Bagging INtroduction.ipynb

{
 "cells": [
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "start_time": "2023-04-29T21:11:53.984349Z",
     "end_time": "2023-04-29T21:11:55.049695Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "df=pd.read_csv('IRIS.csv')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T21:12:02.301509Z",
     "end_time": "2023-04-29T21:12:02.332377Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "   sepal_length  sepal_width  petal_length  petal_width      species\n0           5.1          3.5           1.4          0.2  Iris-setosa\n1           4.9          3.0           1.4          0.2  Iris-setosa\n2           4.7          3.2           1.3          0.2  Iris-setosa\n3           4.6          3.1           1.5          0.2  Iris-setosa\n4           5.0          3.6           1.4          0.2  Iris-setosa",
      "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>sepal_length</th>\n      <th>sepal_width</th>\n      <th>petal_length</th>\n      <th>petal_width</th>\n      <th>species</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5.1</td>\n      <td>3.5</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>Iris-setosa</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.9</td>\n      <td>3.0</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>Iris-setosa</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.7</td>\n      <td>3.2</td>\n      <td>1.3</td>\n      <td>0.2</td>\n      <td>Iris-setosa</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.6</td>\n      <td>3.1</td>\n      <td>1.5</td>\n      <td>0.2</td>\n      <td>Iris-setosa</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>3.6</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>Iris-setosa</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T21:12:05.674859Z",
     "end_time": "2023-04-29T21:12:05.725032Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "df=df.iloc[:,1:]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T21:12:31.656754Z",
     "end_time": "2023-04-29T21:12:31.708856Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "     sepal_width  petal_length  petal_width         species\n0            3.5           1.4          0.2     Iris-setosa\n1            3.0           1.4          0.2     Iris-setosa\n2            3.2           1.3          0.2     Iris-setosa\n3            3.1           1.5          0.2     Iris-setosa\n4            3.6           1.4          0.2     Iris-setosa\n..           ...           ...          ...             ...\n145          3.0           5.2          2.3  Iris-virginica\n146          2.5           5.0          1.9  Iris-virginica\n147          3.0           5.2          2.0  Iris-virginica\n148          3.4           5.4          2.3  Iris-virginica\n149          3.0           5.1          1.8  Iris-virginica\n\n[150 rows x 4 columns]",
      "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>sepal_width</th>\n      <th>petal_length</th>\n      <th>petal_width</th>\n      <th>species</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3.5</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>Iris-setosa</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3.0</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>Iris-setosa</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3.2</td>\n      <td>1.3</td>\n      <td>0.2</td>\n      <td>Iris-setosa</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3.1</td>\n      <td>1.5</td>\n      <td>0.2</td>\n      <td>Iris-setosa</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3.6</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>Iris-setosa</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>145</th>\n      <td>3.0</td>\n      <td>5.2</td>\n      <td>2.3</td>\n      <td>Iris-virginica</td>\n    </tr>\n    <tr>\n      <th>146</th>\n      <td>2.5</td>\n      <td>5.0</td>\n      <td>1.9</td>\n      <td>Iris-virginica</td>\n    </tr>\n    <tr>\n      <th>147</th>\n      <td>3.0</td>\n      <td>5.2</td>\n      <td>2.0</td>\n      <td>Iris-virginica</td>\n    </tr>\n    <tr>\n      <th>148</th>\n      <td>3.4</td>\n      <td>5.4</td>\n      <td>2.3</td>\n      <td>Iris-virginica</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>3.0</td>\n      <td>5.1</td>\n      <td>1.8</td>\n      <td>Iris-virginica</td>\n    </tr>\n  </tbody>\n</table>\n<p>150 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T21:12:33.166741Z",
     "end_time": "2023-04-29T21:12:33.196777Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le=LabelEncoder()\n",
    "df['species']=le.fit_transform(df['species'])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T21:13:28.671334Z",
     "end_time": "2023-04-29T21:13:28.841693Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "   sepal_width  petal_length  petal_width  species\n0          3.5           1.4          0.2        0\n1          3.0           1.4          0.2        0\n2          3.2           1.3          0.2        0\n3          3.1           1.5          0.2        0\n4          3.6           1.4          0.2        0",
      "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>sepal_width</th>\n      <th>petal_length</th>\n      <th>petal_width</th>\n      <th>species</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3.5</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3.0</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3.2</td>\n      <td>1.3</td>\n      <td>0.2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3.1</td>\n      <td>1.5</td>\n      <td>0.2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3.6</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T21:13:33.264154Z",
     "end_time": "2023-04-29T21:13:33.311276Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "df=df[df['species']!=0][['sepal_width','petal_length','species']]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T21:14:10.911827Z",
     "end_time": "2023-04-29T21:14:10.922847Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "    sepal_width  petal_length  species\n50          3.2           4.7        1\n51          3.2           4.5        1\n52          3.1           4.9        1\n53          2.3           4.0        1\n54          2.8           4.6        1",
      "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>sepal_width</th>\n      <th>petal_length</th>\n      <th>species</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>50</th>\n      <td>3.2</td>\n      <td>4.7</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>3.2</td>\n      <td>4.5</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>3.1</td>\n      <td>4.9</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>2.3</td>\n      <td>4.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>2.8</td>\n      <td>4.6</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T21:14:15.657046Z",
     "end_time": "2023-04-29T21:14:15.699902Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.collections.PathCollection at 0x7fa8e6034ca0>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['sepal_width'],df['petal_length'],c=df['species'],cmap='winter')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T21:15:17.839364Z",
     "end_time": "2023-04-29T21:15:18.136256Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Applying Bootstrapping"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "df=df.sample(100)\n",
    "df_train=df.iloc[:60,:].sample(10)\n",
    "df_val=df.iloc[60:80,:].sample(5)\n",
    "df_test=df.iloc[80:,:].sample(5)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T21:16:59.550331Z",
     "end_time": "2023-04-29T21:16:59.563697Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "     sepal_width  petal_length  species\n57           2.4           3.3        1\n119          2.2           5.0        2\n125          3.2           6.0        2\n140          3.1           5.6        2\n126          2.8           4.8        2\n70           3.2           4.8        1\n149          3.0           5.1        2\n79           2.6           3.5        1\n144          3.3           5.7        2\n138          3.0           4.8        2",
      "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>sepal_width</th>\n      <th>petal_length</th>\n      <th>species</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>57</th>\n      <td>2.4</td>\n      <td>3.3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>119</th>\n      <td>2.2</td>\n      <td>5.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>125</th>\n      <td>3.2</td>\n      <td>6.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>140</th>\n      <td>3.1</td>\n      <td>5.6</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>126</th>\n      <td>2.8</td>\n      <td>4.8</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>70</th>\n      <td>3.2</td>\n      <td>4.8</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>3.0</td>\n      <td>5.1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>79</th>\n      <td>2.6</td>\n      <td>3.5</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>144</th>\n      <td>3.3</td>\n      <td>5.7</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>138</th>\n      <td>3.0</td>\n      <td>4.8</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T21:17:07.539649Z",
     "end_time": "2023-04-29T21:17:07.585848Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "     sepal_width  petal_length  species\n59           2.7           3.9        1\n55           2.8           4.5        1\n136          3.4           5.6        2\n74           2.9           4.3        1\n141          3.1           5.1        2",
      "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>sepal_width</th>\n      <th>petal_length</th>\n      <th>species</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>59</th>\n      <td>2.7</td>\n      <td>3.9</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>2.8</td>\n      <td>4.5</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>136</th>\n      <td>3.4</td>\n      <td>5.6</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>2.9</td>\n      <td>4.3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>141</th>\n      <td>3.1</td>\n      <td>5.1</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T21:17:42.073040Z",
     "end_time": "2023-04-29T21:17:42.121693Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "     sepal_width  petal_length  species\n67           2.7           4.1        1\n130          2.8           6.1        2\n115          3.2           5.3        2\n133          2.8           5.1        2\n134          2.6           5.6        2",
      "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>sepal_width</th>\n      <th>petal_length</th>\n      <th>species</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>67</th>\n      <td>2.7</td>\n      <td>4.1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>130</th>\n      <td>2.8</td>\n      <td>6.1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>115</th>\n      <td>3.2</td>\n      <td>5.3</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>133</th>\n      <td>2.8</td>\n      <td>5.1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>134</th>\n      <td>2.6</td>\n      <td>5.6</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_val"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T21:17:45.314394Z",
     "end_time": "2023-04-29T21:17:45.342059Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [],
   "source": [
    "X_test=df_val.iloc[:,0:2].values\n",
    "y_test=df_val.iloc[:,-1].values"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T21:19:52.593364Z",
     "end_time": "2023-04-29T21:19:52.653747Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[2.7, 4.1],\n       [2.8, 6.1],\n       [3.2, 5.3],\n       [2.8, 5.1],\n       [2.6, 5.6]])"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T21:19:52.918061Z",
     "end_time": "2023-04-29T21:19:52.972501Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "data": {
      "text/plain": "array([1, 2, 2, 2, 2])"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T21:19:56.212442Z",
     "end_time": "2023-04-29T21:19:56.226277Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Case1 - Bagging"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# training decision tree"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [],
   "source": [
    "# data for tree 1\n",
    "df_bag=df_train.sample(8,replace=True) # by using replace we are doing sampling with replacement\n",
    "\n",
    "X=df_bag.iloc[:,0:2]\n",
    "y=df_bag.iloc[:,-1]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T21:21:40.592429Z",
     "end_time": "2023-04-29T21:21:40.628302Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.tree import plot_tree\n",
    "from mlxtend.plotting import plot_decision_regions\n",
    "from sklearn.metrics import accuracy_score"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T21:22:34.162411Z",
     "end_time": "2023-04-29T21:22:34.320714Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [],
   "source": [
    "dt_bag1=DecisionTreeClassifier()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T21:22:41.412115Z",
     "end_time": "2023-04-29T21:22:41.429890Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [],
   "source": [
    "def evaluate(clf,X,y):\n",
    "    clf.fit(X,y)\n",
    "    plot_tree(clf)\n",
    "    plt.show()\n",
    "    plot_decision_regions(X.values,y.values,clf=clf, legend=2)\n",
    "    y_pred=clf.predict(X_test)\n",
    "    print(accuracy_score(y_test,y_pred))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T21:25:04.767032Z",
     "end_time": "2023-04-29T21:25:04.774394Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zuhaib/anaconda3/lib/python3.10/site-packages/sklearn/base.py:420: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n",
      "/home/zuhaib/anaconda3/lib/python3.10/site-packages/sklearn/base.py:420: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate(dt_bag1,X,y)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T21:25:27.030824Z",
     "end_time": "2023-04-29T21:25:27.672888Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "data": {
      "text/plain": "     sepal_width  petal_length  species\n149          3.0           5.1        2\n79           2.6           3.5        1\n138          3.0           4.8        2\n144          3.3           5.7        2\n70           3.2           4.8        1\n138          3.0           4.8        2\n125          3.2           6.0        2\n144          3.3           5.7        2",
      "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>sepal_width</th>\n      <th>petal_length</th>\n      <th>species</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>149</th>\n      <td>3.0</td>\n      <td>5.1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>79</th>\n      <td>2.6</td>\n      <td>3.5</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>138</th>\n      <td>3.0</td>\n      <td>4.8</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>144</th>\n      <td>3.3</td>\n      <td>5.7</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>70</th>\n      <td>3.2</td>\n      <td>4.8</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>138</th>\n      <td>3.0</td>\n      <td>4.8</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>125</th>\n      <td>3.2</td>\n      <td>6.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>144</th>\n      <td>3.3</td>\n      <td>5.7</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data for tree 2\n",
    "df_bag=df_train.sample(8,replace=True) # by using replace we are doing sampling with replacement\n",
    "\n",
    "X=df_bag.iloc[:,0:2]\n",
    "y=df_bag.iloc[:,-1]\n",
    "\n",
    "df_bag"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T21:27:11.827781Z",
     "end_time": "2023-04-29T21:27:11.858609Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zuhaib/anaconda3/lib/python3.10/site-packages/sklearn/base.py:420: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n",
      "/home/zuhaib/anaconda3/lib/python3.10/site-packages/sklearn/base.py:420: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt_bag2=DecisionTreeClassifier()\n",
    "evaluate(dt_bag2,X,y)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T21:27:56.639599Z",
     "end_time": "2023-04-29T21:27:57.377896Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "data": {
      "text/plain": "     sepal_width  petal_length  species\n57           2.4           3.3        1\n119          2.2           5.0        2\n140          3.1           5.6        2\n126          2.8           4.8        2\n140          3.1           5.6        2\n144          3.3           5.7        2\n70           3.2           4.8        1\n149          3.0           5.1        2",
      "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>sepal_width</th>\n      <th>petal_length</th>\n      <th>species</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>57</th>\n      <td>2.4</td>\n      <td>3.3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>119</th>\n      <td>2.2</td>\n      <td>5.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>140</th>\n      <td>3.1</td>\n      <td>5.6</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>126</th>\n      <td>2.8</td>\n      <td>4.8</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>140</th>\n      <td>3.1</td>\n      <td>5.6</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>144</th>\n      <td>3.3</td>\n      <td>5.7</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>70</th>\n      <td>3.2</td>\n      <td>4.8</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>3.0</td>\n      <td>5.1</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data for tree 3\n",
    "df_bag=df_train.sample(8,replace=True) # by using replace we are doing sampling with replacement\n",
    "\n",
    "X=df_bag.iloc[:,0:2]\n",
    "y=df_bag.iloc[:,-1]\n",
    "\n",
    "df_bag"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T21:28:15.614109Z",
     "end_time": "2023-04-29T21:28:15.668068Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zuhaib/anaconda3/lib/python3.10/site-packages/sklearn/base.py:420: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n",
      "/home/zuhaib/anaconda3/lib/python3.10/site-packages/sklearn/base.py:420: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt_bag3=DecisionTreeClassifier()\n",
    "evaluate(dt_bag3,X,y)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T21:29:13.091210Z",
     "end_time": "2023-04-29T21:29:13.735049Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Aggregation"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "     sepal_width  petal_length  species\n59           2.7           3.9        1\n55           2.8           4.5        1\n136          3.4           5.6        2\n74           2.9           4.3        1\n141          3.1           5.1        2",
      "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>sepal_width</th>\n      <th>petal_length</th>\n      <th>species</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>59</th>\n      <td>2.7</td>\n      <td>3.9</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>2.8</td>\n      <td>4.5</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>136</th>\n      <td>3.4</td>\n      <td>5.6</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>2.9</td>\n      <td>4.3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>141</th>\n      <td>3.1</td>\n      <td>5.1</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-29T21:29:59.497937Z",
     "end_time": "2023-04-29T21:29:59.537964Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictor 1 [2]\n",
      "Predictor 2 [2]\n",
      "Predictor 3 [2]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zuhaib/anaconda3/lib/python3.10/site-packages/sklearn/base.py:420: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n",
      "/home/zuhaib/anaconda3/lib/python3.10/site-packages/sklearn/base.py:420: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n",
      "/home/zuhaib/anaconda3/lib/python3.10/site-packages/sklearn/base.py:420: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "print(\"Predictor 1\",dt_bag1.predict(np.array([2.9,4.7]).reshape(1,2)))\n",
    "print(\"Predictor 2\",dt_bag2.predict(np.array([2.9,4.7]).reshape(1,2)))\n",
    "print(\"Predictor 3\",dt_bag3.predict(np.array([2.9,4.7]).reshape(1,2)))"
   ],
   "metadata": {
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    "ExecuteTime": {
     "start_time": "2023-04-29T21:33:26.091100Z",
     "end_time": "2023-04-29T21:33:26.139805Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
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