Ensemble models with Decision Trees.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Árvore de Decisão com Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importando pacotes necessários**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "% matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "colors = plt.rcParams['axes.color_cycle'][1], plt.rcParams['axes.color_cycle'][4]\n",
    "\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Carregando dados, gerados localmente para o exemplo 'João joga tênis?' **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dataset = pandas.read_csv(\"./datasets/PlayTennis.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample</th>\n",
       "      <th>target</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windy</th>\n",
       "      <th>outlook</th>\n",
       "      <th>tired</th>\n",
       "      <th>tshirt</th>\n",
       "      <th>friends</th>\n",
       "      <th>went_out</th>\n",
       "      <th>temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>D0</td>\n",
       "      <td>yes</td>\n",
       "      <td>normal</td>\n",
       "      <td>yes</td>\n",
       "      <td>overcast</td>\n",
       "      <td>yes</td>\n",
       "      <td>red</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>hot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>high</td>\n",
       "      <td>no</td>\n",
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       "      <td>yes</td>\n",
       "      <td>blue</td>\n",
       "      <td>yes</td>\n",
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       "      <td>mild</td>\n",
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       "      <th>10</th>\n",
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       "      <th>11</th>\n",
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       "      <th>13</th>\n",
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       "      <td>no</td>\n",
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       "      <th>14</th>\n",
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       "      <th>15</th>\n",
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       "      <th>20</th>\n",
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       "      <td>sunny</td>\n",
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       "      <th>23</th>\n",
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       "      <th>24</th>\n",
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       "      <td>rainy</td>\n",
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       "      <td>yes</td>\n",
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       "      <td>rainy</td>\n",
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       "      <td>no</td>\n",
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       "      <td>no</td>\n",
       "      <td>rainy</td>\n",
       "      <td>no</td>\n",
       "      <td>blue</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
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       "      <td>no</td>\n",
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       "      <th>1703</th>\n",
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       "      <th>1704</th>\n",
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       "      <td>no</td>\n",
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       "      <th>1706</th>\n",
       "      <td>D1706</td>\n",
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       "      <td>blue</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>mild</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1709</th>\n",
       "      <td>D1709</td>\n",
       "      <td>yes</td>\n",
       "      <td>high</td>\n",
       "      <td>no</td>\n",
       "      <td>overcast</td>\n",
       "      <td>yes</td>\n",
       "      <td>yellow</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>mild</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710</th>\n",
       "      <td>D1710</td>\n",
       "      <td>no</td>\n",
       "      <td>normal</td>\n",
       "      <td>no</td>\n",
       "      <td>sunny</td>\n",
       "      <td>no</td>\n",
       "      <td>red</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>hot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1711</th>\n",
       "      <td>D1711</td>\n",
       "      <td>no</td>\n",
       "      <td>high</td>\n",
       "      <td>no</td>\n",
       "      <td>overcast</td>\n",
       "      <td>no</td>\n",
       "      <td>red</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>hot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1712</th>\n",
       "      <td>D1712</td>\n",
       "      <td>no</td>\n",
       "      <td>normal</td>\n",
       "      <td>no</td>\n",
       "      <td>rainy</td>\n",
       "      <td>no</td>\n",
       "      <td>red</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>hot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1713</th>\n",
       "      <td>D1713</td>\n",
       "      <td>yes</td>\n",
       "      <td>normal</td>\n",
       "      <td>yes</td>\n",
       "      <td>rainy</td>\n",
       "      <td>no</td>\n",
       "      <td>blue</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>mild</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1714</th>\n",
       "      <td>D1714</td>\n",
       "      <td>no</td>\n",
       "      <td>normal</td>\n",
       "      <td>no</td>\n",
       "      <td>overcast</td>\n",
       "      <td>no</td>\n",
       "      <td>yellow</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1715</th>\n",
       "      <td>D1715</td>\n",
       "      <td>no</td>\n",
       "      <td>normal</td>\n",
       "      <td>yes</td>\n",
       "      <td>rainy</td>\n",
       "      <td>yes</td>\n",
       "      <td>yellow</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>mild</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1716</th>\n",
       "      <td>D1716</td>\n",
       "      <td>yes</td>\n",
       "      <td>normal</td>\n",
       "      <td>no</td>\n",
       "      <td>sunny</td>\n",
       "      <td>no</td>\n",
       "      <td>yellow</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>hot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1717</th>\n",
       "      <td>D1717</td>\n",
       "      <td>no</td>\n",
       "      <td>normal</td>\n",
       "      <td>no</td>\n",
       "      <td>rainy</td>\n",
       "      <td>yes</td>\n",
       "      <td>yellow</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>hot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1718</th>\n",
       "      <td>D1718</td>\n",
       "      <td>yes</td>\n",
       "      <td>high</td>\n",
       "      <td>no</td>\n",
       "      <td>sunny</td>\n",
       "      <td>yes</td>\n",
       "      <td>blue</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>mild</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1719</th>\n",
       "      <td>D1719</td>\n",
       "      <td>yes</td>\n",
       "      <td>normal</td>\n",
       "      <td>no</td>\n",
       "      <td>overcast</td>\n",
       "      <td>no</td>\n",
       "      <td>blue</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>mild</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1720</th>\n",
       "      <td>D1720</td>\n",
       "      <td>no</td>\n",
       "      <td>high</td>\n",
       "      <td>yes</td>\n",
       "      <td>overcast</td>\n",
       "      <td>no</td>\n",
       "      <td>red</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1721</th>\n",
       "      <td>D1721</td>\n",
       "      <td>no</td>\n",
       "      <td>high</td>\n",
       "      <td>no</td>\n",
       "      <td>sunny</td>\n",
       "      <td>no</td>\n",
       "      <td>blue</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1722</th>\n",
       "      <td>D1722</td>\n",
       "      <td>yes</td>\n",
       "      <td>normal</td>\n",
       "      <td>yes</td>\n",
       "      <td>overcast</td>\n",
       "      <td>no</td>\n",
       "      <td>red</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>mild</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1723</th>\n",
       "      <td>D1723</td>\n",
       "      <td>no</td>\n",
       "      <td>normal</td>\n",
       "      <td>no</td>\n",
       "      <td>sunny</td>\n",
       "      <td>no</td>\n",
       "      <td>red</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>mild</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1724</th>\n",
       "      <td>D1724</td>\n",
       "      <td>yes</td>\n",
       "      <td>high</td>\n",
       "      <td>no</td>\n",
       "      <td>overcast</td>\n",
       "      <td>no</td>\n",
       "      <td>red</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>mild</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1725</th>\n",
       "      <td>D1725</td>\n",
       "      <td>yes</td>\n",
       "      <td>normal</td>\n",
       "      <td>yes</td>\n",
       "      <td>sunny</td>\n",
       "      <td>no</td>\n",
       "      <td>red</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1726</th>\n",
       "      <td>D1726</td>\n",
       "      <td>yes</td>\n",
       "      <td>high</td>\n",
       "      <td>no</td>\n",
       "      <td>sunny</td>\n",
       "      <td>no</td>\n",
       "      <td>red</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>mild</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1727</th>\n",
       "      <td>D1727</td>\n",
       "      <td>no</td>\n",
       "      <td>high</td>\n",
       "      <td>yes</td>\n",
       "      <td>overcast</td>\n",
       "      <td>yes</td>\n",
       "      <td>yellow</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1728 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sample target humidity windy   outlook tired  tshirt friends went_out  \\\n",
       "0        D0    yes   normal   yes  overcast   yes     red     yes       no   \n",
       "1        D1    yes     high    no  overcast   yes    blue     yes       no   \n",
       "2        D2     no     high   yes  overcast   yes  yellow     yes      yes   \n",
       "3        D3     no   normal   yes  overcast    no     red     yes       no   \n",
       "4        D4    yes   normal   yes     rainy    no  yellow      no       no   \n",
       "5        D5     no     high   yes     sunny    no  yellow     yes      yes   \n",
       "6        D6     no   normal   yes     rainy   yes  yellow      no       no   \n",
       "7        D7    yes     high    no     sunny   yes     red     yes      yes   \n",
       "8        D8    yes     high   yes  overcast   yes  yellow     yes      yes   \n",
       "9        D9    yes     high   yes  overcast    no  yellow     yes       no   \n",
       "10      D10     no   normal   yes     sunny    no    blue     yes      yes   \n",
       "11      D11     no   normal    no     sunny    no  yellow      no       no   \n",
       "12      D12    yes   normal   yes  overcast    no  yellow      no      yes   \n",
       "13      D13    yes   normal    no     sunny   yes     red      no      yes   \n",
       "14      D14     no   normal    no     rainy    no     red     yes      yes   \n",
       "15      D15     no     high    no     sunny   yes  yellow     yes       no   \n",
       "16      D16    yes     high    no     sunny    no    blue      no       no   \n",
       "17      D17     no   normal    no  overcast    no     red     yes      yes   \n",
       "18      D18     no     high    no     rainy    no     red      no       no   \n",
       "19      D19    yes   normal    no     rainy    no    blue      no       no   \n",
       "20      D20     no     high    no     rainy   yes     red     yes      yes   \n",
       "21      D21    yes   normal   yes  overcast   yes    blue      no      yes   \n",
       "22      D22     no     high   yes     sunny    no     red      no       no   \n",
       "23      D23     no     high    no     sunny   yes     red      no       no   \n",
       "24      D24     no     high   yes     rainy   yes  yellow      no      yes   \n",
       "25      D25     no   normal   yes     rainy   yes  yellow      no       no   \n",
       "26      D26     no     high    no     rainy    no    blue      no       no   \n",
       "27      D27    yes     high    no     sunny    no    blue     yes      yes   \n",
       "28      D28    yes   normal    no     rainy    no  yellow     yes       no   \n",
       "29      D29     no   normal    no  overcast    no  yellow     yes       no   \n",
       "...     ...    ...      ...   ...       ...   ...     ...     ...      ...   \n",
       "1698  D1698     no   normal   yes     sunny   yes    blue      no      yes   \n",
       "1699  D1699    yes     high   yes  overcast   yes  yellow      no      yes   \n",
       "1700  D1700    yes   normal    no     sunny   yes     red     yes      yes   \n",
       "1701  D1701     no   normal    no     sunny    no     red      no       no   \n",
       "1702  D1702     no   normal    no  overcast   yes     red     yes      yes   \n",
       "1703  D1703    yes     high   yes     sunny   yes    blue     yes       no   \n",
       "1704  D1704    yes     high   yes     rainy   yes  yellow     yes       no   \n",
       "1705  D1705     no   normal   yes     rainy    no  yellow      no       no   \n",
       "1706  D1706     no     high    no     sunny    no     red     yes       no   \n",
       "1707  D1707    yes     high   yes     rainy   yes  yellow      no      yes   \n",
       "1708  D1708     no     high    no     sunny    no    blue     yes      yes   \n",
       "1709  D1709    yes     high    no  overcast   yes  yellow     yes      yes   \n",
       "1710  D1710     no   normal    no     sunny    no     red      no       no   \n",
       "1711  D1711     no     high    no  overcast    no     red     yes      yes   \n",
       "1712  D1712     no   normal    no     rainy    no     red      no      yes   \n",
       "1713  D1713    yes   normal   yes     rainy    no    blue     yes      yes   \n",
       "1714  D1714     no   normal    no  overcast    no  yellow      no      yes   \n",
       "1715  D1715     no   normal   yes     rainy   yes  yellow     yes       no   \n",
       "1716  D1716    yes   normal    no     sunny    no  yellow      no       no   \n",
       "1717  D1717     no   normal    no     rainy   yes  yellow     yes       no   \n",
       "1718  D1718    yes     high    no     sunny   yes    blue     yes      yes   \n",
       "1719  D1719    yes   normal    no  overcast    no    blue      no      yes   \n",
       "1720  D1720     no     high   yes  overcast    no     red     yes       no   \n",
       "1721  D1721     no     high    no     sunny    no    blue      no      yes   \n",
       "1722  D1722    yes   normal   yes  overcast    no     red     yes      yes   \n",
       "1723  D1723     no   normal    no     sunny    no     red      no      yes   \n",
       "1724  D1724    yes     high    no  overcast    no     red      no       no   \n",
       "1725  D1725    yes   normal   yes     sunny    no     red     yes       no   \n",
       "1726  D1726    yes     high    no     sunny    no     red      no       no   \n",
       "1727  D1727     no     high   yes  overcast   yes  yellow      no      yes   \n",
       "\n",
       "      temp  \n",
       "0      hot  \n",
       "1     mild  \n",
       "2     mild  \n",
       "3     cold  \n",
       "4     mild  \n",
       "5     mild  \n",
       "6     mild  \n",
       "7      hot  \n",
       "8     mild  \n",
       "9      hot  \n",
       "10     hot  \n",
       "11    mild  \n",
       "12     hot  \n",
       "13    cold  \n",
       "14    mild  \n",
       "15    mild  \n",
       "16    mild  \n",
       "17    cold  \n",
       "18    cold  \n",
       "19    mild  \n",
       "20     hot  \n",
       "21    mild  \n",
       "22     hot  \n",
       "23    cold  \n",
       "24    cold  \n",
       "25    mild  \n",
       "26    cold  \n",
       "27    mild  \n",
       "28     hot  \n",
       "29    cold  \n",
       "...    ...  \n",
       "1698  mild  \n",
       "1699  mild  \n",
       "1700  cold  \n",
       "1701   hot  \n",
       "1702  cold  \n",
       "1703  mild  \n",
       "1704  cold  \n",
       "1705   hot  \n",
       "1706  mild  \n",
       "1707  cold  \n",
       "1708  mild  \n",
       "1709  mild  \n",
       "1710   hot  \n",
       "1711   hot  \n",
       "1712   hot  \n",
       "1713  mild  \n",
       "1714  cold  \n",
       "1715  mild  \n",
       "1716   hot  \n",
       "1717   hot  \n",
       "1718  mild  \n",
       "1719  mild  \n",
       "1720  cold  \n",
       "1721  cold  \n",
       "1722  mild  \n",
       "1723  mild  \n",
       "1724  mild  \n",
       "1725  cold  \n",
       "1726  mild  \n",
       "1727  cold  \n",
       "\n",
       "[1728 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Transformando atributos categóricos**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample</th>\n",
       "      <th>target</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windy</th>\n",
       "      <th>outlook</th>\n",
       "      <th>tired</th>\n",
       "      <th>tshirt</th>\n",
       "      <th>friends</th>\n",
       "      <th>went_out</th>\n",
       "      <th>temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>D0</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>D1</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D2</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D3</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>D4</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>D5</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>D6</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>D7</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>D8</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>D9</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>D10</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>D11</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>D12</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>D13</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>D14</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>D15</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>D16</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>D17</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>D18</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>D19</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>D20</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>D21</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>D22</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>D23</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>D24</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>D25</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>D26</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>D27</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>D28</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>D29</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1698</th>\n",
       "      <td>D1698</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>D1699</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>D1700</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>D1701</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>D1702</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>D1703</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1704</th>\n",
       "      <td>D1704</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1705</th>\n",
       "      <td>D1705</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1706</th>\n",
       "      <td>D1706</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1707</th>\n",
       "      <td>D1707</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1708</th>\n",
       "      <td>D1708</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1709</th>\n",
       "      <td>D1709</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710</th>\n",
       "      <td>D1710</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1711</th>\n",
       "      <td>D1711</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1712</th>\n",
       "      <td>D1712</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1713</th>\n",
       "      <td>D1713</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1714</th>\n",
       "      <td>D1714</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1715</th>\n",
       "      <td>D1715</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1716</th>\n",
       "      <td>D1716</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1717</th>\n",
       "      <td>D1717</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1718</th>\n",
       "      <td>D1718</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1719</th>\n",
       "      <td>D1719</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1720</th>\n",
       "      <td>D1720</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1721</th>\n",
       "      <td>D1721</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1722</th>\n",
       "      <td>D1722</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1723</th>\n",
       "      <td>D1723</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1724</th>\n",
       "      <td>D1724</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1725</th>\n",
       "      <td>D1725</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1726</th>\n",
       "      <td>D1726</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1727</th>\n",
       "      <td>D1727</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1728 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sample target  humidity  windy  outlook  tired  tshirt  friends  \\\n",
       "0        D0    yes         0      0        0      0       0        0   \n",
       "1        D1    yes         1      1        0      0       1        0   \n",
       "2        D2     no         1      0        0      0       2        0   \n",
       "3        D3     no         0      0        0      1       0        0   \n",
       "4        D4    yes         0      0        1      1       2        1   \n",
       "5        D5     no         1      0        2      1       2        0   \n",
       "6        D6     no         0      0        1      0       2        1   \n",
       "7        D7    yes         1      1        2      0       0        0   \n",
       "8        D8    yes         1      0        0      0       2        0   \n",
       "9        D9    yes         1      0        0      1       2        0   \n",
       "10      D10     no         0      0        2      1       1        0   \n",
       "11      D11     no         0      1        2      1       2        1   \n",
       "12      D12    yes         0      0        0      1       2        1   \n",
       "13      D13    yes         0      1        2      0       0        1   \n",
       "14      D14     no         0      1        1      1       0        0   \n",
       "15      D15     no         1      1        2      0       2        0   \n",
       "16      D16    yes         1      1        2      1       1        1   \n",
       "17      D17     no         0      1        0      1       0        0   \n",
       "18      D18     no         1      1        1      1       0        1   \n",
       "19      D19    yes         0      1        1      1       1        1   \n",
       "20      D20     no         1      1        1      0       0        0   \n",
       "21      D21    yes         0      0        0      0       1        1   \n",
       "22      D22     no         1      0        2      1       0        1   \n",
       "23      D23     no         1      1        2      0       0        1   \n",
       "24      D24     no         1      0        1      0       2        1   \n",
       "25      D25     no         0      0        1      0       2        1   \n",
       "26      D26     no         1      1        1      1       1        1   \n",
       "27      D27    yes         1      1        2      1       1        0   \n",
       "28      D28    yes         0      1        1      1       2        0   \n",
       "29      D29     no         0      1        0      1       2        0   \n",
       "...     ...    ...       ...    ...      ...    ...     ...      ...   \n",
       "1698  D1698     no         0      0        2      0       1        1   \n",
       "1699  D1699    yes         1      0        0      0       2        1   \n",
       "1700  D1700    yes         0      1        2      0       0        0   \n",
       "1701  D1701     no         0      1        2      1       0        1   \n",
       "1702  D1702     no         0      1        0      0       0        0   \n",
       "1703  D1703    yes         1      0        2      0       1        0   \n",
       "1704  D1704    yes         1      0        1      0       2        0   \n",
       "1705  D1705     no         0      0        1      1       2        1   \n",
       "1706  D1706     no         1      1        2      1       0        0   \n",
       "1707  D1707    yes         1      0        1      0       2        1   \n",
       "1708  D1708     no         1      1        2      1       1        0   \n",
       "1709  D1709    yes         1      1        0      0       2        0   \n",
       "1710  D1710     no         0      1        2      1       0        1   \n",
       "1711  D1711     no         1      1        0      1       0        0   \n",
       "1712  D1712     no         0      1        1      1       0        1   \n",
       "1713  D1713    yes         0      0        1      1       1        0   \n",
       "1714  D1714     no         0      1        0      1       2        1   \n",
       "1715  D1715     no         0      0        1      0       2        0   \n",
       "1716  D1716    yes         0      1        2      1       2        1   \n",
       "1717  D1717     no         0      1        1      0       2        0   \n",
       "1718  D1718    yes         1      1        2      0       1        0   \n",
       "1719  D1719    yes         0      1        0      1       1        1   \n",
       "1720  D1720     no         1      0        0      1       0        0   \n",
       "1721  D1721     no         1      1        2      1       1        1   \n",
       "1722  D1722    yes         0      0        0      1       0        0   \n",
       "1723  D1723     no         0      1        2      1       0        1   \n",
       "1724  D1724    yes         1      1        0      1       0        1   \n",
       "1725  D1725    yes         0      0        2      1       0        0   \n",
       "1726  D1726    yes         1      1        2      1       0        1   \n",
       "1727  D1727     no         1      0        0      0       2        1   \n",
       "\n",
       "      went_out  temp  \n",
       "0            0     0  \n",
       "1            0     1  \n",
       "2            1     1  \n",
       "3            0     2  \n",
       "4            0     1  \n",
       "5            1     1  \n",
       "6            0     1  \n",
       "7            1     0  \n",
       "8            1     1  \n",
       "9            0     0  \n",
       "10           1     0  \n",
       "11           0     1  \n",
       "12           1     0  \n",
       "13           1     2  \n",
       "14           1     1  \n",
       "15           0     1  \n",
       "16           0     1  \n",
       "17           1     2  \n",
       "18           0     2  \n",
       "19           0     1  \n",
       "20           1     0  \n",
       "21           1     1  \n",
       "22           0     0  \n",
       "23           0     2  \n",
       "24           1     2  \n",
       "25           0     1  \n",
       "26           0     2  \n",
       "27           1     1  \n",
       "28           0     0  \n",
       "29           0     2  \n",
       "...        ...   ...  \n",
       "1698         1     1  \n",
       "1699         1     1  \n",
       "1700         1     2  \n",
       "1701         0     0  \n",
       "1702         1     2  \n",
       "1703         0     1  \n",
       "1704         0     2  \n",
       "1705         0     0  \n",
       "1706         0     1  \n",
       "1707         1     2  \n",
       "1708         1     1  \n",
       "1709         1     1  \n",
       "1710         0     0  \n",
       "1711         1     0  \n",
       "1712         1     0  \n",
       "1713         1     1  \n",
       "1714         1     2  \n",
       "1715         0     1  \n",
       "1716         0     0  \n",
       "1717         0     0  \n",
       "1718         1     1  \n",
       "1719         1     1  \n",
       "1720         0     2  \n",
       "1721         1     2  \n",
       "1722         1     1  \n",
       "1723         1     1  \n",
       "1724         0     1  \n",
       "1725         0     2  \n",
       "1726         0     1  \n",
       "1727         1     2  \n",
       "\n",
       "[1728 rows x 10 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_cols = ['tshirt','temp','windy','tired','went_out','outlook','friends','humidity']\n",
    "for column in categorical_cols:\n",
    "    dataset[column] = pandas.factorize(dataset[column])[0]\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Transformando alvo binário: **\n",
    "- Se target é igual a **'yes'**, então será transformado para **+1**\n",
    "- Se target é igual a **'no'**, então será transformado para **-1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample</th>\n",
       "      <th>target</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windy</th>\n",
       "      <th>outlook</th>\n",
       "      <th>tired</th>\n",
       "      <th>tshirt</th>\n",
       "      <th>friends</th>\n",
       "      <th>went_out</th>\n",
       "      <th>temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>D0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>D1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D2</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>D4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>D5</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>D6</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>D7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>D8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>D9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>D10</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>D11</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>D12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>D13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>D14</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>D15</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>D16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>D17</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>D18</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>D19</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>D20</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>D21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>D22</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>D23</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>D24</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>D25</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>D26</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>D27</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>D28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>D29</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1698</th>\n",
       "      <td>D1698</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>D1699</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>D1700</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>D1701</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>D1702</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>D1703</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1704</th>\n",
       "      <td>D1704</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1705</th>\n",
       "      <td>D1705</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1706</th>\n",
       "      <td>D1706</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1707</th>\n",
       "      <td>D1707</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1708</th>\n",
       "      <td>D1708</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1709</th>\n",
       "      <td>D1709</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710</th>\n",
       "      <td>D1710</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1711</th>\n",
       "      <td>D1711</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1712</th>\n",
       "      <td>D1712</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1713</th>\n",
       "      <td>D1713</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1714</th>\n",
       "      <td>D1714</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1715</th>\n",
       "      <td>D1715</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1716</th>\n",
       "      <td>D1716</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1717</th>\n",
       "      <td>D1717</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1718</th>\n",
       "      <td>D1718</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1719</th>\n",
       "      <td>D1719</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1720</th>\n",
       "      <td>D1720</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1721</th>\n",
       "      <td>D1721</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1722</th>\n",
       "      <td>D1722</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1723</th>\n",
       "      <td>D1723</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1724</th>\n",
       "      <td>D1724</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1725</th>\n",
       "      <td>D1725</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1726</th>\n",
       "      <td>D1726</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1727</th>\n",
       "      <td>D1727</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1728 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sample  target  humidity  windy  outlook  tired  tshirt  friends  \\\n",
       "0        D0       1         0      0        0      0       0        0   \n",
       "1        D1       1         1      1        0      0       1        0   \n",
       "2        D2      -1         1      0        0      0       2        0   \n",
       "3        D3      -1         0      0        0      1       0        0   \n",
       "4        D4       1         0      0        1      1       2        1   \n",
       "5        D5      -1         1      0        2      1       2        0   \n",
       "6        D6      -1         0      0        1      0       2        1   \n",
       "7        D7       1         1      1        2      0       0        0   \n",
       "8        D8       1         1      0        0      0       2        0   \n",
       "9        D9       1         1      0        0      1       2        0   \n",
       "10      D10      -1         0      0        2      1       1        0   \n",
       "11      D11      -1         0      1        2      1       2        1   \n",
       "12      D12       1         0      0        0      1       2        1   \n",
       "13      D13       1         0      1        2      0       0        1   \n",
       "14      D14      -1         0      1        1      1       0        0   \n",
       "15      D15      -1         1      1        2      0       2        0   \n",
       "16      D16       1         1      1        2      1       1        1   \n",
       "17      D17      -1         0      1        0      1       0        0   \n",
       "18      D18      -1         1      1        1      1       0        1   \n",
       "19      D19       1         0      1        1      1       1        1   \n",
       "20      D20      -1         1      1        1      0       0        0   \n",
       "21      D21       1         0      0        0      0       1        1   \n",
       "22      D22      -1         1      0        2      1       0        1   \n",
       "23      D23      -1         1      1        2      0       0        1   \n",
       "24      D24      -1         1      0        1      0       2        1   \n",
       "25      D25      -1         0      0        1      0       2        1   \n",
       "26      D26      -1         1      1        1      1       1        1   \n",
       "27      D27       1         1      1        2      1       1        0   \n",
       "28      D28       1         0      1        1      1       2        0   \n",
       "29      D29      -1         0      1        0      1       2        0   \n",
       "...     ...     ...       ...    ...      ...    ...     ...      ...   \n",
       "1698  D1698      -1         0      0        2      0       1        1   \n",
       "1699  D1699       1         1      0        0      0       2        1   \n",
       "1700  D1700       1         0      1        2      0       0        0   \n",
       "1701  D1701      -1         0      1        2      1       0        1   \n",
       "1702  D1702      -1         0      1        0      0       0        0   \n",
       "1703  D1703       1         1      0        2      0       1        0   \n",
       "1704  D1704       1         1      0        1      0       2        0   \n",
       "1705  D1705      -1         0      0        1      1       2        1   \n",
       "1706  D1706      -1         1      1        2      1       0        0   \n",
       "1707  D1707       1         1      0        1      0       2        1   \n",
       "1708  D1708      -1         1      1        2      1       1        0   \n",
       "1709  D1709       1         1      1        0      0       2        0   \n",
       "1710  D1710      -1         0      1        2      1       0        1   \n",
       "1711  D1711      -1         1      1        0      1       0        0   \n",
       "1712  D1712      -1         0      1        1      1       0        1   \n",
       "1713  D1713       1         0      0        1      1       1        0   \n",
       "1714  D1714      -1         0      1        0      1       2        1   \n",
       "1715  D1715      -1         0      0        1      0       2        0   \n",
       "1716  D1716       1         0      1        2      1       2        1   \n",
       "1717  D1717      -1         0      1        1      0       2        0   \n",
       "1718  D1718       1         1      1        2      0       1        0   \n",
       "1719  D1719       1         0      1        0      1       1        1   \n",
       "1720  D1720      -1         1      0        0      1       0        0   \n",
       "1721  D1721      -1         1      1        2      1       1        1   \n",
       "1722  D1722       1         0      0        0      1       0        0   \n",
       "1723  D1723      -1         0      1        2      1       0        1   \n",
       "1724  D1724       1         1      1        0      1       0        1   \n",
       "1725  D1725       1         0      0        2      1       0        0   \n",
       "1726  D1726       1         1      1        2      1       0        1   \n",
       "1727  D1727      -1         1      0        0      0       2        1   \n",
       "\n",
       "      went_out  temp  \n",
       "0            0     0  \n",
       "1            0     1  \n",
       "2            1     1  \n",
       "3            0     2  \n",
       "4            0     1  \n",
       "5            1     1  \n",
       "6            0     1  \n",
       "7            1     0  \n",
       "8            1     1  \n",
       "9            0     0  \n",
       "10           1     0  \n",
       "11           0     1  \n",
       "12           1     0  \n",
       "13           1     2  \n",
       "14           1     1  \n",
       "15           0     1  \n",
       "16           0     1  \n",
       "17           1     2  \n",
       "18           0     2  \n",
       "19           0     1  \n",
       "20           1     0  \n",
       "21           1     1  \n",
       "22           0     0  \n",
       "23           0     2  \n",
       "24           1     2  \n",
       "25           0     1  \n",
       "26           0     2  \n",
       "27           1     1  \n",
       "28           0     0  \n",
       "29           0     2  \n",
       "...        ...   ...  \n",
       "1698         1     1  \n",
       "1699         1     1  \n",
       "1700         1     2  \n",
       "1701         0     0  \n",
       "1702         1     2  \n",
       "1703         0     1  \n",
       "1704         0     2  \n",
       "1705         0     0  \n",
       "1706         0     1  \n",
       "1707         1     2  \n",
       "1708         1     1  \n",
       "1709         1     1  \n",
       "1710         0     0  \n",
       "1711         1     0  \n",
       "1712         1     0  \n",
       "1713         1     1  \n",
       "1714         1     2  \n",
       "1715         0     1  \n",
       "1716         0     0  \n",
       "1717         0     0  \n",
       "1718         1     1  \n",
       "1719         1     1  \n",
       "1720         0     2  \n",
       "1721         1     2  \n",
       "1722         1     1  \n",
       "1723         1     1  \n",
       "1724         0     1  \n",
       "1725         0     2  \n",
       "1726         0     1  \n",
       "1727         1     2  \n",
       "\n",
       "[1728 rows x 10 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['target'] = dataset['target'].replace('yes', 1)\n",
    "dataset['target'] = dataset['target'].replace('no', -1)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Dividindo sub-conjunto de teste e treino **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "train, test = train_test_split(dataset, train_size = 0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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37558B0QO7969Y1988YVcx0wgELD27dvX2rZFyh9PacdcltOnT7OmTZvKtQ9q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\nERERERGZsP8Hr3yOtkVlH5UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1146d5e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 8))\n",
    "ax = fig.gca()\n",
    "labels = [\"Conjunto de Treino (%d amostras)\" % len(train), \"Conjunto de Teste (%d amostras)\" % len(test)]\n",
    "sizes = [len(train), len(test)]\n",
    "plt.rcParams.update({'font.size': 22})\n",
    "ax.pie(sizes, labels=labels, colors=colors, autopct='%1.2f%%')\n",
    "plt.title(\"Conjuntos de Treino e Teste\")\n",
    "ax.set_aspect('equal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9epEoirRw4UJSq9XS81FRUdS2bVsSBMFgwnHq1CmDt8kvXLhAjo6OJJfLKSUl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\n2e5yqAdg4KCEU3/xt11Q+16DWcbrsBC1V8TVB+GCBbz4GrULAwclrKC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      "text/plain": [
       "<matplotlib.figure.Figure at 0x116ab1438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 8))\n",
    "ax = fig.gca()\n",
    "labels = [\"Não joga (%d amostras)\" % len(train[train.target < 0]), \"Joga (%d amostras)\" % len(train[train.target > 0])]\n",
    "sizes = [len(train[train.target < 0]), len(train[train.target > 0])]\n",
    "ax.pie(sizes, labels=labels, colors=colors, autopct='%1.2f%%')\n",
    "plt.title(\"Conjunto de Treino\")\n",
    "plt.rcParams.update({'font.size': 22})\n",
    "ax.set_aspect('equal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Treinando árvore de decisão #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
    "dt = DecisionTreeClassifier(max_depth=3)\n",
    "#dt = DecisionTreeClassifier()\n",
    "decision_tree_binary_classifier = dt.fit(train[categorical_cols], train.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Graficando a árvore gerada **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg height=\"467pt\" viewBox=\"0.00 0.00 1059.99 467.20\" width=\"1060pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 463.2)\">\n",
       "<title>Tree</title>\n",
       "<polygon fill=\"white\" points=\"-4,4 -4,-463.2 1055.99,-463.2 1055.99,4 -4,4\" stroke=\"none\"/>\n",
       "<!-- 0 -->\n",
       "<g class=\"node\" id=\"node1\"><title>0</title>\n",
       "<path d=\"M585.611,-459.2C585.611,-459.2 482.274,-459.2 482.274,-459.2 476.274,-459.2 470.274,-453.2 470.274,-447.2 470.274,-447.2 470.274,-379.2 470.274,-379.2 470.274,-373.2 476.274,-367.2 482.274,-367.2 482.274,-367.2 585.611,-367.2 585.611,-367.2 591.611,-367.2 597.611,-373.2 597.611,-379.2 597.611,-379.2 597.611,-447.2 597.611,-447.2 597.611,-453.2 591.611,-459.2 585.611,-459.2\" fill=\"#e58139\" fill-opacity=\"0.090196\" stroke=\"black\"/>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"500.1\" y=\"-442.6\">tshirt ≤ 1.5</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"493.674\" y=\"-425.8\">gini = 0.4989</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"484.342\" y=\"-409\">samples = 1209</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"478.109\" y=\"-392.2\">value = [633, 576]</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"496.793\" y=\"-375.4\">class = joga</text>\n",
       "</g>\n",
       "<!-- 1 -->\n",
       "<g class=\"node\" id=\"node2\"><title>1</title>\n",
       "<path d=\"M485.611,-331.2C485.611,-331.2 382.274,-331.2 382.274,-331.2 376.274,-331.2 370.274,-325.2 370.274,-319.2 370.274,-319.2 370.274,-251.2 370.274,-251.2 370.274,-245.2 376.274,-239.2 382.274,-239.2 382.274,-239.2 485.611,-239.2 485.611,-239.2 491.611,-239.2 497.611,-245.2 497.611,-251.2 497.611,-251.2 497.611,-319.2 497.611,-319.2 497.611,-325.2 491.611,-331.2 485.611,-331.2\" fill=\"#e58139\" fill-opacity=\"0.129412\" stroke=\"black\"/>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"401.654\" y=\"-314.6\">tired ≤ 0.5</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"393.674\" y=\"-297.8\">gini = 0.4975</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"388.234\" y=\"-281\">samples = 798</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"378.109\" y=\"-264.2\">value = [427, 371]</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"396.793\" y=\"-247.4\">class = joga</text>\n",
       "</g>\n",
       "<!-- 0&#45;&gt;1 -->\n",
       "<g class=\"edge\" id=\"edge1\"><title>0-&gt;1</title>\n",
       "<path d=\"M498.123,-367.067C491.014,-358.11 483.489,-348.628 476.189,-339.43\" fill=\"none\" stroke=\"black\"/>\n",
       "<polygon fill=\"black\" points=\"478.807,-337.098 469.849,-331.441 473.324,-341.45 478.807,-337.098\" stroke=\"black\"/>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"466.991\" y=\"-352.078\">True</text>\n",
       "</g>\n",
       "<!-- 8 -->\n",
       "<g class=\"node\" id=\"node9\"><title>8</title>\n",
       "<path d=\"M686.611,-331.2C686.611,-331.2 583.274,-331.2 583.274,-331.2 577.274,-331.2 571.274,-325.2 571.274,-319.2 571.274,-319.2 571.274,-251.2 571.274,-251.2 571.274,-245.2 577.274,-239.2 583.274,-239.2 583.274,-239.2 686.611,-239.2 686.611,-239.2 692.611,-239.2 698.611,-245.2 698.611,-251.2 698.611,-251.2 698.611,-319.2 698.611,-319.2 698.611,-325.2 692.611,-331.2 686.611,-331.2\" fill=\"#e58139\" fill-opacity=\"0.003922\" stroke=\"black\"/>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"595.263\" y=\"-314.6\">friends ≤ 0.5</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"606.348\" y=\"-297.8\">gini = 0.5</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"589.234\" y=\"-281\">samples = 411</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"579.109\" y=\"-264.2\">value = [206, 205]</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"597.793\" y=\"-247.4\">class = joga</text>\n",
       "</g>\n",
       "<!-- 0&#45;&gt;8 -->\n",
       "<g class=\"edge\" id=\"edge8\"><title>0-&gt;8</title>\n",
       "<path d=\"M570.121,-367.067C577.301,-358.11 584.901,-348.628 592.274,-339.43\" fill=\"none\" stroke=\"black\"/>\n",
       "<polygon fill=\"black\" points=\"595.154,-341.433 598.678,-331.441 589.692,-337.055 595.154,-341.433\" stroke=\"black\"/>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"601.415\" y=\"-352.091\">False</text>\n",
       "</g>\n",
       "<!-- 2 -->\n",
       "<g class=\"node\" id=\"node3\"><title>2</title>\n",
       "<path d=\"M249.611,-203.2C249.611,-203.2 146.274,-203.2 146.274,-203.2 140.274,-203.2 134.274,-197.2 134.274,-191.2 134.274,-191.2 134.274,-123.2 134.274,-123.2 134.274,-117.2 140.274,-111.2 146.274,-111.2 146.274,-111.2 249.611,-111.2 249.611,-111.2 255.611,-111.2 261.611,-117.2 261.611,-123.2 261.611,-123.2 261.611,-191.2 261.611,-191.2 261.611,-197.2 255.611,-203.2 249.611,-203.2\" fill=\"#e58139\" fill-opacity=\"0.223529\" stroke=\"black\"/>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"164.1\" y=\"-186.6\">tshirt ≤ 0.5</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"157.674\" y=\"-169.8\">gini = 0.4921</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"152.234\" y=\"-153\">samples = 407</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"142.109\" y=\"-136.2\">value = [229, 178]</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"160.793\" y=\"-119.4\">class = joga</text>\n",
       "</g>\n",
       "<!-- 1&#45;&gt;2 -->\n",
       "<g class=\"edge\" id=\"edge2\"><title>1-&gt;2</title>\n",
       "<path d=\"M370.078,-250.103C339.514,-233.784 302.763,-214.163 271.082,-197.249\" fill=\"none\" stroke=\"black\"/>\n",
       "<polygon fill=\"black\" points=\"272.329,-193.947 261.859,-192.325 269.032,-200.122 272.329,-193.947\" stroke=\"black\"/>\n",
       "</g>\n",
       "<!-- 5 -->\n",
       "<g class=\"node\" id=\"node6\"><title>5</title>\n",
       "<path d=\"M485.611,-203.2C485.611,-203.2 382.274,-203.2 382.274,-203.2 376.274,-203.2 370.274,-197.2 370.274,-191.2 370.274,-191.2 370.274,-123.2 370.274,-123.2 370.274,-117.2 376.274,-111.2 382.274,-111.2 382.274,-111.2 485.611,-111.2 485.611,-111.2 491.611,-111.2 497.611,-117.2 497.611,-123.2 497.611,-123.2 497.611,-191.2 497.611,-191.2 497.611,-197.2 491.611,-203.2 485.611,-203.2\" fill=\"#e58139\" fill-opacity=\"0.023529\" stroke=\"black\"/>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"400.1\" y=\"-186.6\">tshirt ≤ 0.5</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"393.674\" y=\"-169.8\">gini = 0.4999</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"388.234\" y=\"-153\">samples = 391</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"378.109\" y=\"-136.2\">value = [198, 193]</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"396.793\" y=\"-119.4\">class = joga</text>\n",
       "</g>\n",
       "<!-- 1&#45;&gt;5 -->\n",
       "<g class=\"edge\" id=\"edge5\"><title>1-&gt;5</title>\n",
       "<path d=\"M433.943,-239.067C433.943,-230.834 433.943,-222.157 433.943,-213.665\" fill=\"none\" stroke=\"black\"/>\n",
       "<polygon fill=\"black\" points=\"437.443,-213.441 433.943,-203.441 430.443,-213.441 437.443,-213.441\" stroke=\"black\"/>\n",
       "</g>\n",
       "<!-- 3 -->\n",
       "<g class=\"node\" id=\"node4\"><title>3</title>\n",
       "<path d=\"M107.828,-75.3003C107.828,-75.3003 12.0571,-75.3003 12.0571,-75.3003 6.05715,-75.3003 0.0571455,-69.3003 0.0571455,-63.3003 0.0571455,-63.3003 0.0571455,-11.8997 0.0571455,-11.8997 0.0571455,-5.89973 6.05715,0.100267 12.0571,0.100267 12.0571,0.100267 107.828,0.100267 107.828,0.100267 113.828,0.100267 119.828,-5.89973 119.828,-11.8997 119.828,-11.8997 119.828,-63.3003 119.828,-63.3003 119.828,-69.3003 113.828,-75.3003 107.828,-75.3003\" fill=\"#e58139\" fill-opacity=\"0.372549\" stroke=\"black\"/>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"19.6739\" y=\"-58.6\">gini = 0.4737</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"14.2335\" y=\"-41.8\">samples = 205</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"8\" y=\"-25\">value = [126, 79]</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"22.7931\" y=\"-8.2\">class = joga</text>\n",
       "</g>\n",
       "<!-- 2&#45;&gt;3 -->\n",
       "<g class=\"edge\" id=\"edge3\"><title>2-&gt;3</title>\n",
       "<path d=\"M144.983,-111.069C133.851,-101.583 122.13,-91.5942 111.067,-82.1666\" fill=\"none\" stroke=\"black\"/>\n",
       "<polygon fill=\"black\" points=\"113.027,-79.2384 103.146,-75.4162 108.487,-84.5663 113.027,-79.2384\" stroke=\"black\"/>\n",
       "</g>\n",
       "<!-- 4 -->\n",
       "<g class=\"node\" id=\"node5\"><title>4</title>\n",
       "<path d=\"M245.828,-75.3003C245.828,-75.3003 150.057,-75.3003 150.057,-75.3003 144.057,-75.3003 138.057,-69.3003 138.057,-63.3003 138.057,-63.3003 138.057,-11.8997 138.057,-11.8997 138.057,-5.89973 144.057,0.100267 150.057,0.100267 150.057,0.100267 245.828,0.100267 245.828,0.100267 251.828,0.100267 257.828,-5.89973 257.828,-11.8997 257.828,-11.8997 257.828,-63.3003 257.828,-63.3003 257.828,-69.3003 251.828,-75.3003 245.828,-75.3003\" fill=\"#e58139\" fill-opacity=\"0.039216\" stroke=\"black\"/>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"157.674\" y=\"-58.6\">gini = 0.4998</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"152.234\" y=\"-41.8\">samples = 202</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"146\" y=\"-25\">value = [103, 99]</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"160.793\" y=\"-8.2\">class = joga</text>\n",
       "</g>\n",
       "<!-- 2&#45;&gt;4 -->\n",
       "<g class=\"edge\" id=\"edge4\"><title>2-&gt;4</title>\n",
       "<path d=\"M197.943,-111.069C197.943,-102.688 197.943,-93.9148 197.943,-85.4828\" fill=\"none\" stroke=\"black\"/>\n",
       "<polygon fill=\"black\" points=\"201.443,-85.4162 197.943,-75.4162 194.443,-85.4163 201.443,-85.4162\" stroke=\"black\"/>\n",
       "</g>\n",
       "<!-- 6 -->\n",
       "<g class=\"node\" id=\"node7\"><title>6</title>\n",
       "<path d=\"M383.828,-75.3003C383.828,-75.3003 288.057,-75.3003 288.057,-75.3003 282.057,-75.3003 276.057,-69.3003 276.057,-63.3003 276.057,-63.3003 276.057,-11.8997 276.057,-11.8997 276.057,-5.89973 282.057,0.100267 288.057,0.100267 288.057,0.100267 383.828,0.100267 383.828,0.100267 389.828,0.100267 395.828,-5.89973 395.828,-11.8997 395.828,-11.8997 395.828,-63.3003 395.828,-63.3003 395.828,-69.3003 389.828,-75.3003 383.828,-75.3003\" fill=\"#399de5\" fill-opacity=\"0.109804\" stroke=\"black\"/>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"295.674\" y=\"-58.6\">gini = 0.4984</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"290.234\" y=\"-41.8\">samples = 210</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"284\" y=\"-25\">value = [99, 111]</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"285.174\" y=\"-8.2\">class = nao joga</text>\n",
       "</g>\n",
       "<!-- 5&#45;&gt;6 -->\n",
       "<g class=\"edge\" id=\"edge6\"><title>5-&gt;6</title>\n",
       "<path d=\"M396.334,-111.069C388.736,-101.951 380.75,-92.3692 373.166,-83.2674\" fill=\"none\" stroke=\"black\"/>\n",
       "<polygon fill=\"black\" points=\"375.714,-80.8578 366.623,-75.4162 370.336,-85.3391 375.714,-80.8578\" stroke=\"black\"/>\n",
       "</g>\n",
       "<!-- 7 -->\n",
       "<g class=\"node\" id=\"node8\"><title>7</title>\n",
       "<path d=\"M514.046,-75.3003C514.046,-75.3003 425.84,-75.3003 425.84,-75.3003 419.84,-75.3003 413.84,-69.3003 413.84,-63.3003 413.84,-63.3003 413.84,-11.8997 413.84,-11.8997 413.84,-5.89973 419.84,0.100267 425.84,0.100267 425.84,0.100267 514.046,0.100267 514.046,0.100267 520.046,0.100267 526.046,-5.89973 526.046,-11.8997 526.046,-11.8997 526.046,-63.3003 526.046,-63.3003 526.046,-69.3003 520.046,-75.3003 514.046,-75.3003\" fill=\"#e58139\" fill-opacity=\"0.172549\" stroke=\"black\"/>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"429.674\" y=\"-58.6\">gini = 0.4956</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"424.234\" y=\"-41.8\">samples = 181</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"421.891\" y=\"-25\">value = [99, 82]</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"432.793\" y=\"-8.2\">class = joga</text>\n",
       "</g>\n",
       "<!-- 5&#45;&gt;7 -->\n",
       "<g class=\"edge\" id=\"edge7\"><title>5-&gt;7</title>\n",
       "<path d=\"M447.758,-111.069C450.352,-102.596 453.069,-93.7219 455.676,-85.205\" fill=\"none\" stroke=\"black\"/>\n",
       "<polygon fill=\"black\" points=\"459.092,-86.0028 458.673,-75.4162 452.399,-83.9537 459.092,-86.0028\" stroke=\"black\"/>\n",
       "</g>\n",
       "<!-- 9 -->\n",
       "<g class=\"node\" id=\"node10\"><title>9</title>\n",
       "<path d=\"M686.611,-203.2C686.611,-203.2 583.274,-203.2 583.274,-203.2 577.274,-203.2 571.274,-197.2 571.274,-191.2 571.274,-191.2 571.274,-123.2 571.274,-123.2 571.274,-117.2 577.274,-111.2 583.274,-111.2 583.274,-111.2 686.611,-111.2 686.611,-111.2 692.611,-111.2 698.611,-117.2 698.611,-123.2 698.611,-123.2 698.611,-191.2 698.611,-191.2 698.611,-197.2 692.611,-203.2 686.611,-203.2\" fill=\"#399de5\" fill-opacity=\"0.098039\" stroke=\"black\"/>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"600.707\" y=\"-186.6\">temp ≤ 0.5</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"594.674\" y=\"-169.8\">gini = 0.4987</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"589.234\" y=\"-153\">samples = 213</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"579.109\" y=\"-136.2\">value = [101, 112]</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"584.174\" y=\"-119.4\">class = nao joga</text>\n",
       "</g>\n",
       "<!-- 8&#45;&gt;9 -->\n",
       "<g class=\"edge\" id=\"edge9\"><title>8-&gt;9</title>\n",
       "<path d=\"M634.943,-239.067C634.943,-230.834 634.943,-222.157 634.943,-213.665\" fill=\"none\" stroke=\"black\"/>\n",
       "<polygon fill=\"black\" points=\"638.443,-213.441 634.943,-203.441 631.443,-213.441 638.443,-213.441\" stroke=\"black\"/>\n",
       "</g>\n",
       "<!-- 12 -->\n",
       "<g class=\"node\" id=\"node13\"><title>12</title>\n",
       "<path d=\"M913.828,-203.2C913.828,-203.2 818.057,-203.2 818.057,-203.2 812.057,-203.2 806.057,-197.2 806.057,-191.2 806.057,-191.2 806.057,-123.2 806.057,-123.2 806.057,-117.2 812.057,-111.2 818.057,-111.2 818.057,-111.2 913.828,-111.2 913.828,-111.2 919.828,-111.2 925.828,-117.2 925.828,-123.2 925.828,-123.2 925.828,-191.2 925.828,-191.2 925.828,-197.2 919.828,-203.2 913.828,-203.2\" fill=\"#e58139\" fill-opacity=\"0.113725\" stroke=\"black\"/>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"833.654\" y=\"-186.6\">tired ≤ 0.5</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"825.674\" y=\"-169.8\">gini = 0.4982</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"820.234\" y=\"-153\">samples = 198</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"814\" y=\"-136.2\">value = [105, 93]</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"828.793\" y=\"-119.4\">class = joga</text>\n",
       "</g>\n",
       "<!-- 8&#45;&gt;12 -->\n",
       "<g class=\"edge\" id=\"edge12\"><title>8-&gt;12</title>\n",
       "<path d=\"M698.676,-249.436C729.184,-232.796 765.768,-212.841 796.932,-195.842\" fill=\"none\" stroke=\"black\"/>\n",
       "<polygon fill=\"black\" points=\"798.888,-198.762 805.991,-190.901 795.536,-192.617 798.888,-198.762\" stroke=\"black\"/>\n",
       "</g>\n",
       "<!-- 10 -->\n",
       "<g class=\"node\" id=\"node11\"><title>10</title>\n",
       "<path d=\"M644.046,-75.3003C644.046,-75.3003 555.84,-75.3003 555.84,-75.3003 549.84,-75.3003 543.84,-69.3003 543.84,-63.3003 543.84,-63.3003 543.84,-11.8997 543.84,-11.8997 543.84,-5.89973 549.84,0.100267 555.84,0.100267 555.84,0.100267 644.046,0.100267 644.046,0.100267 650.046,0.100267 656.046,-5.89973 656.046,-11.8997 656.046,-11.8997 656.046,-63.3003 656.046,-63.3003 656.046,-69.3003 650.046,-75.3003 644.046,-75.3003\" fill=\"#e58139\" fill-opacity=\"0.203922\" stroke=\"black\"/>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"559.674\" y=\"-58.6\">gini = 0.4934</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"558.125\" y=\"-41.8\">samples = 61</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"551.891\" y=\"-25\">value = [34, 27]</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"562.793\" y=\"-8.2\">class = joga</text>\n",
       "</g>\n",
       "<!-- 9&#45;&gt;10 -->\n",
       "<g class=\"edge\" id=\"edge10\"><title>9-&gt;10</title>\n",
       "<path d=\"M621.511,-111.069C618.989,-102.596 616.348,-93.7219 613.813,-85.205\" fill=\"none\" stroke=\"black\"/>\n",
       "<polygon fill=\"black\" points=\"617.107,-84.0023 610.9,-75.4162 610.398,-85.9992 617.107,-84.0023\" stroke=\"black\"/>\n",
       "</g>\n",
       "<!-- 11 -->\n",
       "<g class=\"node\" id=\"node12\"><title>11</title>\n",
       "<path d=\"M779.482,-75.3003C779.482,-75.3003 686.404,-75.3003 686.404,-75.3003 680.404,-75.3003 674.404,-69.3003 674.404,-63.3003 674.404,-63.3003 674.404,-11.8997 674.404,-11.8997 674.404,-5.89973 680.404,0.100267 686.404,0.100267 686.404,0.100267 779.482,0.100267 779.482,0.100267 785.482,0.100267 791.482,-5.89973 791.482,-11.8997 791.482,-11.8997 791.482,-63.3003 791.482,-63.3003 791.482,-69.3003 785.482,-75.3003 779.482,-75.3003\" fill=\"#399de5\" fill-opacity=\"0.211765\" stroke=\"black\"/>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"696.565\" y=\"-58.6\">gini = 0.493</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"687.234\" y=\"-41.8\">samples = 152</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"684.891\" y=\"-25\">value = [67, 85]</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"682.174\" y=\"-8.2\">class = nao joga</text>\n",
       "</g>\n",
       "<!-- 9&#45;&gt;11 -->\n",
       "<g class=\"edge\" id=\"edge11\"><title>9-&gt;11</title>\n",
       "<path d=\"M672.552,-111.069C680.15,-101.951 688.135,-92.3692 695.72,-83.2674\" fill=\"none\" stroke=\"black\"/>\n",
       "<polygon fill=\"black\" points=\"698.55,-85.3391 702.263,-75.4162 693.172,-80.8578 698.55,-85.3391\" stroke=\"black\"/>\n",
       "</g>\n",
       "<!-- 13 -->\n",
       "<g class=\"node\" id=\"node14\"><title>13</title>\n",
       "<path d=\"M910.046,-75.3003C910.046,-75.3003 821.84,-75.3003 821.84,-75.3003 815.84,-75.3003 809.84,-69.3003 809.84,-63.3003 809.84,-63.3003 809.84,-11.8997 809.84,-11.8997 809.84,-5.89973 815.84,0.100267 821.84,0.100267 821.84,0.100267 910.046,0.100267 910.046,0.100267 916.046,0.100267 922.046,-5.89973 922.046,-11.8997 922.046,-11.8997 922.046,-63.3003 922.046,-63.3003 922.046,-69.3003 916.046,-75.3003 910.046,-75.3003\" fill=\"none\" stroke=\"black\"/>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"837.348\" y=\"-58.6\">gini = 0.5</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"820.234\" y=\"-41.8\">samples = 108</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"817.891\" y=\"-25\">value = [54, 54]</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"828.793\" y=\"-8.2\">class = joga</text>\n",
       "</g>\n",
       "<!-- 12&#45;&gt;13 -->\n",
       "<g class=\"edge\" id=\"edge13\"><title>12-&gt;13</title>\n",
       "<path d=\"M865.943,-111.069C865.943,-102.688 865.943,-93.9148 865.943,-85.4828\" fill=\"none\" stroke=\"black\"/>\n",
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       "</g>\n",
       "<!-- 14 -->\n",
       "<g class=\"node\" id=\"node15\"><title>14</title>\n",
       "<path d=\"M1040.05,-75.3003C1040.05,-75.3003 951.84,-75.3003 951.84,-75.3003 945.84,-75.3003 939.84,-69.3003 939.84,-63.3003 939.84,-63.3003 939.84,-11.8997 939.84,-11.8997 939.84,-5.89973 945.84,0.100267 951.84,0.100267 951.84,0.100267 1040.05,0.100267 1040.05,0.100267 1046.05,0.100267 1052.05,-5.89973 1052.05,-11.8997 1052.05,-11.8997 1052.05,-63.3003 1052.05,-63.3003 1052.05,-69.3003 1046.05,-75.3003 1040.05,-75.3003\" fill=\"#e58139\" fill-opacity=\"0.235294\" stroke=\"black\"/>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"955.674\" y=\"-58.6\">gini = 0.4911</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"954.125\" y=\"-41.8\">samples = 90</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"947.891\" y=\"-25\">value = [51, 39]</text>\n",
       "<text font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" text-anchor=\"start\" x=\"958.793\" y=\"-8.2\">class = joga</text>\n",
       "</g>\n",
       "<!-- 12&#45;&gt;14 -->\n",
       "<g class=\"edge\" id=\"edge14\"><title>12-&gt;14</title>\n",
       "<path d=\"M915.832,-111.069C926.319,-101.583 937.361,-91.5942 947.782,-82.1666\" fill=\"none\" stroke=\"black\"/>\n",
       "<polygon fill=\"black\" points=\"950.177,-84.7204 955.245,-75.4162 945.481,-79.5292 950.177,-84.7204\" stroke=\"black\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import SVG\n",
    "import numpy as np\n",
    "import pydotplus\n",
    "from sklearn.externals.six import StringIO\n",
    "dot_data = StringIO()\n",
    "export_graphviz(\n",
    "    decision_tree_binary_classifier,\n",
    "    out_file=dot_data,  \n",
    "    feature_names=np.array(categorical_cols),\n",
    "    class_names=['joga', 'nao joga'],  \n",
    "    filled=True,\n",
    "    rounded=True,  \n",
    "    special_characters=True\n",
    ")\n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
    "SVG(graph.create(format='svg'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Matriz de Confusão **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ASWZmZo3ycxgZGfEPu8LCQo3qkEdsbCy/7eXlpTK/ZB7JsvJwcHBQWZ+hoSGEQmGdnlNd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\n3/IjXYgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119ef1550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from reports.visualize import visualization\n",
    "test_output = decision_tree_binary_classifier.predict(test[categorical_cols])\n",
    "visualization.plot_confusion_matrix((confusion_matrix(test.target, test_output)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Desempenho do Classificador **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tdrGeujmFy8vLsXfvXuzduxft27eHi4sLbG1t2cl3ycnJCA4OZldulTU5bcWK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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119ef16a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report = classification_report(\n",
    "    test.target,\n",
    "    test_output,\n",
    "    target_names=[\"Não_Joga\", \"Joga\"]\n",
    ")\n",
    "visualization().plot_classification_report(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}