notebooks/T7 - 2 - Trees - Árboles de Regresión.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Árboles de Regresión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>crim</th>\n",
       "      <th>zn</th>\n",
       "      <th>indus</th>\n",
       "      <th>chas</th>\n",
       "      <th>nox</th>\n",
       "      <th>rm</th>\n",
       "      <th>age</th>\n",
       "      <th>dis</th>\n",
       "      <th>rad</th>\n",
       "      <th>tax</th>\n",
       "      <th>ptratio</th>\n",
       "      <th>black</th>\n",
       "      <th>lstat</th>\n",
       "      <th>medv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      crim    zn  indus  chas    nox     rm   age     dis  rad  tax  ptratio  \\\n",
       "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
       "\n",
       "    black  lstat  medv  \n",
       "0  396.90   4.98  24.0  \n",
       "1  396.90   9.14  21.6  \n",
       "2  392.83   4.03  34.7  \n",
       "3  394.63   2.94  33.4  \n",
       "4  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"../datasets/boston/Boston.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames = data.columns.values.tolist()\n",
    "predictors = colnames[:13]\n",
    "target = colnames[13]\n",
    "X = data[predictors]\n",
    "Y = data[target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "regtree = DecisionTreeRegressor(min_samples_split=30, min_samples_leaf=10, max_depth=5, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=5, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=10,\n",
       "           min_samples_split=30, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=0, splitter='best')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regtree.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = regtree.predict(data[predictors])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"preds\"] = preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>preds</th>\n",
       "      <th>medv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23.787500</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.787500</td>\n",
       "      <td>21.6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.247826</td>\n",
       "      <td>34.7</td>\n",
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       "      <th>3</th>\n",
       "      <td>35.247826</td>\n",
       "      <td>33.4</td>\n",
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       "      <th>4</th>\n",
       "      <td>35.247826</td>\n",
       "      <td>36.2</td>\n",
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       "      <th>5</th>\n",
       "      <td>23.787500</td>\n",
       "      <td>28.7</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>21.863636</td>\n",
       "      <td>22.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20.020833</td>\n",
       "      <td>27.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20.020833</td>\n",
       "      <td>16.5</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20.020833</td>\n",
       "      <td>18.9</td>\n",
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       "      <th>10</th>\n",
       "      <td>20.020833</td>\n",
       "      <td>15.0</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>21.863636</td>\n",
       "      <td>18.9</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20.020833</td>\n",
       "      <td>21.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>23.787500</td>\n",
       "      <td>20.4</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20.318750</td>\n",
       "      <td>18.2</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>23.787500</td>\n",
       "      <td>19.9</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>23.787500</td>\n",
       "      <td>23.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17.233962</td>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20.318750</td>\n",
       "      <td>20.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20.318750</td>\n",
       "      <td>18.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>14.041667</td>\n",
       "      <td>13.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>20.318750</td>\n",
       "      <td>19.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>17.233962</td>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>14.041667</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>17.233962</td>\n",
       "      <td>15.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>17.233962</td>\n",
       "      <td>13.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>17.233962</td>\n",
       "      <td>16.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>17.233962</td>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>20.318750</td>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>20.318750</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>17.233962</td>\n",
       "      <td>16.7</td>\n",
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       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>9.913636</td>\n",
       "      <td>12.0</td>\n",
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       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>13.922222</td>\n",
       "      <td>14.6</td>\n",
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       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>20.318750</td>\n",
       "      <td>21.4</td>\n",
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       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>20.318750</td>\n",
       "      <td>23.0</td>\n",
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       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>23.787500</td>\n",
       "      <td>23.7</td>\n",
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       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>28.978261</td>\n",
       "      <td>25.0</td>\n",
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       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>20.318750</td>\n",
       "      <td>21.8</td>\n",
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       "      <th>484</th>\n",
       "      <td>20.318750</td>\n",
       "      <td>20.6</td>\n",
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       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>20.318750</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>17.233962</td>\n",
       "      <td>19.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>20.318750</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>17.233962</td>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>14.041667</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>14.041667</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>17.233962</td>\n",
       "      <td>13.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>20.318750</td>\n",
       "      <td>20.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>20.318750</td>\n",
       "      <td>21.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>20.318750</td>\n",
       "      <td>24.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>17.233962</td>\n",
       "      <td>23.1</td>\n",
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       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>14.041667</td>\n",
       "      <td>19.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>20.318750</td>\n",
       "      <td>18.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>20.318750</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>17.233962</td>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>20.318750</td>\n",
       "      <td>16.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>23.787500</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>23.787500</td>\n",
       "      <td>20.6</td>\n",
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       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>28.978261</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>31.170000</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>23.787500</td>\n",
       "      <td>11.9</td>\n",
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      "text/plain": [
       "         preds  medv\n",
       "0    23.787500  24.0\n",
       "1    23.787500  21.6\n",
       "2    35.247826  34.7\n",
       "3    35.247826  33.4\n",
       "4    35.247826  36.2\n",
       "5    23.787500  28.7\n",
       "6    21.863636  22.9\n",
       "7    20.020833  27.1\n",
       "8    20.020833  16.5\n",
       "9    20.020833  18.9\n",
       "10   20.020833  15.0\n",
       "11   21.863636  18.9\n",
       "12   20.020833  21.7\n",
       "13   23.787500  20.4\n",
       "14   20.318750  18.2\n",
       "15   23.787500  19.9\n",
       "16   23.787500  23.1\n",
       "17   17.233962  17.5\n",
       "18   20.318750  20.2\n",
       "19   20.318750  18.2\n",
       "20   14.041667  13.6\n",
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       "..         ...   ...\n",
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       "\n",
       "[506 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "data[[\"preds\", \"medv\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
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       "<text text-anchor=\"middle\" x=\"332.5\" y=\"-134.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">mse = 9.016</text>\n",
       "<text text-anchor=\"middle\" x=\"332.5\" y=\"-119.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 24</text>\n",
       "<text text-anchor=\"middle\" x=\"332.5\" y=\"-104.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = 20.021</text>\n",
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       "<text text-anchor=\"middle\" x=\"430.5\" y=\"-141.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">lstat &lt;= 18.885</text>\n",
       "<text text-anchor=\"middle\" x=\"430.5\" y=\"-126.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">mse = 8.733</text>\n",
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       "<text text-anchor=\"middle\" x=\"428.5\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">mse = 5.952</text>\n",
       "<text text-anchor=\"middle\" x=\"428.5\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 53</text>\n",
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       "<text text-anchor=\"middle\" x=\"525.5\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">mse = 7.862</text>\n",
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       "<text text-anchor=\"middle\" x=\"525.5\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = 14.042</text>\n",
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       "<text text-anchor=\"middle\" x=\"528.5\" y=\"-134.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">mse = 18.606</text>\n",
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       "<text text-anchor=\"middle\" x=\"626.5\" y=\"-141.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">lstat &lt;= 19.645</text>\n",
       "<text text-anchor=\"middle\" x=\"626.5\" y=\"-126.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">mse = 8.908</text>\n",
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       "<text text-anchor=\"middle\" x=\"624.5\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">mse = 4.18</text>\n",
       "<text text-anchor=\"middle\" x=\"624.5\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 18</text>\n",
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       "<text text-anchor=\"middle\" x=\"719.5\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">mse = 6.177</text>\n",
       "<text text-anchor=\"middle\" x=\"719.5\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 44</text>\n",
       "<text text-anchor=\"middle\" x=\"719.5\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = 9.914</text>\n",
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       "<text text-anchor=\"middle\" x=\"672.5\" y=\"-349.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">lstat &lt;= 5.495</text>\n",
       "<text text-anchor=\"middle\" x=\"672.5\" y=\"-334.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">mse = 41.296</text>\n",
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       "<text text-anchor=\"middle\" x=\"818.5\" y=\"-349.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">ptratio &lt;= 15.4</text>\n",
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       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x10b07a198>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import export_graphviz\n",
    "with open(\"resources/boston_rtree.dot\", \"w\") as dotfile:\n",
    "    export_graphviz(regtree, out_file=dotfile, feature_names=predictors)\n",
    "    dotfile.close()\n",
    "    \n",
    "import os\n",
    "from graphviz import Source\n",
    "file = open(\"resources/boston_rtree.dot\", \"r\")\n",
    "text = file.read()\n",
    "Source(text)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import KFold\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-14.21501779 -16.62018743 -18.48312343 -46.22608214 -10.25202434\n",
      " -18.39546877 -15.08005674 -32.23929702 -23.04375075 -10.93761052]\n",
      "-20.54926189262014\n"
     ]
    }
   ],
   "source": [
    "cv = KFold(n=X.shape[0], n_folds = 10, shuffle=True, random_state=1)\n",
    "scores = cross_val_score(regtree, X, Y, scoring=\"mean_squared_error\", cv = cv, n_jobs=1)\n",
    "print(scores)\n",
    "score = np.mean(scores)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('crim', 0.032184533425691254),\n",
       " ('zn', 0.0),\n",
       " ('indus', 0.0),\n",
       " ('chas', 0.0),\n",
       " ('nox', 0.016195328299152056),\n",
       " ('rm', 0.6341876193016562),\n",
       " ('age', 0.014165271650613091),\n",
       " ('dis', 0.0),\n",
       " ('rad', 0.0),\n",
       " ('tax', 0.0),\n",
       " ('ptratio', 0.009620458196377114),\n",
       " ('black', 0.0),\n",
       " ('lstat', 0.2936467891265104)]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(predictors,regtree.feature_importances_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10000, n_jobs=2,\n",
       "           oob_score=True, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest = RandomForestRegressor(n_jobs=2, oob_score=True, n_estimators=10000)\n",
    "forest.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rforest_pred</th>\n",
       "      <th>medv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28.481279</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22.624058</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34.585730</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34.790003</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34.323048</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>23.997725</td>\n",
       "      <td>28.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20.229325</td>\n",
       "      <td>22.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17.294122</td>\n",
       "      <td>27.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17.910405</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19.775659</td>\n",
       "      <td>18.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20.693292</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20.813106</td>\n",
       "      <td>18.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>21.245221</td>\n",
       "      <td>21.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>19.944679</td>\n",
       "      <td>20.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>19.966388</td>\n",
       "      <td>18.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>20.347713</td>\n",
       "      <td>19.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>21.397943</td>\n",
       "      <td>23.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18.328238</td>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18.738948</td>\n",
       "      <td>20.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19.772087</td>\n",
       "      <td>18.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>14.508871</td>\n",
       "      <td>13.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>18.584422</td>\n",
       "      <td>19.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>16.068443</td>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>15.013955</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>16.630460</td>\n",
       "      <td>15.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>15.760181</td>\n",
       "      <td>13.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>17.402940</td>\n",
       "      <td>16.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>15.118416</td>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>20.057530</td>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>23.006519</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>16.016040</td>\n",
       "      <td>16.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>13.579000</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>14.573479</td>\n",
       "      <td>14.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>20.076588</td>\n",
       "      <td>21.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>21.017078</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>28.701422</td>\n",
       "      <td>23.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>32.116689</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>20.267125</td>\n",
       "      <td>21.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>20.983144</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>22.013684</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>19.040222</td>\n",
       "      <td>19.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>21.368733</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>14.401306</td>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>13.073253</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>10.850246</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>16.120535</td>\n",
       "      <td>13.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>20.220841</td>\n",
       "      <td>20.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>19.672850</td>\n",
       "      <td>21.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>20.752433</td>\n",
       "      <td>24.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>19.425565</td>\n",
       "      <td>23.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>18.670488</td>\n",
       "      <td>19.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>20.333564</td>\n",
       "      <td>18.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>21.284089</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>19.855477</td>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>20.299728</td>\n",
       "      <td>16.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>24.289566</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>18.987793</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>27.636796</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>26.192514</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>20.897088</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     rforest_pred  medv\n",
       "0       28.481279  24.0\n",
       "1       22.624058  21.6\n",
       "2       34.585730  34.7\n",
       "3       34.790003  33.4\n",
       "4       34.323048  36.2\n",
       "5       23.997725  28.7\n",
       "6       20.229325  22.9\n",
       "7       17.294122  27.1\n",
       "8       17.910405  16.5\n",
       "9       19.775659  18.9\n",
       "10      20.693292  15.0\n",
       "11      20.813106  18.9\n",
       "12      21.245221  21.7\n",
       "13      19.944679  20.4\n",
       "14      19.966388  18.2\n",
       "15      20.347713  19.9\n",
       "16      21.397943  23.1\n",
       "17      18.328238  17.5\n",
       "18      18.738948  20.2\n",
       "19      19.772087  18.2\n",
       "20      14.508871  13.6\n",
       "21      18.584422  19.6\n",
       "22      16.068443  15.2\n",
       "23      15.013955  14.5\n",
       "24      16.630460  15.6\n",
       "25      15.760181  13.9\n",
       "26      17.402940  16.6\n",
       "27      15.118416  14.8\n",
       "28      20.057530  18.4\n",
       "29      23.006519  21.0\n",
       "..            ...   ...\n",
       "476     16.016040  16.7\n",
       "477     13.579000  12.0\n",
       "478     14.573479  14.6\n",
       "479     20.076588  21.4\n",
       "480     21.017078  23.0\n",
       "481     28.701422  23.7\n",
       "482     32.116689  25.0\n",
       "483     20.267125  21.8\n",
       "484     20.983144  20.6\n",
       "485     22.013684  21.2\n",
       "486     19.040222  19.1\n",
       "487     21.368733  20.6\n",
       "488     14.401306  15.2\n",
       "489     13.073253   7.0\n",
       "490     10.850246   8.1\n",
       "491     16.120535  13.6\n",
       "492     20.220841  20.1\n",
       "493     19.672850  21.8\n",
       "494     20.752433  24.5\n",
       "495     19.425565  23.1\n",
       "496     18.670488  19.7\n",
       "497     20.333564  18.3\n",
       "498     21.284089  21.2\n",
       "499     19.855477  17.5\n",
       "500     20.299728  16.8\n",
       "501     24.289566  22.4\n",
       "502     18.987793  20.6\n",
       "503     27.636796  23.9\n",
       "504     26.192514  22.0\n",
       "505     20.897088  11.9\n",
       "\n",
       "[506 rows x 2 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"rforest_pred\"]= forest.oob_prediction_\n",
    "data[[\"rforest_pred\", \"medv\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.234649905954406"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"rforest_error2\"] = (data[\"rforest_pred\"]-data[\"medv\"])**2\n",
    "sum(data[\"rforest_error2\"])/len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8787644667661886"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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