</td>\n",
       "      <td id=\"T_47054_row1_col6\" class=\"data row1 col6\" >0.6951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_47054_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_47054_row2_col0\" class=\"data row2 col0\" >0.7937</td>\n",
       "      <td id=\"T_47054_row2_col1\" class=\"data row2 col1\" >0.9338</td>\n",
       "      <td id=\"T_47054_row2_col2\" class=\"data row2 col2\" >0.6250</td>\n",
       "      <td id=\"T_47054_row2_col3\" class=\"data row2 col3\" >0.7895</td>\n",
       "      <td id=\"T_47054_row2_col4\" class=\"data row2 col4\" >0.6977</td>\n",
       "      <td id=\"T_47054_row2_col5\" class=\"data row2 col5\" >0.5442</td>\n",
       "      <td id=\"T_47054_row2_col6\" class=\"data row2 col6\" >0.5528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_47054_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_47054_row3_col0\" class=\"data row3 col0\" >0.8387</td>\n",
       "      <td id=\"T_47054_row3_col1\" class=\"data row3 col1\" >0.8829</td>\n",
       "      <td id=\"T_47054_row3_col2\" class=\"data row3 col2\" >0.6957</td>\n",
       "      <td id=\"T_47054_row3_col3\" class=\"data row3 col3\" >0.8421</td>\n",
       "      <td id=\"T_47054_row3_col4\" class=\"data row3 col4\" >0.7619</td>\n",
       "      <td id=\"T_47054_row3_col5\" class=\"data row3 col5\" >0.6416</td>\n",
       "      <td id=\"T_47054_row3_col6\" class=\"data row3 col6\" >0.6483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_47054_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_47054_row4_col0\" class=\"data row4 col0\" >0.7258</td>\n",
       "      <td id=\"T_47054_row4_col1\" class=\"data row4 col1\" >0.7018</td>\n",
       "      <td id=\"T_47054_row4_col2\" class=\"data row4 col2\" >0.5417</td>\n",
       "      <td id=\"T_47054_row4_col3\" class=\"data row4 col3\" >0.6842</td>\n",
       "      <td id=\"T_47054_row4_col4\" class=\"data row4 col4\" >0.6047</td>\n",
       "      <td id=\"T_47054_row4_col5\" class=\"data row4 col5\" >0.3991</td>\n",
       "      <td id=\"T_47054_row4_col6\" class=\"data row4 col6\" >0.4055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_47054_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_47054_row5_col0\" class=\"data row5 col0\" >0.8065</td>\n",
       "      <td id=\"T_47054_row5_col1\" class=\"data row5 col1\" >0.8158</td>\n",
       "      <td id=\"T_47054_row5_col2\" class=\"data row5 col2\" >0.6667</td>\n",
       "      <td id=\"T_47054_row5_col3\" class=\"data row5 col3\" >0.8000</td>\n",
       "      <td id=\"T_47054_row5_col4\" class=\"data row5 col4\" >0.7273</td>\n",
       "      <td id=\"T_47054_row5_col5\" class=\"data row5 col5\" >0.5792</td>\n",
       "      <td id=\"T_47054_row5_col6\" class=\"data row5 col6\" >0.5850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_47054_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_47054_row6_col0\" class=\"data row6 col0\" >0.9032</td>\n",
       "      <td id=\"T_47054_row6_col1\" class=\"data row6 col1\" >0.8739</td>\n",
       "      <td id=\"T_47054_row6_col2\" class=\"data row6 col2\" >0.8333</td>\n",
       "      <td id=\"T_47054_row6_col3\" class=\"data row6 col3\" >0.9091</td>\n",
       "      <td id=\"T_47054_row6_col4\" class=\"data row6 col4\" >0.8696</td>\n",
       "      <td id=\"T_47054_row6_col5\" class=\"data row6 col5\" >0.7929</td>\n",
       "      <td id=\"T_47054_row6_col6\" class=\"data row6 col6\" >0.7948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_47054_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_47054_row7_col0\" class=\"data row7 col0\" >0.8548</td>\n",
       "      <td id=\"T_47054_row7_col1\" class=\"data row7 col1\" >0.8311</td>\n",
       "      <td id=\"T_47054_row7_col2\" class=\"data row7 col2\" >0.8333</td>\n",
       "      <td id=\"T_47054_row7_col3\" class=\"data row7 col3\" >0.8000</td>\n",
       "      <td id=\"T_47054_row7_col4\" class=\"data row7 col4\" >0.8163</td>\n",
       "      <td id=\"T_47054_row7_col5\" class=\"data row7 col5\" >0.6964</td>\n",
       "      <td id=\"T_47054_row7_col6\" class=\"data row7 col6\" >0.6968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_47054_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_47054_row8_col0\" class=\"data row8 col0\" >0.7903</td>\n",
       "      <td id=\"T_47054_row8_col1\" class=\"data row8 col1\" >0.8459</td>\n",
       "      <td id=\"T_47054_row8_col2\" class=\"data row8 col2\" >0.6667</td>\n",
       "      <td id=\"T_47054_row8_col3\" class=\"data row8 col3\" >0.7619</td>\n",
       "      <td id=\"T_47054_row8_col4\" class=\"data row8 col4\" >0.7111</td>\n",
       "      <td id=\"T_47054_row8_col5\" class=\"data row8 col5\" >0.5477</td>\n",
       "      <td id=\"T_47054_row8_col6\" class=\"data row8 col6\" >0.5507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_47054_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_47054_row9_col0\" class=\"data row9 col0\" >0.8226</td>\n",
       "      <td id=\"T_47054_row9_col1\" class=\"data row9 col1\" >0.8860</td>\n",
       "      <td id=\"T_47054_row9_col2\" class=\"data row9 col2\" >0.8333</td>\n",
       "      <td id=\"T_47054_row9_col3\" class=\"data row9 col3\" >0.7407</td>\n",
       "      <td id=\"T_47054_row9_col4\" class=\"data row9 col4\" >0.7843</td>\n",
       "      <td id=\"T_47054_row9_col5\" class=\"data row9 col5\" >0.6345</td>\n",
       "      <td id=\"T_47054_row9_col6\" class=\"data row9 col6\" >0.6377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_47054_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_47054_row10_col0\" class=\"data row10 col0\" >0.8250</td>\n",
       "      <td id=\"T_47054_row10_col1\" class=\"data row10 col1\" >0.8439</td>\n",
       "      <td id=\"T_47054_row10_col2\" class=\"data row10 col2\" >0.7279</td>\n",
       "      <td id=\"T_47054_row10_col3\" class=\"data row10 col3\" >0.7980</td>\n",
       "      <td id=\"T_47054_row10_col4\" class=\"data row10 col4\" >0.7590</td>\n",
       "      <td id=\"T_47054_row10_col5\" class=\"data row10 col5\" >0.6225</td>\n",
       "      <td id=\"T_47054_row10_col6\" class=\"data row10 col6\" >0.6262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_47054_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_47054_row11_col0\" class=\"data row11 col0\" >0.0465</td>\n",
       "      <td id=\"T_47054_row11_col1\" class=\"data row11 col1\" >0.0600</td>\n",
       "      <td id=\"T_47054_row11_col2\" class=\"data row11 col2\" >0.0976</td>\n",
       "      <td id=\"T_47054_row11_col3\" class=\"data row11 col3\" >0.0579</td>\n",
       "      <td id=\"T_47054_row11_col4\" class=\"data row11 col4\" >0.0721</td>\n",
       "      <td id=\"T_47054_row11_col5\" class=\"data row11 col5\" >0.1044</td>\n",
       "      <td id=\"T_47054_row11_col6\" class=\"data row11 col6\" >0.1025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1e480f08d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gbc = create_model('gbc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_1bc62_row10_col0, #T_1bc62_row10_col1, #T_1bc62_row10_col2, #T_1bc62_row10_col3, #T_1bc62_row10_col4, #T_1bc62_row10_col5, #T_1bc62_row10_col6 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_1bc62\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_1bc62_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th id=\"T_1bc62_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th id=\"T_1bc62_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th id=\"T_1bc62_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th id=\"T_1bc62_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th id=\"T_1bc62_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th id=\"T_1bc62_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_1bc62_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_1bc62_row0_col0\" class=\"data row0 col0\" >0.8413</td>\n",
       "      <td id=\"T_1bc62_row0_col1\" class=\"data row0 col1\" >0.8371</td>\n",
       "      <td id=\"T_1bc62_row0_col2\" class=\"data row0 col2\" >0.7917</td>\n",
       "      <td id=\"T_1bc62_row0_col3\" class=\"data row0 col3\" >0.7917</td>\n",
       "      <td id=\"T_1bc62_row0_col4\" class=\"data row0 col4\" >0.7917</td>\n",
       "      <td id=\"T_1bc62_row0_col5\" class=\"data row0 col5\" >0.6635</td>\n",
       "      <td id=\"T_1bc62_row0_col6\" class=\"data row0 col6\" >0.6635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1bc62_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_1bc62_row1_col0\" class=\"data row1 col0\" >0.8571</td>\n",
       "      <td id=\"T_1bc62_row1_col1\" class=\"data row1 col1\" >0.8387</td>\n",
       "      <td id=\"T_1bc62_row1_col2\" class=\"data row1 col2\" >0.7917</td>\n",
       "      <td id=\"T_1bc62_row1_col3\" class=\"data row1 col3\" >0.8261</td>\n",
       "      <td id=\"T_1bc62_row1_col4\" class=\"data row1 col4\" >0.8085</td>\n",
       "      <td id=\"T_1bc62_row1_col5\" class=\"data row1 col5\" >0.6947</td>\n",
       "      <td id=\"T_1bc62_row1_col6\" class=\"data row1 col6\" >0.6951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1bc62_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_1bc62_row2_col0\" class=\"data row2 col0\" >0.8254</td>\n",
       "      <td id=\"T_1bc62_row2_col1\" class=\"data row2 col1\" >0.8921</td>\n",
       "      <td id=\"T_1bc62_row2_col2\" class=\"data row2 col2\" >0.6667</td>\n",
       "      <td id=\"T_1bc62_row2_col3\" class=\"data row2 col3\" >0.8421</td>\n",
       "      <td id=\"T_1bc62_row2_col4\" class=\"data row2 col4\" >0.7442</td>\n",
       "      <td id=\"T_1bc62_row2_col5\" class=\"data row2 col5\" >0.6144</td>\n",
       "      <td id=\"T_1bc62_row2_col6\" class=\"data row2 col6\" >0.6240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1bc62_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_1bc62_row3_col0\" class=\"data row3 col0\" >0.8548</td>\n",
       "      <td id=\"T_1bc62_row3_col1\" class=\"data row3 col1\" >0.8941</td>\n",
       "      <td id=\"T_1bc62_row3_col2\" class=\"data row3 col2\" >0.6957</td>\n",
       "      <td id=\"T_1bc62_row3_col3\" class=\"data row3 col3\" >0.8889</td>\n",
       "      <td id=\"T_1bc62_row3_col4\" class=\"data row3 col4\" >0.7805</td>\n",
       "      <td id=\"T_1bc62_row3_col5\" class=\"data row3 col5\" >0.6744</td>\n",
       "      <td id=\"T_1bc62_row3_col6\" class=\"data row3 col6\" >0.6858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1bc62_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_1bc62_row4_col0\" class=\"data row4 col0\" >0.7419</td>\n",
       "      <td id=\"T_1bc62_row4_col1\" class=\"data row4 col1\" >0.7325</td>\n",
       "      <td id=\"T_1bc62_row4_col2\" class=\"data row4 col2\" >0.5417</td>\n",
       "      <td id=\"T_1bc62_row4_col3\" class=\"data row4 col3\" >0.7222</td>\n",
       "      <td id=\"T_1bc62_row4_col4\" class=\"data row4 col4\" >0.6190</td>\n",
       "      <td id=\"T_1bc62_row4_col5\" class=\"data row4 col5\" >0.4299</td>\n",
       "      <td id=\"T_1bc62_row4_col6\" class=\"data row4 col6\" >0.4401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1bc62_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_1bc62_row5_col0\" class=\"data row5 col0\" >0.8226</td>\n",
       "      <td id=\"T_1bc62_row5_col1\" class=\"data row5 col1\" >0.8399</td>\n",
       "      <td id=\"T_1bc62_row5_col2\" class=\"data row5 col2\" >0.6667</td>\n",
       "      <td id=\"T_1bc62_row5_col3\" class=\"data row5 col3\" >0.8421</td>\n",
       "      <td id=\"T_1bc62_row5_col4\" class=\"data row5 col4\" >0.7442</td>\n",
       "      <td id=\"T_1bc62_row5_col5\" class=\"data row5 col5\" >0.6112</td>\n",
       "      <td id=\"T_1bc62_row5_col6\" class=\"data row5 col6\" >0.6209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1bc62_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_1bc62_row6_col0\" class=\"data row6 col0\" >0.8871</td>\n",
       "      <td id=\"T_1bc62_row6_col1\" class=\"data row6 col1\" >0.8871</td>\n",
       "      <td id=\"T_1bc62_row6_col2\" class=\"data row6 col2\" >0.7917</td>\n",
       "      <td id=\"T_1bc62_row6_col3\" class=\"data row6 col3\" >0.9048</td>\n",
       "      <td id=\"T_1bc62_row6_col4\" class=\"data row6 col4\" >0.8444</td>\n",
       "      <td id=\"T_1bc62_row6_col5\" class=\"data row6 col5\" >0.7565</td>\n",
       "      <td id=\"T_1bc62_row6_col6\" class=\"data row6 col6\" >0.7606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1bc62_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_1bc62_row7_col0\" class=\"data row7 col0\" >0.8387</td>\n",
       "      <td id=\"T_1bc62_row7_col1\" class=\"data row7 col1\" >0.8333</td>\n",
       "      <td id=\"T_1bc62_row7_col2\" class=\"data row7 col2\" >0.8333</td>\n",
       "      <td id=\"T_1bc62_row7_col3\" class=\"data row7 col3\" >0.7692</td>\n",
       "      <td id=\"T_1bc62_row7_col4\" class=\"data row7 col4\" >0.8000</td>\n",
       "      <td id=\"T_1bc62_row7_col5\" class=\"data row7 col5\" >0.6652</td>\n",
       "      <td id=\"T_1bc62_row7_col6\" class=\"data row7 col6\" >0.6667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1bc62_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_1bc62_row8_col0\" class=\"data row8 col0\" >0.8226</td>\n",
       "      <td id=\"T_1bc62_row8_col1\" class=\"data row8 col1\" >0.8629</td>\n",
       "      <td id=\"T_1bc62_row8_col2\" class=\"data row8 col2\" >0.7083</td>\n",
       "      <td id=\"T_1bc62_row8_col3\" class=\"data row8 col3\" >0.8095</td>\n",
       "      <td id=\"T_1bc62_row8_col4\" class=\"data row8 col4\" >0.7556</td>\n",
       "      <td id=\"T_1bc62_row8_col5\" class=\"data row8 col5\" >0.6173</td>\n",
       "      <td id=\"T_1bc62_row8_col6\" class=\"data row8 col6\" >0.6207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1bc62_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_1bc62_row9_col0\" class=\"data row9 col0\" >0.8548</td>\n",
       "      <td id=\"T_1bc62_row9_col1\" class=\"data row9 col1\" >0.9052</td>\n",
       "      <td id=\"T_1bc62_row9_col2\" class=\"data row9 col2\" >0.8333</td>\n",
       "      <td id=\"T_1bc62_row9_col3\" class=\"data row9 col3\" >0.8000</td>\n",
       "      <td id=\"T_1bc62_row9_col4\" class=\"data row9 col4\" >0.8163</td>\n",
       "      <td id=\"T_1bc62_row9_col5\" class=\"data row9 col5\" >0.6964</td>\n",
       "      <td id=\"T_1bc62_row9_col6\" class=\"data row9 col6\" >0.6968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1bc62_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_1bc62_row10_col0\" class=\"data row10 col0\" >0.8346</td>\n",
       "      <td id=\"T_1bc62_row10_col1\" class=\"data row10 col1\" >0.8523</td>\n",
       "      <td id=\"T_1bc62_row10_col2\" class=\"data row10 col2\" >0.7321</td>\n",
       "      <td id=\"T_1bc62_row10_col3\" class=\"data row10 col3\" >0.8197</td>\n",
       "      <td id=\"T_1bc62_row10_col4\" class=\"data row10 col4\" >0.7704</td>\n",
       "      <td id=\"T_1bc62_row10_col5\" class=\"data row10 col5\" >0.6423</td>\n",
       "      <td id=\"T_1bc62_row10_col6\" class=\"data row10 col6\" >0.6474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1bc62_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_1bc62_row11_col0\" class=\"data row11 col0\" >0.0362</td>\n",
       "      <td id=\"T_1bc62_row11_col1\" class=\"data row11 col1\" >0.0477</td>\n",
       "      <td id=\"T_1bc62_row11_col2\" class=\"data row11 col2\" >0.0882</td>\n",
       "      <td id=\"T_1bc62_row11_col3\" class=\"data row11 col3\" >0.0514</td>\n",
       "      <td id=\"T_1bc62_row11_col4\" class=\"data row11 col4\" >0.0591</td>\n",
       "      <td id=\"T_1bc62_row11_col5\" class=\"data row11 col5\" >0.0826</td>\n",
       "      <td id=\"T_1bc62_row11_col6\" class=\"data row11 col6\" >0.0804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1e4fbf48b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 50 candidates, totalling 500 fits\n"
     ]
    }
   ],
   "source": [
    "# tuned_dt = tune_model(gbc, optimize = 'Accuracy')\n",
    "tuned_gbc = tune_model(gbc, n_iter=50, optimize = 'Accuracy', choose_better=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-19 {\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-19 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 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-19 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-19 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-19 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-19 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-19 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-19 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-19 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-19 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-19 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-19 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-19 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-19 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-19 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 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-19 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 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-19 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-19 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-19 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-19 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-19 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-19 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-19 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-19 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-19 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-19 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-19 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-19 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-19 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-19 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 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-19 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-19 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-19 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-19 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-19\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(ccp_alpha=0.0, criterion=&#x27;friedman_mse&#x27;, init=None,\n",
       "                           learning_rate=0.15, loss=&#x27;log_loss&#x27;, max_depth=9,\n",
       "                           max_features=1.0, max_leaf_nodes=None,\n",
       "                           min_impurity_decrease=0.2, min_samples_leaf=4,\n",
       "                           min_samples_split=7, min_weight_fraction_leaf=0.0,\n",
       "                           n_estimators=260, n_iter_no_change=None,\n",
       "                           random_state=123, subsample=0.7, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)</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-80\" type=\"checkbox\" checked><label for=\"sk-estimator-id-80\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GradientBoostingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingClassifier(ccp_alpha=0.0, criterion=&#x27;friedman_mse&#x27;, init=None,\n",
       "                           learning_rate=0.15, loss=&#x27;log_loss&#x27;, max_depth=9,\n",
       "                           max_features=1.0, max_leaf_nodes=None,\n",
       "                           min_impurity_decrease=0.2, min_samples_leaf=4,\n",
       "                           min_samples_split=7, min_weight_fraction_leaf=0.0,\n",
       "                           n_estimators=260, n_iter_no_change=None,\n",
       "                           random_state=123, subsample=0.7, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
       "                           learning_rate=0.15, loss='log_loss', max_depth=9,\n",
       "                           max_features=1.0, max_leaf_nodes=None,\n",
       "                           min_impurity_decrease=0.2, min_samples_leaf=4,\n",
       "                           min_samples_split=7, min_weight_fraction_leaf=0.0,\n",
       "                           n_estimators=260, n_iter_no_change=None,\n",
       "                           random_state=123, subsample=0.7, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use automl function to tune hyperparameters\n",
    "tuned_best_accuracy = automl(optimize = 'Accuracy')\n",
    "# tuned_best_auc = automl(optimize = 'AUC')\n",
    "# tuned_best_precision = automl(optimize = 'Precision')\n",
    "tuned_best_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Samples</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>AUC</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "      <th>MCC</th>\n",
       "      <th>Selection Rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>93.00</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>175.00</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Samples  Accuracy  AUC  Recall  Precision   F1  Kappa  MCC  \\\n",
       "Sex_1                                                               \n",
       "False    93.00      0.83 0.81    0.85       0.90 0.88   0.60 0.60   \n",
       "True    175.00      0.80 0.61    0.27       0.56 0.36   0.26 0.29   \n",
       "\n",
       "       Selection Rate  \n",
       "Sex_1                  \n",
       "False            0.67  \n",
       "True             0.10  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check fairness\n",
    "check_fairness(best, sensitive_features='Sex_1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dashboard function\n",
    "# dashboard(dt, display_format ='inline')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ensemble model\n",
    "\n",
    "``pycaret.classification.ensemble_model()``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_b6841 th {\n",
       "  text-align: left;\n",
       "}\n",
       "#T_b6841_row0_col0, #T_b6841_row0_col2, #T_b6841_row0_col3, #T_b6841_row1_col0, #T_b6841_row1_col1, #T_b6841_row1_col2, #T_b6841_row1_col3, #T_b6841_row1_col4, #T_b6841_row1_col5, #T_b6841_row1_col6, #T_b6841_row1_col7, #T_b6841_row2_col0, #T_b6841_row2_col1, #T_b6841_row2_col2, #T_b6841_row2_col3, #T_b6841_row2_col4, #T_b6841_row2_col5, #T_b6841_row2_col6, #T_b6841_row2_col7, #T_b6841_row3_col0, #T_b6841_row3_col1, #T_b6841_row3_col2, #T_b6841_row3_col3, #T_b6841_row3_col4, #T_b6841_row3_col5, #T_b6841_row3_col6, #T_b6841_row3_col7, #T_b6841_row4_col0, #T_b6841_row4_col1, #T_b6841_row4_col2, #T_b6841_row4_col3, #T_b6841_row4_col4, #T_b6841_row4_col5, #T_b6841_row4_col6, #T_b6841_row4_col7, #T_b6841_row5_col0, #T_b6841_row5_col1, #T_b6841_row5_col2, #T_b6841_row5_col3, #T_b6841_row5_col4, #T_b6841_row5_col5, #T_b6841_row5_col6, #T_b6841_row5_col7, #T_b6841_row6_col0, #T_b6841_row6_col1, #T_b6841_row6_col3, #T_b6841_row6_col4, #T_b6841_row6_col5, #T_b6841_row6_col6, #T_b6841_row6_col7, #T_b6841_row7_col0, #T_b6841_row7_col1, #T_b6841_row7_col2, #T_b6841_row7_col3, #T_b6841_row7_col4, #T_b6841_row7_col5, #T_b6841_row7_col6, #T_b6841_row7_col7, #T_b6841_row8_col0, #T_b6841_row8_col1, #T_b6841_row8_col2, #T_b6841_row8_col3, #T_b6841_row8_col4, #T_b6841_row8_col5, #T_b6841_row8_col6, #T_b6841_row8_col7, #T_b6841_row9_col0, #T_b6841_row9_col1, #T_b6841_row9_col2, #T_b6841_row9_col3, #T_b6841_row9_col4, #T_b6841_row9_col5, #T_b6841_row9_col6, #T_b6841_row9_col7, #T_b6841_row10_col0, #T_b6841_row10_col1, #T_b6841_row10_col2, #T_b6841_row10_col3, #T_b6841_row10_col4, #T_b6841_row10_col5, #T_b6841_row10_col6, #T_b6841_row10_col7, #T_b6841_row11_col0, #T_b6841_row11_col1, #T_b6841_row11_col2, #T_b6841_row11_col3, #T_b6841_row11_col4, #T_b6841_row11_col5, #T_b6841_row11_col6, #T_b6841_row11_col7, #T_b6841_row12_col0, #T_b6841_row12_col1, #T_b6841_row12_col2, #T_b6841_row12_col4, #T_b6841_row12_col5, #T_b6841_row12_col6, #T_b6841_row12_col7, #T_b6841_row13_col0, #T_b6841_row13_col1, #T_b6841_row13_col2, #T_b6841_row13_col3, #T_b6841_row13_col4, #T_b6841_row13_col5, #T_b6841_row13_col6, #T_b6841_row13_col7, #T_b6841_row14_col0, #T_b6841_row14_col1, #T_b6841_row14_col2, #T_b6841_row14_col3, #T_b6841_row14_col4, #T_b6841_row14_col5, #T_b6841_row14_col6, #T_b6841_row14_col7 {\n",
       "  text-align: left;\n",
       "}\n",
       "#T_b6841_row0_col1, #T_b6841_row0_col4, #T_b6841_row0_col5, #T_b6841_row0_col6, #T_b6841_row0_col7, #T_b6841_row6_col2, #T_b6841_row12_col3 {\n",
       "  text-align: left;\n",
       "  background-color: yellow;\n",
       "}\n",
       "#T_b6841_row0_col8, #T_b6841_row1_col8, #T_b6841_row2_col8, #T_b6841_row3_col8, #T_b6841_row4_col8, #T_b6841_row5_col8, #T_b6841_row6_col8, #T_b6841_row7_col8, #T_b6841_row8_col8, #T_b6841_row9_col8, #T_b6841_row10_col8, #T_b6841_row11_col8, #T_b6841_row12_col8, #T_b6841_row13_col8 {\n",
       "  text-align: left;\n",
       "  background-color: lightgrey;\n",
       "}\n",
       "#T_b6841_row14_col8 {\n",
       "  text-align: left;\n",
       "  background-color: yellow;\n",
       "  background-color: lightgrey;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_b6841\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_b6841_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_b6841_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th id=\"T_b6841_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
       "      <th id=\"T_b6841_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th id=\"T_b6841_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
       "      <th id=\"T_b6841_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
       "      <th id=\"T_b6841_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
       "      <th id=\"T_b6841_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
       "      <th id=\"T_b6841_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_b6841_level0_row0\" class=\"row_heading level0 row0\" >gbc</th>\n",
       "      <td id=\"T_b6841_row0_col0\" class=\"data row0 col0\" >Gradient Boosting Classifier</td>\n",
       "      <td id=\"T_b6841_row0_col1\" class=\"data row0 col1\" >0.8250</td>\n",
       "      <td id=\"T_b6841_row0_col2\" class=\"data row0 col2\" >0.8439</td>\n",
       "      <td id=\"T_b6841_row0_col3\" class=\"data row0 col3\" >0.7279</td>\n",
       "      <td id=\"T_b6841_row0_col4\" class=\"data row0 col4\" >0.7980</td>\n",
       "      <td id=\"T_b6841_row0_col5\" class=\"data row0 col5\" >0.7590</td>\n",
       "      <td id=\"T_b6841_row0_col6\" class=\"data row0 col6\" >0.6225</td>\n",
       "      <td id=\"T_b6841_row0_col7\" class=\"data row0 col7\" >0.6262</td>\n",
       "      <td id=\"T_b6841_row0_col8\" class=\"data row0 col8\" >0.0280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6841_level0_row1\" class=\"row_heading level0 row1\" >lightgbm</th>\n",
       "      <td id=\"T_b6841_row1_col0\" class=\"data row1 col0\" >Light Gradient Boosting Machine</td>\n",
       "      <td id=\"T_b6841_row1_col1\" class=\"data row1 col1\" >0.8202</td>\n",
       "      <td id=\"T_b6841_row1_col2\" class=\"data row1 col2\" >0.8423</td>\n",
       "      <td id=\"T_b6841_row1_col3\" class=\"data row1 col3\" >0.7362</td>\n",
       "      <td id=\"T_b6841_row1_col4\" class=\"data row1 col4\" >0.7862</td>\n",
       "      <td id=\"T_b6841_row1_col5\" class=\"data row1 col5\" >0.7565</td>\n",
       "      <td id=\"T_b6841_row1_col6\" class=\"data row1 col6\" >0.6147</td>\n",
       "      <td id=\"T_b6841_row1_col7\" class=\"data row1 col7\" >0.6191</td>\n",
       "      <td id=\"T_b6841_row1_col8\" class=\"data row1 col8\" >0.0960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6841_level0_row2\" class=\"row_heading level0 row2\" >rf</th>\n",
       "      <td id=\"T_b6841_row2_col0\" class=\"data row2 col0\" >Random Forest Classifier</td>\n",
       "      <td id=\"T_b6841_row2_col1\" class=\"data row2 col1\" >0.8169</td>\n",
       "      <td id=\"T_b6841_row2_col2\" class=\"data row2 col2\" >0.8454</td>\n",
       "      <td id=\"T_b6841_row2_col3\" class=\"data row2 col3\" >0.7236</td>\n",
       "      <td id=\"T_b6841_row2_col4\" class=\"data row2 col4\" >0.7846</td>\n",
       "      <td id=\"T_b6841_row2_col5\" class=\"data row2 col5\" >0.7502</td>\n",
       "      <td id=\"T_b6841_row2_col6\" class=\"data row2 col6\" >0.6065</td>\n",
       "      <td id=\"T_b6841_row2_col7\" class=\"data row2 col7\" >0.6101</td>\n",
       "      <td id=\"T_b6841_row2_col8\" class=\"data row2 col8\" >0.0490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6841_level0_row3\" class=\"row_heading level0 row3\" >ada</th>\n",
       "      <td id=\"T_b6841_row3_col0\" class=\"data row3 col0\" >Ada Boost Classifier</td>\n",
       "      <td id=\"T_b6841_row3_col1\" class=\"data row3 col1\" >0.8105</td>\n",
       "      <td id=\"T_b6841_row3_col2\" class=\"data row3 col2\" >0.8439</td>\n",
       "      <td id=\"T_b6841_row3_col3\" class=\"data row3 col3\" >0.7533</td>\n",
       "      <td id=\"T_b6841_row3_col4\" class=\"data row3 col4\" >0.7551</td>\n",
       "      <td id=\"T_b6841_row3_col5\" class=\"data row3 col5\" >0.7524</td>\n",
       "      <td id=\"T_b6841_row3_col6\" class=\"data row3 col6\" >0.5991</td>\n",
       "      <td id=\"T_b6841_row3_col7\" class=\"data row3 col7\" >0.6010</td>\n",
       "      <td id=\"T_b6841_row3_col8\" class=\"data row3 col8\" >0.0240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6841_level0_row4\" class=\"row_heading level0 row4\" >xgboost</th>\n",
       "      <td id=\"T_b6841_row4_col0\" class=\"data row4 col0\" >Extreme Gradient Boosting</td>\n",
       "      <td id=\"T_b6841_row4_col1\" class=\"data row4 col1\" >0.8091</td>\n",
       "      <td id=\"T_b6841_row4_col2\" class=\"data row4 col2\" >0.8373</td>\n",
       "      <td id=\"T_b6841_row4_col3\" class=\"data row4 col3\" >0.7154</td>\n",
       "      <td id=\"T_b6841_row4_col4\" class=\"data row4 col4\" >0.7682</td>\n",
       "      <td id=\"T_b6841_row4_col5\" class=\"data row4 col5\" >0.7378</td>\n",
       "      <td id=\"T_b6841_row4_col6\" class=\"data row4 col6\" >0.5887</td>\n",
       "      <td id=\"T_b6841_row4_col7\" class=\"data row4 col7\" >0.5921</td>\n",
       "      <td id=\"T_b6841_row4_col8\" class=\"data row4 col8\" >0.0190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6841_level0_row5\" class=\"row_heading level0 row5\" >lda</th>\n",
       "      <td id=\"T_b6841_row5_col0\" class=\"data row5 col0\" >Linear Discriminant Analysis</td>\n",
       "      <td id=\"T_b6841_row5_col1\" class=\"data row5 col1\" >0.8088</td>\n",
       "      <td id=\"T_b6841_row5_col2\" class=\"data row5 col2\" >0.8608</td>\n",
       "      <td id=\"T_b6841_row5_col3\" class=\"data row5 col3\" >0.7281</td>\n",
       "      <td id=\"T_b6841_row5_col4\" class=\"data row5 col4\" >0.7642</td>\n",
       "      <td id=\"T_b6841_row5_col5\" class=\"data row5 col5\" >0.7429</td>\n",
       "      <td id=\"T_b6841_row5_col6\" class=\"data row5 col6\" >0.5913</td>\n",
       "      <td id=\"T_b6841_row5_col7\" class=\"data row5 col7\" >0.5944</td>\n",
       "      <td id=\"T_b6841_row5_col8\" class=\"data row5 col8\" >0.0070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6841_level0_row6\" class=\"row_heading level0 row6\" >lr</th>\n",
       "      <td id=\"T_b6841_row6_col0\" class=\"data row6 col0\" >Logistic Regression</td>\n",
       "      <td id=\"T_b6841_row6_col1\" class=\"data row6 col1\" >0.8071</td>\n",
       "      <td id=\"T_b6841_row6_col2\" class=\"data row6 col2\" >0.8624</td>\n",
       "      <td id=\"T_b6841_row6_col3\" class=\"data row6 col3\" >0.7239</td>\n",
       "      <td id=\"T_b6841_row6_col4\" class=\"data row6 col4\" >0.7642</td>\n",
       "      <td id=\"T_b6841_row6_col5\" class=\"data row6 col5\" >0.7395</td>\n",
       "      <td id=\"T_b6841_row6_col6\" class=\"data row6 col6\" >0.5873</td>\n",
       "      <td id=\"T_b6841_row6_col7\" class=\"data row6 col7\" >0.5914</td>\n",
       "      <td id=\"T_b6841_row6_col8\" class=\"data row6 col8\" >0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6841_level0_row7\" class=\"row_heading level0 row7\" >ridge</th>\n",
       "      <td id=\"T_b6841_row7_col0\" class=\"data row7 col0\" >Ridge Classifier</td>\n",
       "      <td id=\"T_b6841_row7_col1\" class=\"data row7 col1\" >0.8056</td>\n",
       "      <td id=\"T_b6841_row7_col2\" class=\"data row7 col2\" >0.8610</td>\n",
       "      <td id=\"T_b6841_row7_col3\" class=\"data row7 col3\" >0.7197</td>\n",
       "      <td id=\"T_b6841_row7_col4\" class=\"data row7 col4\" >0.7610</td>\n",
       "      <td id=\"T_b6841_row7_col5\" class=\"data row7 col5\" >0.7364</td>\n",
       "      <td id=\"T_b6841_row7_col6\" class=\"data row7 col6\" >0.5833</td>\n",
       "      <td id=\"T_b6841_row7_col7\" class=\"data row7 col7\" >0.5868</td>\n",
       "      <td id=\"T_b6841_row7_col8\" class=\"data row7 col8\" >0.0070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6841_level0_row8\" class=\"row_heading level0 row8\" >qda</th>\n",
       "      <td id=\"T_b6841_row8_col0\" class=\"data row8 col0\" >Quadratic Discriminant Analysis</td>\n",
       "      <td id=\"T_b6841_row8_col1\" class=\"data row8 col1\" >0.7977</td>\n",
       "      <td id=\"T_b6841_row8_col2\" class=\"data row8 col2\" >0.8487</td>\n",
       "      <td id=\"T_b6841_row8_col3\" class=\"data row8 col3\" >0.7196</td>\n",
       "      <td id=\"T_b6841_row8_col4\" class=\"data row8 col4\" >0.7528</td>\n",
       "      <td id=\"T_b6841_row8_col5\" class=\"data row8 col5\" >0.7296</td>\n",
       "      <td id=\"T_b6841_row8_col6\" class=\"data row8 col6\" >0.5691</td>\n",
       "      <td id=\"T_b6841_row8_col7\" class=\"data row8 col7\" >0.5754</td>\n",
       "      <td id=\"T_b6841_row8_col8\" class=\"data row8 col8\" >0.0070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6841_level0_row9\" class=\"row_heading level0 row9\" >et</th>\n",
       "      <td id=\"T_b6841_row9_col0\" class=\"data row9 col0\" >Extra Trees Classifier</td>\n",
       "      <td id=\"T_b6841_row9_col1\" class=\"data row9 col1\" >0.7944</td>\n",
       "      <td id=\"T_b6841_row9_col2\" class=\"data row9 col2\" >0.8317</td>\n",
       "      <td id=\"T_b6841_row9_col3\" class=\"data row9 col3\" >0.7067</td>\n",
       "      <td id=\"T_b6841_row9_col4\" class=\"data row9 col4\" >0.7478</td>\n",
       "      <td id=\"T_b6841_row9_col5\" class=\"data row9 col5\" >0.7250</td>\n",
       "      <td id=\"T_b6841_row9_col6\" class=\"data row9 col6\" >0.5611</td>\n",
       "      <td id=\"T_b6841_row9_col7\" class=\"data row9 col7\" >0.5632</td>\n",
       "      <td id=\"T_b6841_row9_col8\" class=\"data row9 col8\" >0.0450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6841_level0_row10\" class=\"row_heading level0 row10\" >knn</th>\n",
       "      <td id=\"T_b6841_row10_col0\" class=\"data row10 col0\" >K Neighbors Classifier</td>\n",
       "      <td id=\"T_b6841_row10_col1\" class=\"data row10 col1\" >0.7912</td>\n",
       "      <td id=\"T_b6841_row10_col2\" class=\"data row10 col2\" >0.8442</td>\n",
       "      <td id=\"T_b6841_row10_col3\" class=\"data row10 col3\" >0.6944</td>\n",
       "      <td id=\"T_b6841_row10_col4\" class=\"data row10 col4\" >0.7487</td>\n",
       "      <td id=\"T_b6841_row10_col5\" class=\"data row10 col5\" >0.7134</td>\n",
       "      <td id=\"T_b6841_row10_col6\" class=\"data row10 col6\" >0.5509</td>\n",
       "      <td id=\"T_b6841_row10_col7\" class=\"data row10 col7\" >0.5583</td>\n",
       "      <td id=\"T_b6841_row10_col8\" class=\"data row10 col8\" >0.0140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6841_level0_row11\" class=\"row_heading level0 row11\" >svm</th>\n",
       "      <td id=\"T_b6841_row11_col0\" class=\"data row11 col0\" >SVM - Linear Kernel</td>\n",
       "      <td id=\"T_b6841_row11_col1\" class=\"data row11 col1\" >0.7815</td>\n",
       "      <td id=\"T_b6841_row11_col2\" class=\"data row11 col2\" >0.8287</td>\n",
       "      <td id=\"T_b6841_row11_col3\" class=\"data row11 col3\" >0.7027</td>\n",
       "      <td id=\"T_b6841_row11_col4\" class=\"data row11 col4\" >0.7318</td>\n",
       "      <td id=\"T_b6841_row11_col5\" class=\"data row11 col5\" >0.7091</td>\n",
       "      <td id=\"T_b6841_row11_col6\" class=\"data row11 col6\" >0.5358</td>\n",
       "      <td id=\"T_b6841_row11_col7\" class=\"data row11 col7\" >0.5438</td>\n",
       "      <td id=\"T_b6841_row11_col8\" class=\"data row11 col8\" >0.0070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6841_level0_row12\" class=\"row_heading level0 row12\" >nb</th>\n",
       "      <td id=\"T_b6841_row12_col0\" class=\"data row12 col0\" >Naive Bayes</td>\n",
       "      <td id=\"T_b6841_row12_col1\" class=\"data row12 col1\" >0.7753</td>\n",
       "      <td id=\"T_b6841_row12_col2\" class=\"data row12 col2\" >0.8305</td>\n",
       "      <td id=\"T_b6841_row12_col3\" class=\"data row12 col3\" >0.7574</td>\n",
       "      <td id=\"T_b6841_row12_col4\" class=\"data row12 col4\" >0.6928</td>\n",
       "      <td id=\"T_b6841_row12_col5\" class=\"data row12 col5\" >0.7197</td>\n",
       "      <td id=\"T_b6841_row12_col6\" class=\"data row12 col6\" >0.5335</td>\n",
       "      <td id=\"T_b6841_row12_col7\" class=\"data row12 col7\" >0.5400</td>\n",
       "      <td id=\"T_b6841_row12_col8\" class=\"data row12 col8\" >0.0150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6841_level0_row13\" class=\"row_heading level0 row13\" >dt</th>\n",
       "      <td id=\"T_b6841_row13_col0\" class=\"data row13 col0\" >Decision Tree Classifier</td>\n",
       "      <td id=\"T_b6841_row13_col1\" class=\"data row13 col1\" >0.7737</td>\n",
       "      <td id=\"T_b6841_row13_col2\" class=\"data row13 col2\" >0.7521</td>\n",
       "      <td id=\"T_b6841_row13_col3\" class=\"data row13 col3\" >0.7029</td>\n",
       "      <td id=\"T_b6841_row13_col4\" class=\"data row13 col4\" >0.7076</td>\n",
       "      <td id=\"T_b6841_row13_col5\" class=\"data row13 col5\" >0.7035</td>\n",
       "      <td id=\"T_b6841_row13_col6\" class=\"data row13 col6\" >0.5207</td>\n",
       "      <td id=\"T_b6841_row13_col7\" class=\"data row13 col7\" >0.5224</td>\n",
       "      <td id=\"T_b6841_row13_col8\" class=\"data row13 col8\" >0.0070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6841_level0_row14\" class=\"row_heading level0 row14\" >dummy</th>\n",
       "      <td id=\"T_b6841_row14_col0\" class=\"data row14 col0\" >Dummy Classifier</td>\n",
       "      <td id=\"T_b6841_row14_col1\" class=\"data row14 col1\" >0.6164</td>\n",
       "      <td id=\"T_b6841_row14_col2\" class=\"data row14 col2\" >0.5000</td>\n",
       "      <td id=\"T_b6841_row14_col3\" class=\"data row14 col3\" >0.0000</td>\n",
       "      <td id=\"T_b6841_row14_col4\" class=\"data row14 col4\" >0.0000</td>\n",
       "      <td id=\"T_b6841_row14_col5\" class=\"data row14 col5\" >0.0000</td>\n",
       "      <td id=\"T_b6841_row14_col6\" class=\"data row14 col6\" >0.0000</td>\n",
       "      <td id=\"T_b6841_row14_col7\" class=\"data row14 col7\" >0.0000</td>\n",
       "      <td id=\"T_b6841_row14_col8\" class=\"data row14 col8\" >0.0060</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1e4812de590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_accuracy_models_top3 = compare_models(sort = 'Accuracy', n_select = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
       "                            learning_rate=0.1, loss='log_loss', max_depth=3,\n",
       "                            max_features=None, max_leaf_nodes=None,\n",
       "                            min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                            n_estimators=100, n_iter_no_change=None,\n",
       "                            random_state=123, subsample=1.0, tol=0.0001,\n",
       "                            validation_fraction=0.1, verbose=0,\n",
       "                            warm_start=False),\n",
       " LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
       "                importance_type='split', learning_rate=0.1, max_depth=-1,\n",
       "                min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
       "                n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,\n",
       "                random_state=123, reg_alpha=0.0, reg_lambda=0.0, subsample=1.0,\n",
       "                subsample_for_bin=200000, subsample_freq=0),\n",
       " RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                        criterion='gini', max_depth=None, max_features='sqrt',\n",
       "                        max_leaf_nodes=None, max_samples=None,\n",
       "                        min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                        min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                        monotonic_cst=None, n_estimators=100, n_jobs=-1,\n",
       "                        oob_score=False, random_state=123, verbose=0,\n",
       "                        warm_start=False)]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top 3 models based on recall\n",
    "best_accuracy_models_top3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Blend Models\n",
    "This function trains a Soft Voting / Majority Rule classifier for select models passed in the estimator_list parameter. The output of this function is a scoring grid with CV scores by fold. Metrics evaluated during CV can be accessed using the ``get_metrics`` function. Custom metrics can be added or removed using ``add_metric`` and ``remove_metric`` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_cd492_row10_col0, #T_cd492_row10_col1, #T_cd492_row10_col2, #T_cd492_row10_col3, #T_cd492_row10_col4, #T_cd492_row10_col5, #T_cd492_row10_col6 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_cd492\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_cd492_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th id=\"T_cd492_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th id=\"T_cd492_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th id=\"T_cd492_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th id=\"T_cd492_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th id=\"T_cd492_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th id=\"T_cd492_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_cd492_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_cd492_row0_col0\" class=\"data row0 col0\" >0.8254</td>\n",
       "      <td id=\"T_cd492_row0_col1\" class=\"data row0 col1\" >0.8691</td>\n",
       "      <td id=\"T_cd492_row0_col2\" class=\"data row0 col2\" >0.7917</td>\n",
       "      <td id=\"T_cd492_row0_col3\" class=\"data row0 col3\" >0.7600</td>\n",
       "      <td id=\"T_cd492_row0_col4\" class=\"data row0 col4\" >0.7755</td>\n",
       "      <td id=\"T_cd492_row0_col5\" class=\"data row0 col5\" >0.6328</td>\n",
       "      <td id=\"T_cd492_row0_col6\" class=\"data row0 col6\" >0.6331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cd492_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_cd492_row1_col0\" class=\"data row1 col0\" >0.8571</td>\n",
       "      <td id=\"T_cd492_row1_col1\" class=\"data row1 col1\" >0.8291</td>\n",
       "      <td id=\"T_cd492_row1_col2\" class=\"data row1 col2\" >0.7917</td>\n",
       "      <td id=\"T_cd492_row1_col3\" class=\"data row1 col3\" >0.8261</td>\n",
       "      <td id=\"T_cd492_row1_col4\" class=\"data row1 col4\" >0.8085</td>\n",
       "      <td id=\"T_cd492_row1_col5\" class=\"data row1 col5\" >0.6947</td>\n",
       "      <td id=\"T_cd492_row1_col6\" class=\"data row1 col6\" >0.6951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cd492_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_cd492_row2_col0\" class=\"data row2 col0\" >0.7937</td>\n",
       "      <td id=\"T_cd492_row2_col1\" class=\"data row2 col1\" >0.9177</td>\n",
       "      <td id=\"T_cd492_row2_col2\" class=\"data row2 col2\" >0.6250</td>\n",
       "      <td id=\"T_cd492_row2_col3\" class=\"data row2 col3\" >0.7895</td>\n",
       "      <td id=\"T_cd492_row2_col4\" class=\"data row2 col4\" >0.6977</td>\n",
       "      <td id=\"T_cd492_row2_col5\" class=\"data row2 col5\" >0.5442</td>\n",
       "      <td id=\"T_cd492_row2_col6\" class=\"data row2 col6\" >0.5528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cd492_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_cd492_row3_col0\" class=\"data row3 col0\" >0.8226</td>\n",
       "      <td id=\"T_cd492_row3_col1\" class=\"data row3 col1\" >0.9130</td>\n",
       "      <td id=\"T_cd492_row3_col2\" class=\"data row3 col2\" >0.6522</td>\n",
       "      <td id=\"T_cd492_row3_col3\" class=\"data row3 col3\" >0.8333</td>\n",
       "      <td id=\"T_cd492_row3_col4\" class=\"data row3 col4\" >0.7317</td>\n",
       "      <td id=\"T_cd492_row3_col5\" class=\"data row3 col5\" >0.6021</td>\n",
       "      <td id=\"T_cd492_row3_col6\" class=\"data row3 col6\" >0.6122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cd492_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_cd492_row4_col0\" class=\"data row4 col0\" >0.7419</td>\n",
       "      <td id=\"T_cd492_row4_col1\" class=\"data row4 col1\" >0.7039</td>\n",
       "      <td id=\"T_cd492_row4_col2\" class=\"data row4 col2\" >0.5417</td>\n",
       "      <td id=\"T_cd492_row4_col3\" class=\"data row4 col3\" >0.7222</td>\n",
       "      <td id=\"T_cd492_row4_col4\" class=\"data row4 col4\" >0.6190</td>\n",
       "      <td id=\"T_cd492_row4_col5\" class=\"data row4 col5\" >0.4299</td>\n",
       "      <td id=\"T_cd492_row4_col6\" class=\"data row4 col6\" >0.4401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cd492_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_cd492_row5_col0\" class=\"data row5 col0\" >0.8387</td>\n",
       "      <td id=\"T_cd492_row5_col1\" class=\"data row5 col1\" >0.8399</td>\n",
       "      <td id=\"T_cd492_row5_col2\" class=\"data row5 col2\" >0.7083</td>\n",
       "      <td id=\"T_cd492_row5_col3\" class=\"data row5 col3\" >0.8500</td>\n",
       "      <td id=\"T_cd492_row5_col4\" class=\"data row5 col4\" >0.7727</td>\n",
       "      <td id=\"T_cd492_row5_col5\" class=\"data row5 col5\" >0.6493</td>\n",
       "      <td id=\"T_cd492_row5_col6\" class=\"data row5 col6\" >0.6558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cd492_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_cd492_row6_col0\" class=\"data row6 col0\" >0.8871</td>\n",
       "      <td id=\"T_cd492_row6_col1\" class=\"data row6 col1\" >0.8794</td>\n",
       "      <td id=\"T_cd492_row6_col2\" class=\"data row6 col2\" >0.8333</td>\n",
       "      <td id=\"T_cd492_row6_col3\" class=\"data row6 col3\" >0.8696</td>\n",
       "      <td id=\"T_cd492_row6_col4\" class=\"data row6 col4\" >0.8511</td>\n",
       "      <td id=\"T_cd492_row6_col5\" class=\"data row6 col5\" >0.7602</td>\n",
       "      <td id=\"T_cd492_row6_col6\" class=\"data row6 col6\" >0.7607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cd492_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_cd492_row7_col0\" class=\"data row7 col0\" >0.8065</td>\n",
       "      <td id=\"T_cd492_row7_col1\" class=\"data row7 col1\" >0.8202</td>\n",
       "      <td id=\"T_cd492_row7_col2\" class=\"data row7 col2\" >0.8333</td>\n",
       "      <td id=\"T_cd492_row7_col3\" class=\"data row7 col3\" >0.7143</td>\n",
       "      <td id=\"T_cd492_row7_col4\" class=\"data row7 col4\" >0.7692</td>\n",
       "      <td id=\"T_cd492_row7_col5\" class=\"data row7 col5\" >0.6043</td>\n",
       "      <td id=\"T_cd492_row7_col6\" class=\"data row7 col6\" >0.6096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cd492_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_cd492_row8_col0\" class=\"data row8 col0\" >0.8226</td>\n",
       "      <td id=\"T_cd492_row8_col1\" class=\"data row8 col1\" >0.8509</td>\n",
       "      <td id=\"T_cd492_row8_col2\" class=\"data row8 col2\" >0.7500</td>\n",
       "      <td id=\"T_cd492_row8_col3\" class=\"data row8 col3\" >0.7826</td>\n",
       "      <td id=\"T_cd492_row8_col4\" class=\"data row8 col4\" >0.7660</td>\n",
       "      <td id=\"T_cd492_row8_col5\" class=\"data row8 col5\" >0.6232</td>\n",
       "      <td id=\"T_cd492_row8_col6\" class=\"data row8 col6\" >0.6236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cd492_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_cd492_row9_col0\" class=\"data row9 col0\" >0.8387</td>\n",
       "      <td id=\"T_cd492_row9_col1\" class=\"data row9 col1\" >0.8871</td>\n",
       "      <td id=\"T_cd492_row9_col2\" class=\"data row9 col2\" >0.8333</td>\n",
       "      <td id=\"T_cd492_row9_col3\" class=\"data row9 col3\" >0.7692</td>\n",
       "      <td id=\"T_cd492_row9_col4\" class=\"data row9 col4\" >0.8000</td>\n",
       "      <td id=\"T_cd492_row9_col5\" class=\"data row9 col5\" >0.6652</td>\n",
       "      <td id=\"T_cd492_row9_col6\" class=\"data row9 col6\" >0.6667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cd492_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_cd492_row10_col0\" class=\"data row10 col0\" >0.8234</td>\n",
       "      <td id=\"T_cd492_row10_col1\" class=\"data row10 col1\" >0.8510</td>\n",
       "      <td id=\"T_cd492_row10_col2\" class=\"data row10 col2\" >0.7361</td>\n",
       "      <td id=\"T_cd492_row10_col3\" class=\"data row10 col3\" >0.7917</td>\n",
       "      <td id=\"T_cd492_row10_col4\" class=\"data row10 col4\" >0.7591</td>\n",
       "      <td id=\"T_cd492_row10_col5\" class=\"data row10 col5\" >0.6206</td>\n",
       "      <td id=\"T_cd492_row10_col6\" class=\"data row10 col6\" >0.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cd492_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_cd492_row11_col0\" class=\"data row11 col0\" >0.0367</td>\n",
       "      <td id=\"T_cd492_row11_col1\" class=\"data row11 col1\" >0.0582</td>\n",
       "      <td id=\"T_cd492_row11_col2\" class=\"data row11 col2\" >0.0963</td>\n",
       "      <td id=\"T_cd492_row11_col3\" class=\"data row11 col3\" >0.0498</td>\n",
       "      <td id=\"T_cd492_row11_col4\" class=\"data row11 col4\" >0.0611</td>\n",
       "      <td id=\"T_cd492_row11_col5\" class=\"data row11 col5\" >0.0841</td>\n",
       "      <td id=\"T_cd492_row11_col6\" class=\"data row11 col6\" >0.0812</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1e480f97f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-20 {\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-20 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-20 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-20 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-20 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-20 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-20 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-20 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-20 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-20 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-20 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-20 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-20 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-20 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-20 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-20 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-20 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-20 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-20 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-20 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-20 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-20 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-20 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-20 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-20 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-20 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-20 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-20 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-20 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-20 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-20 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-20 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-20 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-20 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-20 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-20 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-20 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-20 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-20 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-20 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-20 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-20 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-20\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingClassifier(estimators=[(&#x27;Gradient Boosting Classifier&#x27;,\n",
       "                              GradientBoostingClassifier(ccp_alpha=0.0,\n",
       "                                                         criterion=&#x27;friedman_mse&#x27;,\n",
       "                                                         init=None,\n",
       "                                                         learning_rate=0.1,\n",
       "                                                         loss=&#x27;log_loss&#x27;,\n",
       "                                                         max_depth=3,\n",
       "                                                         max_features=None,\n",
       "                                                         max_leaf_nodes=None,\n",
       "                                                         min_impurity_decrease=0.0,\n",
       "                                                         min_samples_leaf=1,\n",
       "                                                         min_samples_split=2,\n",
       "                                                         min_weight_fraction_leaf=0.0,\n",
       "                                                         n_estimators=100,\n",
       "                                                         n_iter_no_change=Non...\n",
       "                                                     criterion=&#x27;gini&#x27;,\n",
       "                                                     max_depth=None,\n",
       "                                                     max_features=&#x27;sqrt&#x27;,\n",
       "                                                     max_leaf_nodes=None,\n",
       "                                                     max_samples=None,\n",
       "                                                     min_impurity_decrease=0.0,\n",
       "                                                     min_samples_leaf=1,\n",
       "                                                     min_samples_split=2,\n",
       "                                                     min_weight_fraction_leaf=0.0,\n",
       "                                                     monotonic_cst=None,\n",
       "                                                     n_estimators=100,\n",
       "                                                     n_jobs=-1, oob_score=False,\n",
       "                                                     random_state=123,\n",
       "                                                     verbose=0,\n",
       "                                                     warm_start=False))],\n",
       "                 flatten_transform=True, n_jobs=-1, verbose=False,\n",
       "                 voting=&#x27;soft&#x27;, weights=None)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-81\" type=\"checkbox\" ><label for=\"sk-estimator-id-81\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;VotingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.VotingClassifier.html\">?<span>Documentation for VotingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>VotingClassifier(estimators=[(&#x27;Gradient Boosting Classifier&#x27;,\n",
       "                              GradientBoostingClassifier(ccp_alpha=0.0,\n",
       "                                                         criterion=&#x27;friedman_mse&#x27;,\n",
       "                                                         init=None,\n",
       "                                                         learning_rate=0.1,\n",
       "                                                         loss=&#x27;log_loss&#x27;,\n",
       "                                                         max_depth=3,\n",
       "                                                         max_features=None,\n",
       "                                                         max_leaf_nodes=None,\n",
       "                                                         min_impurity_decrease=0.0,\n",
       "                                                         min_samples_leaf=1,\n",
       "                                                         min_samples_split=2,\n",
       "                                                         min_weight_fraction_leaf=0.0,\n",
       "                                                         n_estimators=100,\n",
       "                                                         n_iter_no_change=Non...\n",
       "                                                     criterion=&#x27;gini&#x27;,\n",
       "                                                     max_depth=None,\n",
       "                                                     max_features=&#x27;sqrt&#x27;,\n",
       "                                                     max_leaf_nodes=None,\n",
       "                                                     max_samples=None,\n",
       "                                                     min_impurity_decrease=0.0,\n",
       "                                                     min_samples_leaf=1,\n",
       "                                                     min_samples_split=2,\n",
       "                                                     min_weight_fraction_leaf=0.0,\n",
       "                                                     monotonic_cst=None,\n",
       "                                                     n_estimators=100,\n",
       "                                                     n_jobs=-1, oob_score=False,\n",
       "                                                     random_state=123,\n",
       "                                                     verbose=0,\n",
       "                                                     warm_start=False))],\n",
       "                 flatten_transform=True, n_jobs=-1, verbose=False,\n",
       "                 voting=&#x27;soft&#x27;, weights=None)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>Gradient Boosting Classifier</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-82\" type=\"checkbox\" ><label for=\"sk-estimator-id-82\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;GradientBoostingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingClassifier(random_state=123)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>Light Gradient Boosting Machine</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-83\" type=\"checkbox\" ><label for=\"sk-estimator-id-83\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">LGBMClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>LGBMClassifier(n_jobs=-1, random_state=123)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>Random Forest Classifier</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-84\" type=\"checkbox\" ><label for=\"sk-estimator-id-84\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(n_jobs=-1, random_state=123)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "VotingClassifier(estimators=[('Gradient Boosting Classifier',\n",
       "                              GradientBoostingClassifier(ccp_alpha=0.0,\n",
       "                                                         criterion='friedman_mse',\n",
       "                                                         init=None,\n",
       "                                                         learning_rate=0.1,\n",
       "                                                         loss='log_loss',\n",
       "                                                         max_depth=3,\n",
       "                                                         max_features=None,\n",
       "                                                         max_leaf_nodes=None,\n",
       "                                                         min_impurity_decrease=0.0,\n",
       "                                                         min_samples_leaf=1,\n",
       "                                                         min_samples_split=2,\n",
       "                                                         min_weight_fraction_leaf=0.0,\n",
       "                                                         n_estimators=100,\n",
       "                                                         n_iter_no_change=Non...\n",
       "                                                     criterion='gini',\n",
       "                                                     max_depth=None,\n",
       "                                                     max_features='sqrt',\n",
       "                                                     max_leaf_nodes=None,\n",
       "                                                     max_samples=None,\n",
       "                                                     min_impurity_decrease=0.0,\n",
       "                                                     min_samples_leaf=1,\n",
       "                                                     min_samples_split=2,\n",
       "                                                     min_weight_fraction_leaf=0.0,\n",
       "                                                     monotonic_cst=None,\n",
       "                                                     n_estimators=100,\n",
       "                                                     n_jobs=-1, oob_score=False,\n",
       "                                                     random_state=123,\n",
       "                                                     verbose=0,\n",
       "                                                     warm_start=False))],\n",
       "                 flatten_transform=True, n_jobs=-1, verbose=False,\n",
       "                 voting='soft', weights=None)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# blend top 3 models\n",
    "blended_model = blend_models(best_accuracy_models_top3)\n",
    "blended_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_fa2e3_row10_col0, #T_fa2e3_row10_col1, #T_fa2e3_row10_col2, #T_fa2e3_row10_col3, #T_fa2e3_row10_col4, #T_fa2e3_row10_col5, #T_fa2e3_row10_col6 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_fa2e3\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_fa2e3_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th id=\"T_fa2e3_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th id=\"T_fa2e3_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th id=\"T_fa2e3_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th id=\"T_fa2e3_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th id=\"T_fa2e3_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th id=\"T_fa2e3_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_fa2e3_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_fa2e3_row0_col0\" class=\"data row0 col0\" >0.8254</td>\n",
       "      <td id=\"T_fa2e3_row0_col1\" class=\"data row0 col1\" >0.8584</td>\n",
       "      <td id=\"T_fa2e3_row0_col2\" class=\"data row0 col2\" >0.7917</td>\n",
       "      <td id=\"T_fa2e3_row0_col3\" class=\"data row0 col3\" >0.7600</td>\n",
       "      <td id=\"T_fa2e3_row0_col4\" class=\"data row0 col4\" >0.7755</td>\n",
       "      <td id=\"T_fa2e3_row0_col5\" class=\"data row0 col5\" >0.6328</td>\n",
       "      <td id=\"T_fa2e3_row0_col6\" class=\"data row0 col6\" >0.6331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fa2e3_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_fa2e3_row1_col0\" class=\"data row1 col0\" >0.8571</td>\n",
       "      <td id=\"T_fa2e3_row1_col1\" class=\"data row1 col1\" >0.8387</td>\n",
       "      <td id=\"T_fa2e3_row1_col2\" class=\"data row1 col2\" >0.7917</td>\n",
       "      <td id=\"T_fa2e3_row1_col3\" class=\"data row1 col3\" >0.8261</td>\n",
       "      <td id=\"T_fa2e3_row1_col4\" class=\"data row1 col4\" >0.8085</td>\n",
       "      <td id=\"T_fa2e3_row1_col5\" class=\"data row1 col5\" >0.6947</td>\n",
       "      <td id=\"T_fa2e3_row1_col6\" class=\"data row1 col6\" >0.6951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fa2e3_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_fa2e3_row2_col0\" class=\"data row2 col0\" >0.8254</td>\n",
       "      <td id=\"T_fa2e3_row2_col1\" class=\"data row2 col1\" >0.8985</td>\n",
       "      <td id=\"T_fa2e3_row2_col2\" class=\"data row2 col2\" >0.6250</td>\n",
       "      <td id=\"T_fa2e3_row2_col3\" class=\"data row2 col3\" >0.8824</td>\n",
       "      <td id=\"T_fa2e3_row2_col4\" class=\"data row2 col4\" >0.7317</td>\n",
       "      <td id=\"T_fa2e3_row2_col5\" class=\"data row2 col5\" >0.6078</td>\n",
       "      <td id=\"T_fa2e3_row2_col6\" class=\"data row2 col6\" >0.6277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fa2e3_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_fa2e3_row3_col0\" class=\"data row3 col0\" >0.8387</td>\n",
       "      <td id=\"T_fa2e3_row3_col1\" class=\"data row3 col1\" >0.9075</td>\n",
       "      <td id=\"T_fa2e3_row3_col2\" class=\"data row3 col2\" >0.6957</td>\n",
       "      <td id=\"T_fa2e3_row3_col3\" class=\"data row3 col3\" >0.8421</td>\n",
       "      <td id=\"T_fa2e3_row3_col4\" class=\"data row3 col4\" >0.7619</td>\n",
       "      <td id=\"T_fa2e3_row3_col5\" class=\"data row3 col5\" >0.6416</td>\n",
       "      <td id=\"T_fa2e3_row3_col6\" class=\"data row3 col6\" >0.6483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fa2e3_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_fa2e3_row4_col0\" class=\"data row4 col0\" >0.7419</td>\n",
       "      <td id=\"T_fa2e3_row4_col1\" class=\"data row4 col1\" >0.6930</td>\n",
       "      <td id=\"T_fa2e3_row4_col2\" class=\"data row4 col2\" >0.5417</td>\n",
       "      <td id=\"T_fa2e3_row4_col3\" class=\"data row4 col3\" >0.7222</td>\n",
       "      <td id=\"T_fa2e3_row4_col4\" class=\"data row4 col4\" >0.6190</td>\n",
       "      <td id=\"T_fa2e3_row4_col5\" class=\"data row4 col5\" >0.4299</td>\n",
       "      <td id=\"T_fa2e3_row4_col6\" class=\"data row4 col6\" >0.4401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fa2e3_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_fa2e3_row5_col0\" class=\"data row5 col0\" >0.8226</td>\n",
       "      <td id=\"T_fa2e3_row5_col1\" class=\"data row5 col1\" >0.8476</td>\n",
       "      <td id=\"T_fa2e3_row5_col2\" class=\"data row5 col2\" >0.6667</td>\n",
       "      <td id=\"T_fa2e3_row5_col3\" class=\"data row5 col3\" >0.8421</td>\n",
       "      <td id=\"T_fa2e3_row5_col4\" class=\"data row5 col4\" >0.7442</td>\n",
       "      <td id=\"T_fa2e3_row5_col5\" class=\"data row5 col5\" >0.6112</td>\n",
       "      <td id=\"T_fa2e3_row5_col6\" class=\"data row5 col6\" >0.6209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fa2e3_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_fa2e3_row6_col0\" class=\"data row6 col0\" >0.8871</td>\n",
       "      <td id=\"T_fa2e3_row6_col1\" class=\"data row6 col1\" >0.8794</td>\n",
       "      <td id=\"T_fa2e3_row6_col2\" class=\"data row6 col2\" >0.8333</td>\n",
       "      <td id=\"T_fa2e3_row6_col3\" class=\"data row6 col3\" >0.8696</td>\n",
       "      <td id=\"T_fa2e3_row6_col4\" class=\"data row6 col4\" >0.8511</td>\n",
       "      <td id=\"T_fa2e3_row6_col5\" class=\"data row6 col5\" >0.7602</td>\n",
       "      <td id=\"T_fa2e3_row6_col6\" class=\"data row6 col6\" >0.7607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fa2e3_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_fa2e3_row7_col0\" class=\"data row7 col0\" >0.8065</td>\n",
       "      <td id=\"T_fa2e3_row7_col1\" class=\"data row7 col1\" >0.8235</td>\n",
       "      <td id=\"T_fa2e3_row7_col2\" class=\"data row7 col2\" >0.8333</td>\n",
       "      <td id=\"T_fa2e3_row7_col3\" class=\"data row7 col3\" >0.7143</td>\n",
       "      <td id=\"T_fa2e3_row7_col4\" class=\"data row7 col4\" >0.7692</td>\n",
       "      <td id=\"T_fa2e3_row7_col5\" class=\"data row7 col5\" >0.6043</td>\n",
       "      <td id=\"T_fa2e3_row7_col6\" class=\"data row7 col6\" >0.6096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fa2e3_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_fa2e3_row8_col0\" class=\"data row8 col0\" >0.8226</td>\n",
       "      <td id=\"T_fa2e3_row8_col1\" class=\"data row8 col1\" >0.8553</td>\n",
       "      <td id=\"T_fa2e3_row8_col2\" class=\"data row8 col2\" >0.7500</td>\n",
       "      <td id=\"T_fa2e3_row8_col3\" class=\"data row8 col3\" >0.7826</td>\n",
       "      <td id=\"T_fa2e3_row8_col4\" class=\"data row8 col4\" >0.7660</td>\n",
       "      <td id=\"T_fa2e3_row8_col5\" class=\"data row8 col5\" >0.6232</td>\n",
       "      <td id=\"T_fa2e3_row8_col6\" class=\"data row8 col6\" >0.6236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fa2e3_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_fa2e3_row9_col0\" class=\"data row9 col0\" >0.8548</td>\n",
       "      <td id=\"T_fa2e3_row9_col1\" class=\"data row9 col1\" >0.8882</td>\n",
       "      <td id=\"T_fa2e3_row9_col2\" class=\"data row9 col2\" >0.8333</td>\n",
       "      <td id=\"T_fa2e3_row9_col3\" class=\"data row9 col3\" >0.8000</td>\n",
       "      <td id=\"T_fa2e3_row9_col4\" class=\"data row9 col4\" >0.8163</td>\n",
       "      <td id=\"T_fa2e3_row9_col5\" class=\"data row9 col5\" >0.6964</td>\n",
       "      <td id=\"T_fa2e3_row9_col6\" class=\"data row9 col6\" >0.6968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fa2e3_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_fa2e3_row10_col0\" class=\"data row10 col0\" >0.8282</td>\n",
       "      <td id=\"T_fa2e3_row10_col1\" class=\"data row10 col1\" >0.8490</td>\n",
       "      <td id=\"T_fa2e3_row10_col2\" class=\"data row10 col2\" >0.7362</td>\n",
       "      <td id=\"T_fa2e3_row10_col3\" class=\"data row10 col3\" >0.8041</td>\n",
       "      <td id=\"T_fa2e3_row10_col4\" class=\"data row10 col4\" >0.7643</td>\n",
       "      <td id=\"T_fa2e3_row10_col5\" class=\"data row10 col5\" >0.6302</td>\n",
       "      <td id=\"T_fa2e3_row10_col6\" class=\"data row10 col6\" >0.6356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fa2e3_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_fa2e3_row11_col0\" class=\"data row11 col0\" >0.0362</td>\n",
       "      <td id=\"T_fa2e3_row11_col1\" class=\"data row11 col1\" >0.0579</td>\n",
       "      <td id=\"T_fa2e3_row11_col2\" class=\"data row11 col2\" >0.0956</td>\n",
       "      <td id=\"T_fa2e3_row11_col3\" class=\"data row11 col3\" >0.0557</td>\n",
       "      <td id=\"T_fa2e3_row11_col4\" class=\"data row11 col4\" >0.0591</td>\n",
       "      <td id=\"T_fa2e3_row11_col5\" class=\"data row11 col5\" >0.0820</td>\n",
       "      <td id=\"T_fa2e3_row11_col6\" class=\"data row11 col6\" >0.0790</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1e480fa38d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b512ccf880084f2cacfc1a9e29a0adb0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing:   0%|          | 0/7 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-25 {\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-25 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-25 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-25 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-25 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-25 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-25 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-25 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-25 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-25 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-25 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-25 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-25 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-25 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-25 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-25 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-25 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-25 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-25 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-25 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-25 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-25 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-25 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-25 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-25 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-25 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-25 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-25 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-25 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-25 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-25 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-25 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-25 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-25 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-25 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-25 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-25 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-25 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-25 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-25 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-25 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-25 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-25\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingClassifier(estimators=[(&#x27;Gradient Boosting Classifier&#x27;,\n",
       "                              GradientBoostingClassifier(ccp_alpha=0.0,\n",
       "                                                         criterion=&#x27;friedman_mse&#x27;,\n",
       "                                                         init=None,\n",
       "                                                         learning_rate=0.1,\n",
       "                                                         loss=&#x27;log_loss&#x27;,\n",
       "                                                         max_depth=3,\n",
       "                                                         max_features=None,\n",
       "                                                         max_leaf_nodes=None,\n",
       "                                                         min_impurity_decrease=0.0,\n",
       "                                                         min_samples_leaf=1,\n",
       "                                                         min_samples_split=2,\n",
       "                                                         min_weight_fraction_leaf=0.0,\n",
       "                                                         n_estimators=100,\n",
       "                                                         n_iter_no_change=Non...\n",
       "                                                     max_leaf_nodes=None,\n",
       "                                                     max_samples=None,\n",
       "                                                     min_impurity_decrease=0.0,\n",
       "                                                     min_samples_leaf=1,\n",
       "                                                     min_samples_split=2,\n",
       "                                                     min_weight_fraction_leaf=0.0,\n",
       "                                                     monotonic_cst=None,\n",
       "                                                     n_estimators=100,\n",
       "                                                     n_jobs=-1, oob_score=False,\n",
       "                                                     random_state=123,\n",
       "                                                     verbose=0,\n",
       "                                                     warm_start=False))],\n",
       "                 flatten_transform=True, n_jobs=-1, verbose=False,\n",
       "                 voting=&#x27;soft&#x27;,\n",
       "                 weights=[0.2, 0.8200000000000001, 0.41000000000000003])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-110\" type=\"checkbox\" ><label for=\"sk-estimator-id-110\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;VotingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.VotingClassifier.html\">?<span>Documentation for VotingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>VotingClassifier(estimators=[(&#x27;Gradient Boosting Classifier&#x27;,\n",
       "                              GradientBoostingClassifier(ccp_alpha=0.0,\n",
       "                                                         criterion=&#x27;friedman_mse&#x27;,\n",
       "                                                         init=None,\n",
       "                                                         learning_rate=0.1,\n",
       "                                                         loss=&#x27;log_loss&#x27;,\n",
       "                                                         max_depth=3,\n",
       "                                                         max_features=None,\n",
       "                                                         max_leaf_nodes=None,\n",
       "                                                         min_impurity_decrease=0.0,\n",
       "                                                         min_samples_leaf=1,\n",
       "                                                         min_samples_split=2,\n",
       "                                                         min_weight_fraction_leaf=0.0,\n",
       "                                                         n_estimators=100,\n",
       "                                                         n_iter_no_change=Non...\n",
       "                                                     max_leaf_nodes=None,\n",
       "                                                     max_samples=None,\n",
       "                                                     min_impurity_decrease=0.0,\n",
       "                                                     min_samples_leaf=1,\n",
       "                                                     min_samples_split=2,\n",
       "                                                     min_weight_fraction_leaf=0.0,\n",
       "                                                     monotonic_cst=None,\n",
       "                                                     n_estimators=100,\n",
       "                                                     n_jobs=-1, oob_score=False,\n",
       "                                                     random_state=123,\n",
       "                                                     verbose=0,\n",
       "                                                     warm_start=False))],\n",
       "                 flatten_transform=True, n_jobs=-1, verbose=False,\n",
       "                 voting=&#x27;soft&#x27;,\n",
       "                 weights=[0.2, 0.8200000000000001, 0.41000000000000003])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>Gradient Boosting Classifier</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-111\" type=\"checkbox\" ><label for=\"sk-estimator-id-111\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;GradientBoostingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingClassifier(random_state=123)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>Light Gradient Boosting Machine</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-112\" type=\"checkbox\" ><label for=\"sk-estimator-id-112\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">LGBMClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>LGBMClassifier(n_jobs=-1, random_state=123)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>Random Forest Classifier</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-113\" type=\"checkbox\" ><label for=\"sk-estimator-id-113\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(n_jobs=-1, random_state=123)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "VotingClassifier(estimators=[('Gradient Boosting Classifier',\n",
       "                              GradientBoostingClassifier(ccp_alpha=0.0,\n",
       "                                                         criterion='friedman_mse',\n",
       "                                                         init=None,\n",
       "                                                         learning_rate=0.1,\n",
       "                                                         loss='log_loss',\n",
       "                                                         max_depth=3,\n",
       "                                                         max_features=None,\n",
       "                                                         max_leaf_nodes=None,\n",
       "                                                         min_impurity_decrease=0.0,\n",
       "                                                         min_samples_leaf=1,\n",
       "                                                         min_samples_split=2,\n",
       "                                                         min_weight_fraction_leaf=0.0,\n",
       "                                                         n_estimators=100,\n",
       "                                                         n_iter_no_change=Non...\n",
       "                                                     max_leaf_nodes=None,\n",
       "                                                     max_samples=None,\n",
       "                                                     min_impurity_decrease=0.0,\n",
       "                                                     min_samples_leaf=1,\n",
       "                                                     min_samples_split=2,\n",
       "                                                     min_weight_fraction_leaf=0.0,\n",
       "                                                     monotonic_cst=None,\n",
       "                                                     n_estimators=100,\n",
       "                                                     n_jobs=-1, oob_score=False,\n",
       "                                                     random_state=123,\n",
       "                                                     verbose=0,\n",
       "                                                     warm_start=False))],\n",
       "                 flatten_transform=True, n_jobs=-1, verbose=False,\n",
       "                 voting='soft',\n",
       "                 weights=[0.2, 0.8200000000000001, 0.41000000000000003])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tune the blended model\n",
    "tuned_blend = tune_model(blended_model, optimize = 'Accuracy')\n",
    "tuned_blend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stack Models\n",
    "This function trains a meta-model over select estimators passed in the estimator_list parameter. The output of this function is a scoring grid with CV scores by fold. Metrics evaluated during CV can be accessed using the ``get_metrics`` function. Custom metrics can be added or removed using ``add_metric`` and ``remove_metric`` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_d1773_row10_col0, #T_d1773_row10_col1, #T_d1773_row10_col2, #T_d1773_row10_col3, #T_d1773_row10_col4, #T_d1773_row10_col5, #T_d1773_row10_col6 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_d1773\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_d1773_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th id=\"T_d1773_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th id=\"T_d1773_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th id=\"T_d1773_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th id=\"T_d1773_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th id=\"T_d1773_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th id=\"T_d1773_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_d1773_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_d1773_row0_col0\" class=\"data row0 col0\" >0.8254</td>\n",
       "      <td id=\"T_d1773_row0_col1\" class=\"data row0 col1\" >0.8691</td>\n",
       "      <td id=\"T_d1773_row0_col2\" class=\"data row0 col2\" >0.7917</td>\n",
       "      <td id=\"T_d1773_row0_col3\" class=\"data row0 col3\" >0.7600</td>\n",
       "      <td id=\"T_d1773_row0_col4\" class=\"data row0 col4\" >0.7755</td>\n",
       "      <td id=\"T_d1773_row0_col5\" class=\"data row0 col5\" >0.6328</td>\n",
       "      <td id=\"T_d1773_row0_col6\" class=\"data row0 col6\" >0.6331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1773_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_d1773_row1_col0\" class=\"data row1 col0\" >0.8571</td>\n",
       "      <td id=\"T_d1773_row1_col1\" class=\"data row1 col1\" >0.7981</td>\n",
       "      <td id=\"T_d1773_row1_col2\" class=\"data row1 col2\" >0.7917</td>\n",
       "      <td id=\"T_d1773_row1_col3\" class=\"data row1 col3\" >0.8261</td>\n",
       "      <td id=\"T_d1773_row1_col4\" class=\"data row1 col4\" >0.8085</td>\n",
       "      <td id=\"T_d1773_row1_col5\" class=\"data row1 col5\" >0.6947</td>\n",
       "      <td id=\"T_d1773_row1_col6\" class=\"data row1 col6\" >0.6951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1773_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_d1773_row2_col0\" class=\"data row2 col0\" >0.8095</td>\n",
       "      <td id=\"T_d1773_row2_col1\" class=\"data row2 col1\" >0.9199</td>\n",
       "      <td id=\"T_d1773_row2_col2\" class=\"data row2 col2\" >0.6250</td>\n",
       "      <td id=\"T_d1773_row2_col3\" class=\"data row2 col3\" >0.8333</td>\n",
       "      <td id=\"T_d1773_row2_col4\" class=\"data row2 col4\" >0.7143</td>\n",
       "      <td id=\"T_d1773_row2_col5\" class=\"data row2 col5\" >0.5758</td>\n",
       "      <td id=\"T_d1773_row2_col6\" class=\"data row2 col6\" >0.5892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1773_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_d1773_row3_col0\" class=\"data row3 col0\" >0.8387</td>\n",
       "      <td id=\"T_d1773_row3_col1\" class=\"data row3 col1\" >0.9052</td>\n",
       "      <td id=\"T_d1773_row3_col2\" class=\"data row3 col2\" >0.6522</td>\n",
       "      <td id=\"T_d1773_row3_col3\" class=\"data row3 col3\" >0.8824</td>\n",
       "      <td id=\"T_d1773_row3_col4\" class=\"data row3 col4\" >0.7500</td>\n",
       "      <td id=\"T_d1773_row3_col5\" class=\"data row3 col5\" >0.6349</td>\n",
       "      <td id=\"T_d1773_row3_col6\" class=\"data row3 col6\" >0.6507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1773_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_d1773_row4_col0\" class=\"data row4 col0\" >0.7419</td>\n",
       "      <td id=\"T_d1773_row4_col1\" class=\"data row4 col1\" >0.7072</td>\n",
       "      <td id=\"T_d1773_row4_col2\" class=\"data row4 col2\" >0.5417</td>\n",
       "      <td id=\"T_d1773_row4_col3\" class=\"data row4 col3\" >0.7222</td>\n",
       "      <td id=\"T_d1773_row4_col4\" class=\"data row4 col4\" >0.6190</td>\n",
       "      <td id=\"T_d1773_row4_col5\" class=\"data row4 col5\" >0.4299</td>\n",
       "      <td id=\"T_d1773_row4_col6\" class=\"data row4 col6\" >0.4401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1773_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_d1773_row5_col0\" class=\"data row5 col0\" >0.8065</td>\n",
       "      <td id=\"T_d1773_row5_col1\" class=\"data row5 col1\" >0.8366</td>\n",
       "      <td id=\"T_d1773_row5_col2\" class=\"data row5 col2\" >0.6250</td>\n",
       "      <td id=\"T_d1773_row5_col3\" class=\"data row5 col3\" >0.8333</td>\n",
       "      <td id=\"T_d1773_row5_col4\" class=\"data row5 col4\" >0.7143</td>\n",
       "      <td id=\"T_d1773_row5_col5\" class=\"data row5 col5\" >0.5724</td>\n",
       "      <td id=\"T_d1773_row5_col6\" class=\"data row5 col6\" >0.5860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1773_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_d1773_row6_col0\" class=\"data row6 col0\" >0.8871</td>\n",
       "      <td id=\"T_d1773_row6_col1\" class=\"data row6 col1\" >0.8750</td>\n",
       "      <td id=\"T_d1773_row6_col2\" class=\"data row6 col2\" >0.7917</td>\n",
       "      <td id=\"T_d1773_row6_col3\" class=\"data row6 col3\" >0.9048</td>\n",
       "      <td id=\"T_d1773_row6_col4\" class=\"data row6 col4\" >0.8444</td>\n",
       "      <td id=\"T_d1773_row6_col5\" class=\"data row6 col5\" >0.7565</td>\n",
       "      <td id=\"T_d1773_row6_col6\" class=\"data row6 col6\" >0.7606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1773_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_d1773_row7_col0\" class=\"data row7 col0\" >0.8387</td>\n",
       "      <td id=\"T_d1773_row7_col1\" class=\"data row7 col1\" >0.8213</td>\n",
       "      <td id=\"T_d1773_row7_col2\" class=\"data row7 col2\" >0.8333</td>\n",
       "      <td id=\"T_d1773_row7_col3\" class=\"data row7 col3\" >0.7692</td>\n",
       "      <td id=\"T_d1773_row7_col4\" class=\"data row7 col4\" >0.8000</td>\n",
       "      <td id=\"T_d1773_row7_col5\" class=\"data row7 col5\" >0.6652</td>\n",
       "      <td id=\"T_d1773_row7_col6\" class=\"data row7 col6\" >0.6667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1773_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_d1773_row8_col0\" class=\"data row8 col0\" >0.8065</td>\n",
       "      <td id=\"T_d1773_row8_col1\" class=\"data row8 col1\" >0.8564</td>\n",
       "      <td id=\"T_d1773_row8_col2\" class=\"data row8 col2\" >0.7083</td>\n",
       "      <td id=\"T_d1773_row8_col3\" class=\"data row8 col3\" >0.7727</td>\n",
       "      <td id=\"T_d1773_row8_col4\" class=\"data row8 col4\" >0.7391</td>\n",
       "      <td id=\"T_d1773_row8_col5\" class=\"data row8 col5\" >0.5857</td>\n",
       "      <td id=\"T_d1773_row8_col6\" class=\"data row8 col6\" >0.5871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1773_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_d1773_row9_col0\" class=\"data row9 col0\" >0.8387</td>\n",
       "      <td id=\"T_d1773_row9_col1\" class=\"data row9 col1\" >0.8893</td>\n",
       "      <td id=\"T_d1773_row9_col2\" class=\"data row9 col2\" >0.8333</td>\n",
       "      <td id=\"T_d1773_row9_col3\" class=\"data row9 col3\" >0.7692</td>\n",
       "      <td id=\"T_d1773_row9_col4\" class=\"data row9 col4\" >0.8000</td>\n",
       "      <td id=\"T_d1773_row9_col5\" class=\"data row9 col5\" >0.6652</td>\n",
       "      <td id=\"T_d1773_row9_col6\" class=\"data row9 col6\" >0.6667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1773_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_d1773_row10_col0\" class=\"data row10 col0\" >0.8250</td>\n",
       "      <td id=\"T_d1773_row10_col1\" class=\"data row10 col1\" >0.8478</td>\n",
       "      <td id=\"T_d1773_row10_col2\" class=\"data row10 col2\" >0.7194</td>\n",
       "      <td id=\"T_d1773_row10_col3\" class=\"data row10 col3\" >0.8073</td>\n",
       "      <td id=\"T_d1773_row10_col4\" class=\"data row10 col4\" >0.7565</td>\n",
       "      <td id=\"T_d1773_row10_col5\" class=\"data row10 col5\" >0.6213</td>\n",
       "      <td id=\"T_d1773_row10_col6\" class=\"data row10 col6\" >0.6275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1773_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_d1773_row11_col0\" class=\"data row11 col0\" >0.0364</td>\n",
       "      <td id=\"T_d1773_row11_col1\" class=\"data row11 col1\" >0.0588</td>\n",
       "      <td id=\"T_d1773_row11_col2\" class=\"data row11 col2\" >0.0978</td>\n",
       "      <td id=\"T_d1773_row11_col3\" class=\"data row11 col3\" >0.0551</td>\n",
       "      <td id=\"T_d1773_row11_col4\" class=\"data row11 col4\" >0.0611</td>\n",
       "      <td id=\"T_d1773_row11_col5\" class=\"data row11 col5\" >0.0838</td>\n",
       "      <td id=\"T_d1773_row11_col6\" class=\"data row11 col6\" >0.0812</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1e481196950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-22 {\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-22 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-22 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-22 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-22 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-22 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-22 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-22 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-22 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-22 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-22 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-22 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-22 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-22 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-22 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-22 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-22 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-22 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-22 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-22 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-22 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-22 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-22 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-22 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-22 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-22 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-22 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-22 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-22 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-22 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-22 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-22 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-22 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-22 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-22 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-22 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-22 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-22 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-22 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-22 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-22 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-22 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-22\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StackingClassifier(cv=5,\n",
       "                   estimators=[(&#x27;Gradient Boosting Classifier&#x27;,\n",
       "                                GradientBoostingClassifier(ccp_alpha=0.0,\n",
       "                                                           criterion=&#x27;friedman_mse&#x27;,\n",
       "                                                           init=None,\n",
       "                                                           learning_rate=0.1,\n",
       "                                                           loss=&#x27;log_loss&#x27;,\n",
       "                                                           max_depth=3,\n",
       "                                                           max_features=None,\n",
       "                                                           max_leaf_nodes=None,\n",
       "                                                           min_impurity_decrease=0.0,\n",
       "                                                           min_samples_leaf=1,\n",
       "                                                           min_samples_split=2,\n",
       "                                                           min_weight_fraction_leaf=0.0,\n",
       "                                                           n_estimators=100,\n",
       "                                                           n_iter_no_cha...\n",
       "                                                       random_state=123,\n",
       "                                                       verbose=0,\n",
       "                                                       warm_start=False))],\n",
       "                   final_estimator=LogisticRegression(C=1.0, class_weight=None,\n",
       "                                                      dual=False,\n",
       "                                                      fit_intercept=True,\n",
       "                                                      intercept_scaling=1,\n",
       "                                                      l1_ratio=None,\n",
       "                                                      max_iter=1000,\n",
       "                                                      multi_class=&#x27;auto&#x27;,\n",
       "                                                      n_jobs=None, penalty=&#x27;l2&#x27;,\n",
       "                                                      random_state=123,\n",
       "                                                      solver=&#x27;lbfgs&#x27;,\n",
       "                                                      tol=0.0001, verbose=0,\n",
       "                                                      warm_start=False),\n",
       "                   n_jobs=-1, passthrough=False, stack_method=&#x27;auto&#x27;,\n",
       "                   verbose=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-89\" type=\"checkbox\" ><label for=\"sk-estimator-id-89\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;StackingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.StackingClassifier.html\">?<span>Documentation for StackingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>StackingClassifier(cv=5,\n",
       "                   estimators=[(&#x27;Gradient Boosting Classifier&#x27;,\n",
       "                                GradientBoostingClassifier(ccp_alpha=0.0,\n",
       "                                                           criterion=&#x27;friedman_mse&#x27;,\n",
       "                                                           init=None,\n",
       "                                                           learning_rate=0.1,\n",
       "                                                           loss=&#x27;log_loss&#x27;,\n",
       "                                                           max_depth=3,\n",
       "                                                           max_features=None,\n",
       "                                                           max_leaf_nodes=None,\n",
       "                                                           min_impurity_decrease=0.0,\n",
       "                                                           min_samples_leaf=1,\n",
       "                                                           min_samples_split=2,\n",
       "                                                           min_weight_fraction_leaf=0.0,\n",
       "                                                           n_estimators=100,\n",
       "                                                           n_iter_no_cha...\n",
       "                                                       random_state=123,\n",
       "                                                       verbose=0,\n",
       "                                                       warm_start=False))],\n",
       "                   final_estimator=LogisticRegression(C=1.0, class_weight=None,\n",
       "                                                      dual=False,\n",
       "                                                      fit_intercept=True,\n",
       "                                                      intercept_scaling=1,\n",
       "                                                      l1_ratio=None,\n",
       "                                                      max_iter=1000,\n",
       "                                                      multi_class=&#x27;auto&#x27;,\n",
       "                                                      n_jobs=None, penalty=&#x27;l2&#x27;,\n",
       "                                                      random_state=123,\n",
       "                                                      solver=&#x27;lbfgs&#x27;,\n",
       "                                                      tol=0.0001, verbose=0,\n",
       "                                                      warm_start=False),\n",
       "                   n_jobs=-1, passthrough=False, stack_method=&#x27;auto&#x27;,\n",
       "                   verbose=0)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>Gradient Boosting Classifier</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-90\" type=\"checkbox\" ><label for=\"sk-estimator-id-90\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;GradientBoostingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingClassifier(random_state=123)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>Light Gradient Boosting Machine</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-91\" type=\"checkbox\" ><label for=\"sk-estimator-id-91\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">LGBMClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>LGBMClassifier(n_jobs=-1, random_state=123)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>Random Forest Classifier</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-92\" type=\"checkbox\" ><label for=\"sk-estimator-id-92\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(n_jobs=-1, random_state=123)</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>final_estimator</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-93\" type=\"checkbox\" ><label for=\"sk-estimator-id-93\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000, random_state=123)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "StackingClassifier(cv=5,\n",
       "                   estimators=[('Gradient Boosting Classifier',\n",
       "                                GradientBoostingClassifier(ccp_alpha=0.0,\n",
       "                                                           criterion='friedman_mse',\n",
       "                                                           init=None,\n",
       "                                                           learning_rate=0.1,\n",
       "                                                           loss='log_loss',\n",
       "                                                           max_depth=3,\n",
       "                                                           max_features=None,\n",
       "                                                           max_leaf_nodes=None,\n",
       "                                                           min_impurity_decrease=0.0,\n",
       "                                                           min_samples_leaf=1,\n",
       "                                                           min_samples_split=2,\n",
       "                                                           min_weight_fraction_leaf=0.0,\n",
       "                                                           n_estimators=100,\n",
       "                                                           n_iter_no_cha...\n",
       "                                                       random_state=123,\n",
       "                                                       verbose=0,\n",
       "                                                       warm_start=False))],\n",
       "                   final_estimator=LogisticRegression(C=1.0, class_weight=None,\n",
       "                                                      dual=False,\n",
       "                                                      fit_intercept=True,\n",
       "                                                      intercept_scaling=1,\n",
       "                                                      l1_ratio=None,\n",
       "                                                      max_iter=1000,\n",
       "                                                      multi_class='auto',\n",
       "                                                      n_jobs=None, penalty='l2',\n",
       "                                                      random_state=123,\n",
       "                                                      solver='lbfgs',\n",
       "                                                      tol=0.0001, verbose=0,\n",
       "                                                      warm_start=False),\n",
       "                   n_jobs=-1, passthrough=False, stack_method='auto',\n",
       "                   verbose=0)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# stack models\n",
    "stacked_model = stack_models(best_accuracy_models_top3)\n",
    "stacked_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_adf7e_row10_col0, #T_adf7e_row10_col1, #T_adf7e_row10_col2, #T_adf7e_row10_col3, #T_adf7e_row10_col4, #T_adf7e_row10_col5, #T_adf7e_row10_col6 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_adf7e\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_adf7e_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th id=\"T_adf7e_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th id=\"T_adf7e_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th id=\"T_adf7e_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th id=\"T_adf7e_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th id=\"T_adf7e_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th id=\"T_adf7e_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_adf7e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_adf7e_row0_col0\" class=\"data row0 col0\" >0.8254</td>\n",
       "      <td id=\"T_adf7e_row0_col1\" class=\"data row0 col1\" >0.8681</td>\n",
       "      <td id=\"T_adf7e_row0_col2\" class=\"data row0 col2\" >0.7917</td>\n",
       "      <td id=\"T_adf7e_row0_col3\" class=\"data row0 col3\" >0.7600</td>\n",
       "      <td id=\"T_adf7e_row0_col4\" class=\"data row0 col4\" >0.7755</td>\n",
       "      <td id=\"T_adf7e_row0_col5\" class=\"data row0 col5\" >0.6328</td>\n",
       "      <td id=\"T_adf7e_row0_col6\" class=\"data row0 col6\" >0.6331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_adf7e_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_adf7e_row1_col0\" class=\"data row1 col0\" >0.8571</td>\n",
       "      <td id=\"T_adf7e_row1_col1\" class=\"data row1 col1\" >0.8205</td>\n",
       "      <td id=\"T_adf7e_row1_col2\" class=\"data row1 col2\" >0.7917</td>\n",
       "      <td id=\"T_adf7e_row1_col3\" class=\"data row1 col3\" >0.8261</td>\n",
       "      <td id=\"T_adf7e_row1_col4\" class=\"data row1 col4\" >0.8085</td>\n",
       "      <td id=\"T_adf7e_row1_col5\" class=\"data row1 col5\" >0.6947</td>\n",
       "      <td id=\"T_adf7e_row1_col6\" class=\"data row1 col6\" >0.6951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_adf7e_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_adf7e_row2_col0\" class=\"data row2 col0\" >0.8095</td>\n",
       "      <td id=\"T_adf7e_row2_col1\" class=\"data row2 col1\" >0.9167</td>\n",
       "      <td id=\"T_adf7e_row2_col2\" class=\"data row2 col2\" >0.5833</td>\n",
       "      <td id=\"T_adf7e_row2_col3\" class=\"data row2 col3\" >0.8750</td>\n",
       "      <td id=\"T_adf7e_row2_col4\" class=\"data row2 col4\" >0.7000</td>\n",
       "      <td id=\"T_adf7e_row2_col5\" class=\"data row2 col5\" >0.5685</td>\n",
       "      <td id=\"T_adf7e_row2_col6\" class=\"data row2 col6\" >0.5936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_adf7e_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_adf7e_row3_col0\" class=\"data row3 col0\" >0.8548</td>\n",
       "      <td id=\"T_adf7e_row3_col1\" class=\"data row3 col1\" >0.9142</td>\n",
       "      <td id=\"T_adf7e_row3_col2\" class=\"data row3 col2\" >0.6522</td>\n",
       "      <td id=\"T_adf7e_row3_col3\" class=\"data row3 col3\" >0.9375</td>\n",
       "      <td id=\"T_adf7e_row3_col4\" class=\"data row3 col4\" >0.7692</td>\n",
       "      <td id=\"T_adf7e_row3_col5\" class=\"data row3 col5\" >0.6683</td>\n",
       "      <td id=\"T_adf7e_row3_col6\" class=\"data row3 col6\" >0.6917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_adf7e_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_adf7e_row4_col0\" class=\"data row4 col0\" >0.7419</td>\n",
       "      <td id=\"T_adf7e_row4_col1\" class=\"data row4 col1\" >0.7039</td>\n",
       "      <td id=\"T_adf7e_row4_col2\" class=\"data row4 col2\" >0.5417</td>\n",
       "      <td id=\"T_adf7e_row4_col3\" class=\"data row4 col3\" >0.7222</td>\n",
       "      <td id=\"T_adf7e_row4_col4\" class=\"data row4 col4\" >0.6190</td>\n",
       "      <td id=\"T_adf7e_row4_col5\" class=\"data row4 col5\" >0.4299</td>\n",
       "      <td id=\"T_adf7e_row4_col6\" class=\"data row4 col6\" >0.4401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_adf7e_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_adf7e_row5_col0\" class=\"data row5 col0\" >0.8065</td>\n",
       "      <td id=\"T_adf7e_row5_col1\" class=\"data row5 col1\" >0.8410</td>\n",
       "      <td id=\"T_adf7e_row5_col2\" class=\"data row5 col2\" >0.6250</td>\n",
       "      <td id=\"T_adf7e_row5_col3\" class=\"data row5 col3\" >0.8333</td>\n",
       "      <td id=\"T_adf7e_row5_col4\" class=\"data row5 col4\" >0.7143</td>\n",
       "      <td id=\"T_adf7e_row5_col5\" class=\"data row5 col5\" >0.5724</td>\n",
       "      <td id=\"T_adf7e_row5_col6\" class=\"data row5 col6\" >0.5860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_adf7e_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_adf7e_row6_col0\" class=\"data row6 col0\" >0.8871</td>\n",
       "      <td id=\"T_adf7e_row6_col1\" class=\"data row6 col1\" >0.8783</td>\n",
       "      <td id=\"T_adf7e_row6_col2\" class=\"data row6 col2\" >0.7917</td>\n",
       "      <td id=\"T_adf7e_row6_col3\" class=\"data row6 col3\" >0.9048</td>\n",
       "      <td id=\"T_adf7e_row6_col4\" class=\"data row6 col4\" >0.8444</td>\n",
       "      <td id=\"T_adf7e_row6_col5\" class=\"data row6 col5\" >0.7565</td>\n",
       "      <td id=\"T_adf7e_row6_col6\" class=\"data row6 col6\" >0.7606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_adf7e_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_adf7e_row7_col0\" class=\"data row7 col0\" >0.8710</td>\n",
       "      <td id=\"T_adf7e_row7_col1\" class=\"data row7 col1\" >0.8224</td>\n",
       "      <td id=\"T_adf7e_row7_col2\" class=\"data row7 col2\" >0.8333</td>\n",
       "      <td id=\"T_adf7e_row7_col3\" class=\"data row7 col3\" >0.8333</td>\n",
       "      <td id=\"T_adf7e_row7_col4\" class=\"data row7 col4\" >0.8333</td>\n",
       "      <td id=\"T_adf7e_row7_col5\" class=\"data row7 col5\" >0.7281</td>\n",
       "      <td id=\"T_adf7e_row7_col6\" class=\"data row7 col6\" >0.7281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_adf7e_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_adf7e_row8_col0\" class=\"data row8 col0\" >0.8226</td>\n",
       "      <td id=\"T_adf7e_row8_col1\" class=\"data row8 col1\" >0.8520</td>\n",
       "      <td id=\"T_adf7e_row8_col2\" class=\"data row8 col2\" >0.7083</td>\n",
       "      <td id=\"T_adf7e_row8_col3\" class=\"data row8 col3\" >0.8095</td>\n",
       "      <td id=\"T_adf7e_row8_col4\" class=\"data row8 col4\" >0.7556</td>\n",
       "      <td id=\"T_adf7e_row8_col5\" class=\"data row8 col5\" >0.6173</td>\n",
       "      <td id=\"T_adf7e_row8_col6\" class=\"data row8 col6\" >0.6207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_adf7e_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_adf7e_row9_col0\" class=\"data row9 col0\" >0.8387</td>\n",
       "      <td id=\"T_adf7e_row9_col1\" class=\"data row9 col1\" >0.8882</td>\n",
       "      <td id=\"T_adf7e_row9_col2\" class=\"data row9 col2\" >0.8333</td>\n",
       "      <td id=\"T_adf7e_row9_col3\" class=\"data row9 col3\" >0.7692</td>\n",
       "      <td id=\"T_adf7e_row9_col4\" class=\"data row9 col4\" >0.8000</td>\n",
       "      <td id=\"T_adf7e_row9_col5\" class=\"data row9 col5\" >0.6652</td>\n",
       "      <td id=\"T_adf7e_row9_col6\" class=\"data row9 col6\" >0.6667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_adf7e_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_adf7e_row10_col0\" class=\"data row10 col0\" >0.8315</td>\n",
       "      <td id=\"T_adf7e_row10_col1\" class=\"data row10 col1\" >0.8505</td>\n",
       "      <td id=\"T_adf7e_row10_col2\" class=\"data row10 col2\" >0.7152</td>\n",
       "      <td id=\"T_adf7e_row10_col3\" class=\"data row10 col3\" >0.8271</td>\n",
       "      <td id=\"T_adf7e_row10_col4\" class=\"data row10 col4\" >0.7620</td>\n",
       "      <td id=\"T_adf7e_row10_col5\" class=\"data row10 col5\" >0.6333</td>\n",
       "      <td id=\"T_adf7e_row10_col6\" class=\"data row10 col6\" >0.6416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_adf7e_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_adf7e_row11_col0\" class=\"data row11 col0\" >0.0389</td>\n",
       "      <td id=\"T_adf7e_row11_col1\" class=\"data row11 col1\" >0.0585</td>\n",
       "      <td id=\"T_adf7e_row11_col2\" class=\"data row11 col2\" >0.1025</td>\n",
       "      <td id=\"T_adf7e_row11_col3\" class=\"data row11 col3\" >0.0630</td>\n",
       "      <td id=\"T_adf7e_row11_col4\" class=\"data row11 col4\" >0.0651</td>\n",
       "      <td id=\"T_adf7e_row11_col5\" class=\"data row11 col5\" >0.0893</td>\n",
       "      <td id=\"T_adf7e_row11_col6\" class=\"data row11 col6\" >0.0861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1e4fbd4e3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "58cc235d89e74209a1d1539f2a1a4dd1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing:   0%|          | 0/7 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-23 {\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-23 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-23 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-23 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-23 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-23 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-23 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-23 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-23 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-23 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-23 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-23 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-23 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-23 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-23 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-23 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-23 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-23 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-23 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-23 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-23 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-23 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-23 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-23 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-23 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-23 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-23 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-23 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-23 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-23 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-23 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-23 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-23 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-23 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-23 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-23 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-23 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-23 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-23 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-23 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-23 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-23 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-23\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StackingClassifier(cv=5,\n",
       "                   estimators=[(&#x27;Gradient Boosting Classifier&#x27;,\n",
       "                                GradientBoostingClassifier(ccp_alpha=0.0,\n",
       "                                                           criterion=&#x27;friedman_mse&#x27;,\n",
       "                                                           init=None,\n",
       "                                                           learning_rate=0.1,\n",
       "                                                           loss=&#x27;log_loss&#x27;,\n",
       "                                                           max_depth=3,\n",
       "                                                           max_features=None,\n",
       "                                                           max_leaf_nodes=None,\n",
       "                                                           min_impurity_decrease=0.0,\n",
       "                                                           min_samples_leaf=1,\n",
       "                                                           min_samples_split=2,\n",
       "                                                           min_weight_fraction_leaf=0.0,\n",
       "                                                           n_estimators=100,\n",
       "                                                           n_iter_no_cha...\n",
       "                                                       random_state=123,\n",
       "                                                       verbose=0,\n",
       "                                                       warm_start=False))],\n",
       "                   final_estimator=LogisticRegression(C=0.056, class_weight={},\n",
       "                                                      dual=False,\n",
       "                                                      fit_intercept=True,\n",
       "                                                      intercept_scaling=1,\n",
       "                                                      l1_ratio=None,\n",
       "                                                      max_iter=1000,\n",
       "                                                      multi_class=&#x27;auto&#x27;,\n",
       "                                                      n_jobs=None, penalty=&#x27;l2&#x27;,\n",
       "                                                      random_state=123,\n",
       "                                                      solver=&#x27;lbfgs&#x27;,\n",
       "                                                      tol=0.0001, verbose=0,\n",
       "                                                      warm_start=False),\n",
       "                   n_jobs=-1, passthrough=False, stack_method=&#x27;auto&#x27;,\n",
       "                   verbose=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-94\" type=\"checkbox\" ><label for=\"sk-estimator-id-94\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;StackingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.StackingClassifier.html\">?<span>Documentation for StackingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>StackingClassifier(cv=5,\n",
       "                   estimators=[(&#x27;Gradient Boosting Classifier&#x27;,\n",
       "                                GradientBoostingClassifier(ccp_alpha=0.0,\n",
       "                                                           criterion=&#x27;friedman_mse&#x27;,\n",
       "                                                           init=None,\n",
       "                                                           learning_rate=0.1,\n",
       "                                                           loss=&#x27;log_loss&#x27;,\n",
       "                                                           max_depth=3,\n",
       "                                                           max_features=None,\n",
       "                                                           max_leaf_nodes=None,\n",
       "                                                           min_impurity_decrease=0.0,\n",
       "                                                           min_samples_leaf=1,\n",
       "                                                           min_samples_split=2,\n",
       "                                                           min_weight_fraction_leaf=0.0,\n",
       "                                                           n_estimators=100,\n",
       "                                                           n_iter_no_cha...\n",
       "                                                       random_state=123,\n",
       "                                                       verbose=0,\n",
       "                                                       warm_start=False))],\n",
       "                   final_estimator=LogisticRegression(C=0.056, class_weight={},\n",
       "                                                      dual=False,\n",
       "                                                      fit_intercept=True,\n",
       "                                                      intercept_scaling=1,\n",
       "                                                      l1_ratio=None,\n",
       "                                                      max_iter=1000,\n",
       "                                                      multi_class=&#x27;auto&#x27;,\n",
       "                                                      n_jobs=None, penalty=&#x27;l2&#x27;,\n",
       "                                                      random_state=123,\n",
       "                                                      solver=&#x27;lbfgs&#x27;,\n",
       "                                                      tol=0.0001, verbose=0,\n",
       "                                                      warm_start=False),\n",
       "                   n_jobs=-1, passthrough=False, stack_method=&#x27;auto&#x27;,\n",
       "                   verbose=0)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>Gradient Boosting Classifier</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-95\" type=\"checkbox\" ><label for=\"sk-estimator-id-95\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;GradientBoostingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingClassifier(random_state=123)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>Light Gradient Boosting Machine</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-96\" type=\"checkbox\" ><label for=\"sk-estimator-id-96\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">LGBMClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>LGBMClassifier(n_jobs=-1, random_state=123)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>Random Forest Classifier</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-97\" type=\"checkbox\" ><label for=\"sk-estimator-id-97\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(n_jobs=-1, random_state=123)</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>final_estimator</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-98\" type=\"checkbox\" ><label for=\"sk-estimator-id-98\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.056, class_weight={}, max_iter=1000, random_state=123)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "StackingClassifier(cv=5,\n",
       "                   estimators=[('Gradient Boosting Classifier',\n",
       "                                GradientBoostingClassifier(ccp_alpha=0.0,\n",
       "                                                           criterion='friedman_mse',\n",
       "                                                           init=None,\n",
       "                                                           learning_rate=0.1,\n",
       "                                                           loss='log_loss',\n",
       "                                                           max_depth=3,\n",
       "                                                           max_features=None,\n",
       "                                                           max_leaf_nodes=None,\n",
       "                                                           min_impurity_decrease=0.0,\n",
       "                                                           min_samples_leaf=1,\n",
       "                                                           min_samples_split=2,\n",
       "                                                           min_weight_fraction_leaf=0.0,\n",
       "                                                           n_estimators=100,\n",
       "                                                           n_iter_no_cha...\n",
       "                                                       random_state=123,\n",
       "                                                       verbose=0,\n",
       "                                                       warm_start=False))],\n",
       "                   final_estimator=LogisticRegression(C=0.056, class_weight={},\n",
       "                                                      dual=False,\n",
       "                                                      fit_intercept=True,\n",
       "                                                      intercept_scaling=1,\n",
       "                                                      l1_ratio=None,\n",
       "                                                      max_iter=1000,\n",
       "                                                      multi_class='auto',\n",
       "                                                      n_jobs=None, penalty='l2',\n",
       "                                                      random_state=123,\n",
       "                                                      solver='lbfgs',\n",
       "                                                      tol=0.0001, verbose=0,\n",
       "                                                      warm_start=False),\n",
       "                   n_jobs=-1, passthrough=False, stack_method='auto',\n",
       "                   verbose=0)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tune the blended model\n",
    "tuned_stack = tune_model(stacked_model, optimize = 'Accuracy')\n",
    "tuned_stack"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>PassengerId</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Sex_1</th>\n",
       "      <th>Cabin_1</th>\n",
       "      <th>Embarked_1</th>\n",
       "      <th>Embarked_2</th>\n",
       "      <th>prediction_label</th>\n",
       "      <th>prediction_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0.39</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>1.36</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>0.48</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2.52</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Age  Fare  SibSp  Parch  Pclass_2  Pclass_3  Sex_1  Cabin_1  \\\n",
       "0          892 0.39 -0.49  -0.48  -0.44     False      True   True    False   \n",
       "1          893 1.36 -0.51   0.48  -0.44     False      True  False    False   \n",
       "2          894 2.52 -0.46  -0.48  -0.44      True     False   True    False   \n",
       "\n",
       "   Embarked_1  Embarked_2  prediction_label  prediction_score  \n",
       "0        True       False                 0              0.94  \n",
       "1       False        True                 0              0.89  \n",
       "2        True       False                 0              0.79  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict on test data\n",
    "predictions_gbc = predict_model(tuned_gbc, data=test_data)\n",
    "predictions_blend3_gbc_lgbm_rf = predict_model(tuned_blend, data=test_data)\n",
    "predictions_stack3_gbc_lgbm_rf = predict_model(tuned_stack, data=test_data)\n",
    "predictions_gbc.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         0\n",
       "2          894         0"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# structure the Kaggle submission files\n",
    "\n",
    "# gradient boosting classifier\n",
    "submission_gbc = predictions_gbc[['PassengerId', 'prediction_label']]\n",
    "submission_gbc.columns = ['PassengerId', 'Survived']\n",
    "\n",
    "# blend3 model of gradient boosting classifier, lightgbm and random forest\n",
    "submission_blend3_gbc_lgbm_rf = predictions_blend3_gbc_lgbm_rf[['PassengerId', 'prediction_label']]\n",
    "submission_blend3_gbc_lgbm_rf.columns = ['PassengerId', 'Survived']\n",
    "\n",
    "# stack3 model of gradient boosting classifier, lightgbm and random forest\n",
    "submission_stack3_gbc_lgbm_rf = predictions_stack3_gbc_lgbm_rf[['PassengerId', 'prediction_label']]\n",
    "submission_stack3_gbc_lgbm_rf.columns = ['PassengerId', 'Survived']\n",
    "\n",
    "submission_gbc.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the submission files\n",
    "submission_gbc.to_csv('submissions/gbc.csv', index=False)\n",
    "submission_blend3_gbc_lgbm_rf.to_csv('submissions/blend3_gbc_lgbm_rf.csv', index=False)\n",
    "submission_stack3_gbc_lgbm_rf.to_csv('submissions/stack3_gbc_lgbm_rf.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuned Model Performance\n",
    "\n",
    "| Model                           | Accuracy | Accuracy_tuned |  Accuracy Kaggle test_set | \n",
    "|---------------------------------|----------|----------------|-----------|\n",
    "| Gradient Boosting Classifier    | 82.5     | 83.5           | 76.6    |\n",
    "| 3x blend (gbm, lgbm, rf)        | 82.3     | 82.8           | 75.8    |\n",
    "| 3x stack (gbm, lgbm, rf)        | 82.5     | 83.2           | 77.0    |\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "kaggle_ML_env",
   "language": "python",
   "name": "kaggle_ml_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}