decision_tree_regularization_cancer_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import export_graphviz\n",
    "\n",
    "import graphviz\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.keys()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'c:\\\\python365\\\\lib\\\\site-packages\\\\sklearn\\\\datasets\\\\data\\\\breast_cancer.csv'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
       "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
       "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
       "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
       "       'smoothness error', 'compactness error', 'concavity error',\n",
       "       'concave points error', 'symmetry error',\n",
       "       'fractal dimension error', 'worst radius', 'worst texture',\n",
       "       'worst perimeter', 'worst area', 'worst smoothness',\n",
       "       'worst compactness', 'worst concavity', 'worst concave points',\n",
       "       'worst symmetry', 'worst fractal dimension'], dtype='<U23')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.feature_names\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['malignant', 'benign'], dtype='<U9')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        1.238e-01, 1.866e-01, 2.416e-01, 1.860e-01, 2.750e-01, 8.902e-02],\n",
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       "        1.974e-01, 1.279e-01, 2.069e-01, 5.999e-02, 7.456e-01, 7.869e-01,\n",
       "        4.585e+00, 9.403e+01, 6.150e-03, 4.006e-02, 3.832e-02, 2.058e-02,\n",
       "        2.250e-02, 4.571e-03, 2.357e+01, 2.553e+01, 1.525e+02, 1.709e+03,\n",
       "        1.444e-01, 4.245e-01, 4.504e-01, 2.430e-01, 3.613e-01, 8.758e-02],\n",
       "       [1.142e+01, 2.038e+01, 7.758e+01, 3.861e+02, 1.425e-01, 2.839e-01,\n",
       "        2.414e-01, 1.052e-01, 2.597e-01, 9.744e-02, 4.956e-01, 1.156e+00,\n",
       "        3.445e+00, 2.723e+01, 9.110e-03, 7.458e-02, 5.661e-02, 1.867e-02,\n",
       "        5.963e-02, 9.208e-03, 1.491e+01, 2.650e+01, 9.887e+01, 5.677e+02,\n",
       "        2.098e-01, 8.663e-01, 6.869e-01, 2.575e-01, 6.638e-01, 1.730e-01],\n",
       "       [2.029e+01, 1.434e+01, 1.351e+02, 1.297e+03, 1.003e-01, 1.328e-01,\n",
       "        1.980e-01, 1.043e-01, 1.809e-01, 5.883e-02, 7.572e-01, 7.813e-01,\n",
       "        5.438e+00, 9.444e+01, 1.149e-02, 2.461e-02, 5.688e-02, 1.885e-02,\n",
       "        1.756e-02, 5.115e-03, 2.254e+01, 1.667e+01, 1.522e+02, 1.575e+03,\n",
       "        1.374e-01, 2.050e-01, 4.000e-01, 1.625e-01, 2.364e-01, 7.678e-02]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.data[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0])"
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     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.target[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension           ...             worst radius  \\\n",
       "0                 0.07871           ...                    25.38   \n",
       "1                 0.05667           ...                    24.99   \n",
       "2                 0.05999           ...                    23.57   \n",
       "3                 0.09744           ...                    14.91   \n",
       "4                 0.05883           ...                    22.54   \n",
       "\n",
       "   worst texture  worst perimeter  worst area  worst smoothness  \\\n",
       "0          17.33           184.60      2019.0            0.1622   \n",
       "1          23.41           158.80      1956.0            0.1238   \n",
       "2          25.53           152.50      1709.0            0.1444   \n",
       "3          26.50            98.87       567.7            0.2098   \n",
       "4          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   worst compactness  worst concavity  worst concave points  worst symmetry  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   worst fractal dimension  \n",
       "0                  0.11890  \n",
       "1                  0.08902  \n",
       "2                  0.08758  \n",
       "3                  0.17300  \n",
       "4                  0.07678  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_df = pd.DataFrame(cancer.data, columns= cancer.feature_names)\n",
    "cancer_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 30)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "cancer_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_labels = pd.Series(cancer.target)\n",
    "cancer_labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569,)"
      ]
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     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "cancer_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 30 columns):\n",
      "mean radius                569 non-null float64\n",
      "mean texture               569 non-null float64\n",
      "mean perimeter             569 non-null float64\n",
      "mean area                  569 non-null float64\n",
      "mean smoothness            569 non-null float64\n",
      "mean compactness           569 non-null float64\n",
      "mean concavity             569 non-null float64\n",
      "mean concave points        569 non-null float64\n",
      "mean symmetry              569 non-null float64\n",
      "mean fractal dimension     569 non-null float64\n",
      "radius error               569 non-null float64\n",
      "texture error              569 non-null float64\n",
      "perimeter error            569 non-null float64\n",
      "area error                 569 non-null float64\n",
      "smoothness error           569 non-null float64\n",
      "compactness error          569 non-null float64\n",
      "concavity error            569 non-null float64\n",
      "concave points error       569 non-null float64\n",
      "symmetry error             569 non-null float64\n",
      "fractal dimension error    569 non-null float64\n",
      "worst radius               569 non-null float64\n",
      "worst texture              569 non-null float64\n",
      "worst perimeter            569 non-null float64\n",
      "worst area                 569 non-null float64\n",
      "worst smoothness           569 non-null float64\n",
      "worst compactness          569 non-null float64\n",
      "worst concavity            569 non-null float64\n",
      "worst concave points       569 non-null float64\n",
      "worst symmetry             569 non-null float64\n",
      "worst fractal dimension    569 non-null float64\n",
      "dtypes: float64(30)\n",
      "memory usage: 133.4 KB\n"
     ]
    }
   ],
   "source": [
    "cancer_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
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       "      <th>count</th>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.127292</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>0.181162</td>\n",
       "      <td>0.062798</td>\n",
       "      <td>...</td>\n",
       "      <td>16.269190</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.083946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.524049</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.027414</td>\n",
       "      <td>0.007060</td>\n",
       "      <td>...</td>\n",
       "      <td>4.833242</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.018061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.981000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>0.049960</td>\n",
       "      <td>...</td>\n",
       "      <td>7.930000</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.055040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.700000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.161900</td>\n",
       "      <td>0.057700</td>\n",
       "      <td>...</td>\n",
       "      <td>13.010000</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.071460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.370000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>0.179200</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>...</td>\n",
       "      <td>14.970000</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.080040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.780000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.195700</td>\n",
       "      <td>0.066120</td>\n",
       "      <td>...</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.092080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>28.110000</td>\n",
       "      <td>39.280000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>2501.000000</td>\n",
       "      <td>0.163400</td>\n",
       "      <td>0.345400</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.201200</td>\n",
       "      <td>0.304000</td>\n",
       "      <td>0.097440</td>\n",
       "      <td>...</td>\n",
       "      <td>36.040000</td>\n",
       "      <td>49.540000</td>\n",
       "      <td>251.200000</td>\n",
       "      <td>4254.000000</td>\n",
       "      <td>0.222600</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.252000</td>\n",
       "      <td>0.291000</td>\n",
       "      <td>0.663800</td>\n",
       "      <td>0.207500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean radius  mean texture  mean perimeter    mean area  \\\n",
       "count   569.000000    569.000000      569.000000   569.000000   \n",
       "mean     14.127292     19.289649       91.969033   654.889104   \n",
       "std       3.524049      4.301036       24.298981   351.914129   \n",
       "min       6.981000      9.710000       43.790000   143.500000   \n",
       "25%      11.700000     16.170000       75.170000   420.300000   \n",
       "50%      13.370000     18.840000       86.240000   551.100000   \n",
       "75%      15.780000     21.800000      104.100000   782.700000   \n",
       "max      28.110000     39.280000      188.500000  2501.000000   \n",
       "\n",
       "       mean smoothness  mean compactness  mean concavity  mean concave points  \\\n",
       "count       569.000000        569.000000      569.000000           569.000000   \n",
       "mean          0.096360          0.104341        0.088799             0.048919   \n",
       "std           0.014064          0.052813        0.079720             0.038803   \n",
       "min           0.052630          0.019380        0.000000             0.000000   \n",
       "25%           0.086370          0.064920        0.029560             0.020310   \n",
       "50%           0.095870          0.092630        0.061540             0.033500   \n",
       "75%           0.105300          0.130400        0.130700             0.074000   \n",
       "max           0.163400          0.345400        0.426800             0.201200   \n",
       "\n",
       "       mean symmetry  mean fractal dimension           ...             \\\n",
       "count     569.000000              569.000000           ...              \n",
       "mean        0.181162                0.062798           ...              \n",
       "std         0.027414                0.007060           ...              \n",
       "min         0.106000                0.049960           ...              \n",
       "25%         0.161900                0.057700           ...              \n",
       "50%         0.179200                0.061540           ...              \n",
       "75%         0.195700                0.066120           ...              \n",
       "max         0.304000                0.097440           ...              \n",
       "\n",
       "       worst radius  worst texture  worst perimeter   worst area  \\\n",
       "count    569.000000     569.000000       569.000000   569.000000   \n",
       "mean      16.269190      25.677223       107.261213   880.583128   \n",
       "std        4.833242       6.146258        33.602542   569.356993   \n",
       "min        7.930000      12.020000        50.410000   185.200000   \n",
       "25%       13.010000      21.080000        84.110000   515.300000   \n",
       "50%       14.970000      25.410000        97.660000   686.500000   \n",
       "75%       18.790000      29.720000       125.400000  1084.000000   \n",
       "max       36.040000      49.540000       251.200000  4254.000000   \n",
       "\n",
       "       worst smoothness  worst compactness  worst concavity  \\\n",
       "count        569.000000         569.000000       569.000000   \n",
       "mean           0.132369           0.254265         0.272188   \n",
       "std            0.022832           0.157336         0.208624   \n",
       "min            0.071170           0.027290         0.000000   \n",
       "25%            0.116600           0.147200         0.114500   \n",
       "50%            0.131300           0.211900         0.226700   \n",
       "75%            0.146000           0.339100         0.382900   \n",
       "max            0.222600           1.058000         1.252000   \n",
       "\n",
       "       worst concave points  worst symmetry  worst fractal dimension  \n",
       "count            569.000000      569.000000               569.000000  \n",
       "mean               0.114606        0.290076                 0.083946  \n",
       "std                0.065732        0.061867                 0.018061  \n",
       "min                0.000000        0.156500                 0.055040  \n",
       "25%                0.064930        0.250400                 0.071460  \n",
       "50%                0.099930        0.282200                 0.080040  \n",
       "75%                0.161400        0.317900                 0.092080  \n",
       "max                0.291000        0.663800                 0.207500  \n",
       "\n",
       "[8 rows x 30 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(cancer.data, cancer.target, stratify=cancer.target,\n",
    "                                                test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(455, 30)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(114, 30)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.032e+01, 1.635e+01, 6.531e+01, 3.249e+02, 9.434e-02, 4.994e-02,\n",
       "        1.012e-02, 5.495e-03, 1.885e-01, 6.201e-02, 2.104e-01, 9.670e-01,\n",
       "        1.356e+00, 1.297e+01, 7.086e-03, 7.247e-03, 1.012e-02, 5.495e-03,\n",
       "        1.560e-02, 2.606e-03, 1.125e+01, 2.177e+01, 7.112e+01, 3.849e+02,\n",
       "        1.285e-01, 8.842e-02, 4.384e-02, 2.381e-02, 2.681e-01, 7.399e-02],\n",
       "       [2.018e+01, 1.954e+01, 1.338e+02, 1.250e+03, 1.133e-01, 1.489e-01,\n",
       "        2.133e-01, 1.259e-01, 1.724e-01, 6.053e-02, 4.331e-01, 1.001e+00,\n",
       "        3.008e+00, 5.249e+01, 9.087e-03, 2.715e-02, 5.546e-02, 1.910e-02,\n",
       "        2.451e-02, 4.005e-03, 2.203e+01, 2.507e+01, 1.460e+02, 1.479e+03,\n",
       "        1.665e-01, 2.942e-01, 5.308e-01, 2.173e-01, 3.032e-01, 8.075e-02],\n",
       "       [1.066e+01, 1.515e+01, 6.749e+01, 3.496e+02, 8.792e-02, 4.302e-02,\n",
       "        0.000e+00, 0.000e+00, 1.928e-01, 5.975e-02, 3.309e-01, 1.925e+00,\n",
       "        2.155e+00, 2.198e+01, 8.713e-03, 1.017e-02, 0.000e+00, 0.000e+00,\n",
       "        3.265e-02, 1.002e-03, 1.154e+01, 1.920e+01, 7.320e+01, 4.083e+02,\n",
       "        1.076e-01, 6.791e-02, 0.000e+00, 0.000e+00, 2.710e-01, 6.164e-02],\n",
       "       [1.356e+01, 1.390e+01, 8.859e+01, 5.613e+02, 1.051e-01, 1.192e-01,\n",
       "        7.860e-02, 4.451e-02, 1.962e-01, 6.303e-02, 2.569e-01, 4.981e-01,\n",
       "        2.011e+00, 2.103e+01, 5.851e-03, 2.314e-02, 2.544e-02, 8.360e-03,\n",
       "        1.842e-02, 2.918e-03, 1.498e+01, 1.713e+01, 1.011e+02, 6.866e+02,\n",
       "        1.376e-01, 2.698e-01, 2.577e-01, 9.090e-02, 3.065e-01, 8.177e-02],\n",
       "       [1.137e+01, 1.889e+01, 7.217e+01, 3.960e+02, 8.713e-02, 5.008e-02,\n",
       "        2.399e-02, 2.173e-02, 2.013e-01, 5.955e-02, 2.656e-01, 1.974e+00,\n",
       "        1.954e+00, 1.749e+01, 6.538e-03, 1.395e-02, 1.376e-02, 9.924e-03,\n",
       "        3.416e-02, 2.928e-03, 1.236e+01, 2.614e+01, 7.929e+01, 4.593e+02,\n",
       "        1.118e-01, 9.708e-02, 7.529e-02, 6.203e-02, 3.267e-01, 6.994e-02]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 1, 1])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, 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, presort=False, random_state=24,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_tree = DecisionTreeClassifier(random_state=24)\n",
    "\n",
    "cancer_tree.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 1.000\n",
      "Accuracy on testing set: 0.904\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy on training set: {:.3f}'.format(cancer_tree.score(X_train, y_train)))\n",
    "print('Accuracy on testing set: {:.3f}'.format(cancer_tree.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=4,\n",
       "            max_features=None, 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, presort=False, random_state=24,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_tree = DecisionTreeClassifier(max_depth=4, random_state=24)\n",
    "cancer_tree.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.987\n",
      "Accuracy on testing set: 0.930\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy on training set: {:.3f}'.format(cancer_tree.score(X_train, y_train)))\n",
    "print('Accuracy on testing set: {:.3f}'.format(cancer_tree.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=5, min_impurity_decrease=0.0,\n",
       "            min_impurity_split=None, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=24, splitter='best')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_tree1 = DecisionTreeClassifier(max_leaf_nodes=5, random_state=24)\n",
    "cancer_tree1.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.971\n",
      "Accuracy on testing set: 0.939\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy on training set: {:.3f}'.format(cancer_tree1.score(X_train, y_train)))\n",
    "print('Accuracy on testing set: {:.3f}'.format(cancer_tree1.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "export_graphviz(cancer_tree,\n",
    "               out_file='cancer_tree.dot',\n",
    "               class_names=cancer.target_names,\n",
    "               feature_names=cancer.feature_names,\n",
    "               filled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
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       "<text text-anchor=\"middle\" x=\"529.5\" y=\"-513.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.468</text>\r\n",
       "<text text-anchor=\"middle\" x=\"529.5\" y=\"-498.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 455</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"196.5\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">area error &lt;= 38.605</text>\r\n",
       "<text text-anchor=\"middle\" x=\"196.5\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.029</text>\r\n",
       "<text text-anchor=\"middle\" x=\"196.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 270</text>\r\n",
       "<text text-anchor=\"middle\" x=\"196.5\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [4, 266]</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"77.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.015</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"196.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.298</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"414.5\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">worst area &lt;= 817.1</text>\r\n",
       "<text text-anchor=\"middle\" x=\"414.5\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.375</text>\r\n",
       "<text text-anchor=\"middle\" x=\"414.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 12</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"577.5\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">worst symmetry &lt;= 0.268</text>\r\n",
       "<text text-anchor=\"middle\" x=\"577.5\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.172</text>\r\n",
       "<text text-anchor=\"middle\" x=\"577.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 21</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"311.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 10</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"311.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = benign</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"434.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 2</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"557.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.444</text>\r\n",
       "<text text-anchor=\"middle\" x=\"557.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 3</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"680.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 18</text>\r\n",
       "<text text-anchor=\"middle\" x=\"680.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [18, 0]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"680.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = malignant</text>\r\n",
       "</g>\r\n",
       "<!-- 11&#45;&gt;13 -->\r\n",
       "<g id=\"edge13\" class=\"edge\"><title>11&#45;&gt;13</title>\r\n",
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       "<!-- 15 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"626.5\" y=\"-283.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"626.5\" y=\"-268.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"626.5\" y=\"-253.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [0, 5]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"626.5\" y=\"-238.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = benign</text>\r\n",
       "</g>\r\n",
       "<!-- 14&#45;&gt;15 -->\r\n",
       "<g id=\"edge15\" class=\"edge\"><title>14&#45;&gt;15</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M626.5,-341.907C626.5,-331.204 626.5,-319.615 626.5,-308.776\"/>\r\n",
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       "<text text-anchor=\"middle\" x=\"803.5\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">worst concavity &lt;= 0.191</text>\r\n",
       "<text text-anchor=\"middle\" x=\"803.5\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.04</text>\r\n",
       "<text text-anchor=\"middle\" x=\"803.5\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 146</text>\r\n",
       "<text text-anchor=\"middle\" x=\"803.5\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [143, 3]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"803.5\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = malignant</text>\r\n",
       "</g>\r\n",
       "<!-- 14&#45;&gt;16 -->\r\n",
       "<g id=\"edge16\" class=\"edge\"><title>14&#45;&gt;16</title>\r\n",
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       "<polygon fill=\"#399de5\" fill-opacity=\"0.333333\" stroke=\"black\" points=\"884,-187 723,-187 723,-104 884,-104 884,-187\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"803.5\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">worst symmetry &lt;= 0.284</text>\r\n",
       "<text text-anchor=\"middle\" x=\"803.5\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.48</text>\r\n",
       "<text text-anchor=\"middle\" x=\"803.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"803.5\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [2, 3]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"803.5\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = benign</text>\r\n",
       "</g>\r\n",
       "<!-- 16&#45;&gt;17 -->\r\n",
       "<g id=\"edge17\" class=\"edge\"><title>16&#45;&gt;17</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M803.5,-222.907C803.5,-214.649 803.5,-205.864 803.5,-197.302\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"807,-197.021 803.5,-187.021 800,-197.021 807,-197.021\"/>\r\n",
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       "<text text-anchor=\"middle\" x=\"958.5\" y=\"-164.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"958.5\" y=\"-149.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 141</text>\r\n",
       "<text text-anchor=\"middle\" x=\"958.5\" y=\"-134.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [141, 0]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"958.5\" y=\"-119.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = malignant</text>\r\n",
       "</g>\r\n",
       "<!-- 16&#45;&gt;20 -->\r\n",
       "<g id=\"edge20\" class=\"edge\"><title>16&#45;&gt;20</title>\r\n",
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       "<polygon fill=\"#399de5\" stroke=\"black\" points=\"852,-68 755,-68 755,-0 852,-0 852,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"803.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"803.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 3</text>\r\n",
       "<text text-anchor=\"middle\" x=\"803.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [0, 3]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"803.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = benign</text>\r\n",
       "</g>\r\n",
       "<!-- 17&#45;&gt;18 -->\r\n",
       "<g id=\"edge18\" class=\"edge\"><title>17&#45;&gt;18</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M803.5,-103.726C803.5,-95.5175 803.5,-86.8595 803.5,-78.56\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"807,-78.2996 803.5,-68.2996 800,-78.2996 807,-78.2996\"/>\r\n",
       "</g>\r\n",
       "<!-- 19 -->\r\n",
       "<g id=\"node20\" class=\"node\"><title>19</title>\r\n",
       "<polygon fill=\"#e58139\" stroke=\"black\" points=\"983,-68 870,-68 870,-0 983,-0 983,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"926.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"926.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 2</text>\r\n",
       "<text text-anchor=\"middle\" x=\"926.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [2, 0]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"926.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = malignant</text>\r\n",
       "</g>\r\n",
       "<!-- 17&#45;&gt;19 -->\r\n",
       "<g id=\"edge19\" class=\"edge\"><title>17&#45;&gt;19</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M849.301,-103.726C859.853,-94.3318 871.067,-84.349 881.582,-74.9883\"/>\r\n",
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       "</g>\r\n",
       "</g>\r\n",
       "</svg>\r\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x1d85b9b2390>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('cancer_tree.dot')as f:\n",
    "    dot_graph = f.read()\n",
    "graphviz.Source(dot_graph)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "export_graphviz(cancer_tree1,\n",
    "               out_file='cancer_tree1.dot',\n",
    "               class_names=cancer.target_names,\n",
    "               feature_names=cancer.feature_names,\n",
    "               filled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " viewBox=\"0.00 0.00 542.50 433.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n",
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       "<title>Tree</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"274\" y=\"-409.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">worst radius &lt;= 16.795</text>\r\n",
       "<text text-anchor=\"middle\" x=\"274\" y=\"-394.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.468</text>\r\n",
       "<text text-anchor=\"middle\" x=\"274\" y=\"-379.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 455</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"182\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.162</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"366\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">texture error &lt;= 0.473</text>\r\n",
       "<text text-anchor=\"middle\" x=\"366\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.1</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"366\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = malignant</text>\r\n",
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       "<g id=\"node3\" class=\"node\"><title>3</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"52\" y=\"-164.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.036</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"52\" y=\"-119.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = benign</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"194\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">worst texture &lt;= 25.62</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"132\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.375</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"255\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.172</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"355\" y=\"-164.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
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       "<g id=\"edge7\" class=\"edge\"><title>2&#45;&gt;5</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M362.184,-222.907C361.177,-212.204 360.088,-200.615 359.069,-189.776\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"362.539,-189.295 358.118,-179.667 355.57,-189.951 362.539,-189.295\"/>\r\n",
       "</g>\r\n",
       "<!-- 6 -->\r\n",
       "<g id=\"node9\" class=\"node\"><title>6</title>\r\n",
       "<polygon fill=\"#e58139\" fill-opacity=\"0.980392\" stroke=\"black\" points=\"534.5,-179.5 421.5,-179.5 421.5,-111.5 534.5,-111.5 534.5,-179.5\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"478\" y=\"-164.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.04</text>\r\n",
       "<text text-anchor=\"middle\" x=\"478\" y=\"-149.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 146</text>\r\n",
       "<text text-anchor=\"middle\" x=\"478\" y=\"-134.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [143, 3]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"478\" y=\"-119.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = malignant</text>\r\n",
       "</g>\r\n",
       "<!-- 2&#45;&gt;6 -->\r\n",
       "<g id=\"edge8\" class=\"edge\"><title>2&#45;&gt;6</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M404.858,-222.907C415.949,-211.321 428.033,-198.698 439.125,-187.111\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"441.864,-189.311 446.25,-179.667 436.807,-184.47 441.864,-189.311\"/>\r\n",
       "</g>\r\n",
       "</g>\r\n",
       "</svg>\r\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x1d85b943b70>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('cancer_tree1.dot')as f:\n",
    "    dot_graph = f.read()\n",
    "graphviz.Source(dot_graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importances:\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.04578469\n",
      " 0.         0.00315729 0.         0.         0.         0.\n",
      " 0.         0.         0.73354757 0.03231926 0.         0.01332729\n",
      " 0.         0.         0.01716116 0.12202752 0.02312884 0.00954637]\n"
     ]
    }
   ],
   "source": [
    "print('Feature importances:\\n{}'.format(cancer_tree.feature_importances_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_feature_importances_cancer_data(model):\n",
    "    n_features = cancer.data.shape[1]\n",
    "    plt.barh(range(n_features),model.feature_importances_, align=\"center\")\n",
    "    plt.yticks(np.arange(n_features),cancer.feature_names)\n",
    "    plt.xlabel(\"Feature Imaportance\")\n",
    "    plt.ylabel(\"Feature\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+/Wr/9ZIkSZJFpluNsOl+cqxZ5h7XjwP3mK8/zKKp2cafzey90DiPw6POK8qzog03lvTQZvZ6jfZWMvfYBThQ0nR8rXwVXOc9GThY7oe9qZm9jWdJW0fSBZK+HO9sIYpR4n379q3RnCRJkqQ1dKsOuxvLseYX9ufTdGajPIjAqC7PUoXyzd2/aO4h4OhIjjLQzNY2s7FmNgH/sHkBuFrSgWb2BjAAnxb/XzxiPEmSJOkkulWHHZTkWBPwDvtwYHqMXh8BdpC0akztfosK5hchx+pjZn8BjsWzmUEVOZY8y9lkXI5VWgteMdqwh6TlJa2Ay7EmtvB5dpe0rKRVgCFxn2ryrN7At6Iskv4VU+a1ZGRFxgBHxDICktaXtIKkz8T9LsOXCraIenuY2U3A/+FZ2ZIkSZJOorutYUP7GoT8Cfi9FjYI2QMfje+mFhqE1MEk4A6gH3CauSFHNcOQXYCrgPskfQysHMebGJvUuNcIfHp8mnyh+5V4tiG4DO0j3GzkQHwa/gp5HnFwmVuSJEnSSXS7EbaZ3WNmvczsndhfH1gqOivwdexXzGwTYIwiehzPzz1L0mPAsWY2OPJ/rw2sK48eN1x7vAxwS1n0+M/wke4eMZ38UNT7XPz7MR68Bd4B/l3S7ZJmA1OAfpIexTvX0rr6p4Av4dPfj+HZyAA+jX9ILI2vM38BX6tvwIPX/osHqr2Ku2pdEPf/VnxkjAeOlDQe/yD4dbyr+Xj+8/eijqfxDvqaaGMv3EhkDzObgWvDl8b/OzmomT9NkiRJ0o50uw67Cp1p5nEy8KUw4ditcHwTvHMdDJwOvGtmm+OBcqUc3XviTmCb4cFoP4/jVwE/Kh6PCPApwH7Rjvei7Kth9nExvlRQYkP8Y2Aw8HNJveS2nfviUeYD8U5+v3gna5rZJma2KY2JUk4CNo92HF7t5SdJkiTtz+LSYXdm9PgDeFrRQ/H0oCXGmdnbZvYK7ld9exyfhRt49AE+MLMj4/iVwPZxfCUzu694vMb9K0WDg38IfBAR5f/Bo8x3xiVvkyNSfGdgHapHhM8ERknaHx+RL4QK5h+vvPJKjWYmSZIkrWGx6LA7M3rczA7Hp8vXAqaXAsKoP0q8tVSKBi+/f9E05MpClPgGZja8RkT4rsCFeCc/VdJC7U5ZV5IkScewWHTYQYdHj8c165rZI2Z2Mr6mvFY9jQ0d9xulLGR4bvD7qh1vrh11cg+wt6RPRttXlvSZShHhEWy2lpmNA07E17Z7t+LeSZIkSSvojlHi1WjP6PEmUdhl69hnS1ovyt+Dm4UMpD4OAi6RtDw+LX1wM8dHxvH3gK3xjGTr4x8KzWJmT0j6GZ4BrgfwET6ifo+FI8J7AtfEFL3wnOJv1vlcSZIkSRuzRJp/dDfUTmYlkpaKZYA2YZnV17PVD/rNQsfnpOVmkiRJVbQ4mn90N9S5ZiVfl/SI3OLzr5JWi+PDJV0qzyF+laobglQ1IEmSJEk6nsVpSrwr0plmJfcDn4/y38XXoX8Y5wYB25rZe3LzjkqGIM9RwYDEckomSZKkU8gOu30pl5tNo1FudgwFuRlAZDjbHriV6nKzO4DRddz708D1klbHk5/MLpy7raDj3gXYTFIpQ1of3BDkedyAZHs8sr1kQPLv4k1UcOvquWJGiSdJkrQXOSXejnSyWckFwO8iEcr3cPOQEu8UtisaglDdgKT8GRfIunou36eOZiVJkiSLQo6w25+S3OwQPGnKr4GpMVX9CPDbmHJ+A5ebXVBeQcjNljezv0h6GPh7nKol8+qDu29B7bSiJUOQe8NsZP24rpoBSVU2XbMPUzLALEmSpF3IEXYbI2kPSRsVDk0EVsflZi/jU9sL5Ga4hGocLgebVkNuNlruj/0cTeVmJ0Rg2bpl1wwHbpA0kQqyL0lDInBtBJ6SdVrkWf89/iE3CmiQG5DsR6MBSZIkSdIJpKxrEWkvqVVL79eK+oYD88zsnBZcU1MG1tDQYFOmTGmL5iVJkiwxpKyrCp0stRop6RJJEyU9LelrcbyatGqIpHGSrsWn05E0r3DuPkl/irrOkLSfpEnR9nWjXF9JN0XdkyVtI7f/PBw4Ltq5XaVycX0TGVg7/VmSJEmSZlgS17A7U2oFbtCxAx54Nk7SZ3H3rkrSKvBgs03MbHaFugYAnwNexzOijTCzwZK+j9tuHgv8Fs9Sdr+kfsAYM/ucpEsojLDjo6BJuagbCjKw8gYUo8T79etX/a0nSZIkrWJJ7LA7U2oF8KfwpX5G0rO4DWY1adWHwKQqnTXA5FgHR9I/gFInPwuPRgcYCmwkLQhIXzGevZxa5W6r1FmDR4kDl4JPiVd/7CRJkqQ1LHEddkQ9z6FRajWTplKr9Wtc3kRqJWkwblH5TeAo3G+72SZU2C9Jq8YUT0gaQlMJVjn1OIL1ALYu73ALHTN1lKvVhiRJkqQDWOLWsINOcfYK9pHUI9aY1wH+hmucT4ypeSStL2mFKL9aWdR5S3kG+Emh3dXaORb/6CgvlyRJknQBlrgRdtBZzl7CO+j78Kxhh5vZ+3JXrrm4tErAK3iCFIBPARvh0qtFYT6wVUjClsI/Ug4HbgdulOcIPxpfDriwQrm6mfXCXPqfdEeLG5jmIEmSJM2zRHbYZnYP0Kuwv37Z+WuBa0v7kk7Ep8N7SzoPGGBmO0XHfrCZ7V+KLMc75dvN7Edx7Tw8WcqXgNeAFYFV8PXvIZJexyPL58Zvr1InL+lDPNDtbLkt5l7AAEl34VnI3pW0oZk9FdduBEwBNgDmxpr4ADwRynt48NiTklY1s6clHQKcY2YTQ+Y1F089+irwv5LOBobggXlvm9nvW/XikyRJkkVmieywF4G2iiy/Fo8Q/3Q9keVm9mD5OUn34CPzZyRtBVyEr50fhkeXz462ft7MXpd0FHC8mU2J62s9Z7OmIDUC4JIkSZJ2JDvs+miryPIDo66WRpYT9fYGvoBnMCsdXgbAzF6WdDI+lb+nmb2+CM9ZjylIkw5baf6RJEnSIWSHXQddILK8RA/gzTDjqMSm+LT7GjXq+C+NwYblZh6VTEHGUIOirGuZ1ddLWVeSJEk7saRGiS8KnRVZvuCcmb0FzJa0T9QnSQNiezDwFWBz4HhJa1epew4+9Q2+Jl6NkilIpcj1JEmSpIPJEXb9dGhkuaQ9gKfLz+FGHBdHEFov4DpJTwGX4QFwL0r6IXC5pJ2AkcAlkt7Dk71chTuEjcINPqoxAs/KVilyvSLp1pUkSdJ+pPlHJ6NWmoioGUOOsrLjKQSgtTXLrL6erX7Qb9qj6iRJki5La6WpSvOP9kWdbyLya0njgDMlDZb0oNxm80FJG0S55SRdF/VeDyxXqGNOTOH3l9tqlo4fHxIvJB1TaNd17fMmkyRJknrIKfFFp7NNRNYHhprZx5JWBLaPoLahwC/x9ekjgHfNbDNJm+HR7S3hJGBtM/tA0kotvDZJkiRpQ3KEveiUS70eolHqNZGC1CumrEtSL6huIvIN4N06739DYSq9Dy71egw4D9g4jm8PXANgZjPx6PaWMBMYJWl/PLp8ISQdJmmKpCkfvzu3hdUnSZIk9ZId9iJiZh/hEdclqddEmkq9amUoaSL1wi00b8KDuu6qswlFCdZpwDgz2wT4Ok3lWs0FKRRlXpRduytwIT5LMFXSQjMyZnapmTWYWUPP5fvU2fQkSZKkpeSUeOsoSb0OwS0tfw1MjantR/Bo7FXxKfFvAReUVxBSr+XN7C+SHgb+HqeaMxEp0gdPPwowrKx9++G+25sAm1W49mXgk5JWAeYBXwPuktQDWMvMxkm6H/g20Bt4s1ojMko8SZKk/eiwEXYEMD0ZcqLW1jUs1oibKzeykKmrWpkFQVeSGiSd34KmTARWx6VeL+NT2wukXkBJ6jUDmFZD6jVa0ju4drso9TohAsnWrXBdkbOAX0l6AOhZOH4x0Ftu6HEiMKn8wpgpOBXXko8GnopTPYFr5PnRHwXOM7OqnXWSJEnSvnSYrCu0wl8pz0XdEllS4Zrx1CFPqkcaJal/lNmkJW3ozpS/83r/Bs2Va2hosClT2kUxliRJsthSr6yrQ6bEJV2Cez/fJulyfAp3DTwxx6uSfgJcjUdPAxxlZg/GtScCB+A2kXfiblQNeDDUe8DWwAn42u1y+Hry96zGl4ikQcDleIDX/YXjQ/APga+FtGltfAS9PvAD4PN4NrEXgK9HytJB+FR4b9zlalgkUhmPj1p3BFYCvhOuWBsDVwBL4zMce4WRx7xwAxM+Yv4Kvv78CzO7Pto2PO6xCR70tn/5c8Zo/ELCzQs41Myeio+X1/FMaNMkvV32NzgEH5E34OvaP4jp8GH4Wvay8fdpSSrVJEmSpK0wsw754QFaq8b2cLzDWS72lweWje31gCmx/RW8A14+9leOf8cDDYW6Vy5sX413puBZvvau0JaZwA6xfTYusQK3khxdaOP9eDaxAXjn95U4dwseINYr2tc3ju8LXF5o47mx/VXgr7F9AbBfbC9deAfz4t+9gLvxKenVgH/hHw1DcPvLT+Md/UO4s1b5s90DrBfbWwH3Ft7FaKBnlb/BD4ErYnvDuO+y+Jr488V3XHa/w/CPqCn9+vWzJEmSpGWU+rzmfp0ZdFZ0huoF/E7SQFzyVDLTGIp3Iu8CWHUHqh1jJL48sDLwOHB7pYKS+gArmVkp1/fV+IdBJe40H0XPwjvQUgT3LHxkugE+2r3bB8b0BF4qXH9z/Ds1yoN3tD+V9GngZjN7puye2wJ/NI8if1nSfbhE7C1gkpk9H88xPeoszhBUdfMKilIwaPo32JYIijMfkf+Txr/D3dXevRXMPxoaGjJtXpIkSTvRmR12UZZ0HB6tPAAfPb4fx0UzsiR5Xu6L8BH3czGVXe5C1eSS5uos8AGAmc2X9FF8CYFPzy8VdT1uZlvXuh7/CFkq6ro2Ish3BcZI+q6Z3VvWvprtKa+zQHNuXu/U2K913/LrkiRJkg6mq+iw+wAvmdl8fL26FOk8FjhE0vIAkRUMmkqeSp3zqzHCrBkVbh7pPFfStnFov1a0+29AX0lbR/t6xRp1VSStAzxrZucDt1GQWskNP54F9pXUU1JfPPnJQtHdlbAabl51UJKAIWl9oF88X5IkSdIF6Cod9kXAQaFDXp8Y0ZnZXXinNiWmgI+P8iNxB6rp+KjzMnya+lZgch33Oxi4UNJDwHvNFa6GmX2IfyCcKWkGMB2fkq7FvsBj0fYNcfesEnsAL+Jr7DOAe4ETzezfNJVr1WI/4DvRnseB3csLlCdAif2LgJ4x/X89Hjz3QVm5etuQJEmStDFLrFuX3Nv5T3gQV088W9ireIT6nlHmi8ARZvYNSfPw6OuheCKUn+DR3P2AY83stoio3iPq2wQ4Fw8sOwD/sPiqmb1eKZIbX3sfjQeWzcWDz/6AB7Vtg3few4D1Y119RbxjX89cS116rr7AJdEuom0PxFLBgqhwfPaiGP29M9Wj03+Or80PNLONqr3TlHUlSZK0nC4l6+qifBl40cx2hQXBaG/hI+++ZvYKPhK/IsqvgOcG/5GkW4BfAF8ENgKuxGcCwDvqzfGO8O/Aj8xsc0nnAQcCv8GDtA43l3NtBVxkZjuVG35E4NhKZrZD7PfHO9lbgW8CNxU76+C3eJKT+yX1A8YAn4tzg/DI8vfi42JrYLP4iNgLGIjHEawKTJY0Ia4bDGxiZRr6JEmSpONYkjvsWcA5ctvL0WY2EUDS1cD+kq7AO7QDo/yHNI0S/6AQQd6/UO84M3sbeFvSXBqj1WcBm9URyV3O9YXtEXjGslvxj4lDK5QfCmxUqHtFuUEJNI0Kh6bR381Fp1fsrCUdhku76NevX6UiSZIkSRuwxHbYZvZ0JD35Kp7Wc6yZnYqPqG/HI9VvsMbMXuVR4sUI8uJ7LK77zi/slyLLm4vkLmdBhHZMbfeXtAOup36sQvkewNZlHXNptN7mUeIp60qSJOkYukrQWYcTucjfNbNrgHOALQDM7EU88OtneHBbm9JMJHc9hh9XAX+kcaq+nLHAUaWd0LbXwwQWMTo9SZIkaX+W2A4b2BSYFNHaP8XXpEuMAp4zsyfa6d77AUdKepOmkdyVDD8OLrt2FPD/8E67Eseo70dmAAAgAElEQVQADZJmSnoCOLzONt1C5ej0JEmSpAuwxEaJ10LS74BHzewPXaAt88ysd2F/b2B3MzugFXWm+UeSJEkXod4o8S47wpZ0YIwSZ0QgGJI+I+meOH5PREGXbDTPl/SgpGdVsNSUdKKkWVHPGXHsUEmT49hNkpaX1EfSHElT8WQmN0l6LpKhrCvpLklTJU2UtGGF9g6XdLWkeyU9I+nQOC5JZ0t6LNqxbxwv2noOk3Rz3OMZSWfF8TOA5SRNlzRK0sV4RHpD1LdvhXZUbGu8o19LGofrxodLulTSWOAqSctKuiLa+KikHQttu0HS7fh0e5IkSdIZ1JNwvKN/wMZ4lq2SWUjJ9ON24KDYPgS41RqNLW7AP0A2Av5utc1DVinc6xfA0bH9Z2BHazTyGGE1DDXK2jwcn05eDpdFPYfrnquZefSn0XRkGJ7hrA8uB/snsJYVTEGs0RjkssJ+nwrtSPOPJEmSbgR1mn/UNcKWtH6MaEsjws0k/ayeaxeRnYAbzexVaGL6sTVwbWxfjUuRStxqZvPN151Xi2PVzEM2idHnLHw9uZRO9Hq8owbXOV9fJsOaDvwe73Ar8Wczey/aPQ7XLy+QS5nZy0BJLlXOPWY218zeB54APlOhzCxgqKQzJW1nZnOLJ+toa3PmH1fHe3oK/2ioy/zDzBrMrKFv376V30qSJEnSauqdEr8M+DHwEYCZzcQ7tPaiXoOOYpminEqFfyvVMxLPaLYpcAqN+chvA74iz1k+CA++WiDDKvw+V6HO8vaU9mvJpYo0Z+yBmT0d7ZqFS9FOLivSXFvT/CNJkqSbUm+HvbyZlUt8mg1SagX3AP8jaRVoYvrxII0fCvtRsJasQjXzkE8AL0nqRcH8w8zm4VKm3+LJVD62lhlq7B5rwavg/tWTaaFcStKp+JQ6ko4FPop2VpWiFdqf5h9JkiSLKfV22K/KZUYGCyKVX6p9yaJjZo8DpwP3yU0sfh2njgEOljQTz8/9/WbqqWYe8n/AI/ja8lNll10P7E/TDGPNGmoEk4A7gIeB08w13S2SS5nZyXiub4Bj8WnqmZJGUVuKVndbVWbiofrNP5bYRDtJkiSdTb3/A/5fPJvVhpJeAGbTOlvKZjGzK/GI6OKxOfj6dnnZYWX7vQvbZwBnlJ2/GLi4tK+ISMc/SGaamRQR6bhBxyvAwWb2L0kjgVUlPQh8Cu+Ab4yq1gA+iRt6lHTU38VH2x/jucVvl+ctHw+sE2X+BJwm6Th8+WEkHqm+BrAj8G+8s/+ymW0WbT4U+DYe8FVkPTx47WPgaRo/doYAz0q6H/hd7D8oT0F6G3AjsFa8g9eBfxSuez2iy6fhwWlJkiRJB9PsCFtSD6DBzIbindeGZratmf2z3VvXAcj9q38K7GRmA2gctf8OuCo6yFHA+YXLVseDtL5G48fAZ3Hjj62inrPi+M1mtmUcexL4TgSLzQB2iDJfB8ZYwcjD3C/7RTxqfUc8qcpupelxmhqTlJ5lVTxD21Az2wLvzH9QKPJ+/O2ui/2VzGwHMzu3meddP+rMzjpJkqSTaLbDNrP5RKpLM3vH3NhicaKtItJfBk5vTUR6rUaa2Tv4KPtroa3uZWazyop9Hpe1PRDT5gfRNNq8/B7F/VrPWx5dvgBJh0maImnKK6+8UusRkiRJklZQ75T43ZKOx/8HXzSjqCj16WZ0RET6HmY2Q25pOSSO34ZHehcj0ptjBO7D/RSVc4kLl2B9q8r1taLEyyk+S5p/JEmSdDL1Bp0dgq9jT8CTbUxl4bXT7kqXiUivUGcTMxAzewRfZ/42lXOJPwxsI+mz0YblI+K7Hlr6vEmSJEkHUtcI28zWbu+GdBZm9rikUkT6x8CjeHavY4DLJZ1ABJ01U89dcmesKVHPC8CXaYxI/yeuny66cV2PZ2gbUqXaS4E7Jb0U69jgAWoDzeyNCm14JUbxf5RU8tj+GR581hwtet4kSZKkY6nL/EPSgZWOm9lVbd6ixQBJ/fFR8ybtUPdo4Dwzu6cVdfQsjujL92tcl+YfSZIkbYza2Pxjy8JvOzwP9W6L3Lo2Qi03CLlY0ji5QcgOki6X9GRItUp1zpN0rqRpcX3fOL6QYUgcX03SLXF8hqQv4JHj68pNO86WNETSeEk3SnpKbuShuH6QpPvkZh1jJK0ex4+R9EQ8x3WSVpL0HB4Mdq7coGMh72xJ+0uaFPf+fUlzHc91qqRHgK3lRicnh8xrH0kDJT0c97tF0v+L68ZL+mXIv2rq3pMkSZJ2pJ6E4+U/XOd726Jc21Y/Fs0g5Do8MGt34C08EUkPfE1+YJQzYL/YPhn4ndU2DLkeODa2e8a76U8Ye8TxIcBc4NNxv4fwjrcXvnbc1xoNRy6P7ReBZWJ7pcKzbRPbvYGlyt7J56JMr9i/CDiw8Fz/Uyg7B9eQl/ZnAjvE9qnAb2J7PHBRPX+TQYMGWZIkSdIyqNP8Y1EzV72LJ+joTGrJsb4R21fTqIcGuN3MLCRWL1vIoiQ9jney04H5NMqdrgFuju1NJP0CWAnvLMcU2nFgtOFjYG5pdFrGJDN7Pu43Pe73Jq7dvjsG3D1pzCA3Exgl6Vbg1jj2APBredazm0v1FdgZjzifHPUtB/wnzn0M3FRW/vpoTx/8o+C+OH4lvrbepFwlJB2GO3bRr1+/asWSJEmSVlJXhy33Qi4tdpcsLG+ofkWH0Bo51nyaSrPmU/1dlK4fSWV5Vr1UMvcQ8LiZbV2h/K543vHdgP+TtLGZnSHpDuCrwMOShpo7a5UQcKWZ/bhCfe/bwuvU9Zp6pKwrSZKkk6l3Dfsc4Nz4/QrY3sx+1G6tqo+2kmOV0wPYO7a/Xbi+ojwr2nFEtKGnpBUpk2PV4G9AX0lbx/W9JG0szy63lpmNA04kRvWS1jWzWWZ2Ji6r27CsvnuAvSV9MupbWVIlm84mmGdee0PSdnHoANwGNEmSJOki1Dsl/tXyDlrSmZ3ZaVsbybEq8A6wsaSp+LpzKRvZQvKsiAYfj1tyfgdfv55kZntLekDuH34nbghS6Rk+lBupnB/T0ksBv8FlWNfEMeFR4W9KOk3SjvgI/Ymou1jfE3Kf8rHR6X+E6+frSSN7EHBJBNM9S8q6kiRJuhT1yrqmmeemLh6baWFEsTghaZ4VzEOaKTsEON7Mvhb7w/C860e1Xwtbj8rkWeX79V5XTsq6kiRJWo7aQtYl6YgI0Nog5D6l32w8KKrLIWkFSXeExOoxSfvG8TkhT3pInvt6i5BR/UPS4VFGwNJx3azCtQp5VpPjuHxru5BQHRfH1pB0l6RnJJ1VaNc8SadHux6WtFoc7yuXiU2O3zZxfIeod3pJwiVpdUkT4thjhSns4vNXk4k1kWfJZW6/lrtwnRnT57fG3/dhSSVXsOGSLpU0FkjdfZIkSWdRK4ScRonSH3ETidJv5XpC0DvjB+wFXFZ8BmuUMR0R2+fhHxyfwB3I/lO49m48Wns14F+4M1e140PwBCmlew3Dp5P7AMviU9FrWaOs6uuxfRbws9i+Ftg2tvsBT1oVCRdubflTa5SQfaLs2WvJxMZTkGfhQXSjgZ6xfwHw89jeCZge28Nx2dtyVd73Yfh6+pR+/fpZkiRJ0jJoC1mXeTDSXOBbABHMtCweANXbzP5V6/pOYhZwjqQz8c50YuHcbYUyvc2dx96W9L6klXBt9B/No6lfjtHoljWOv1Xh/vfEe0PSE/gHznPAh3gHCd4BfjG2hwIb+eAegBXlCVEWknBJmoyvz/fC9eXTy+69AdVlYrCwPKvowrUt/mGCmd0raZVYQwfX3L9X4VkzSjxJkqSDqCtKXNLXJT0DzMajh+dQFvDUVTCzp3Et8izcDevkwunmZF2iMtWOV6KSfAvgo/iSKj/eA9jazAbGb00ze9vMzgC+i2upH5a0oZlNwKVeLwBXa+GUsSWZWKmuTc1sl8L5Wm5dlZ7RKpRLkiRJOoF6ZV2/wL2WnzY3AtkZHwF2OSStAbxrZtfgcrQtmrmkyARgX7k8qy/eOU6qcbxe+VYtxhJ+49H+gfHvQhIuuUTrP2Z2GfCHCs9WUSZWZzsmEHK1CKZ71cwqzSAkSZIknUC9sq6PzOw1ST0k9TCzcTHl3BXZFDhb0nxc1nREC669Bc+UNgMfXZ5oZv+WVO34a8B/Jc3A14QXctCqg2OACyXNxP8eE4HvAcdqYQnXN4ETJH0EzCMyrJWw6jKxx4vlJC1Vtt8TX6u+ItrxLi7zoqyccGXB/EoPMuuFuS178iRJkqRu6pV1/RXYA4+KXgVPd7mlmX2hfZu3eCFPM7oWHgfw21j/RdI84NfAl/DAsvdivzfwKjDMzF6SdCge5LU08HfgADN7t+weK+ABZJviHfZwM/uzXHK2a9x7BTxf+M/xNe6BZraRpB/g+dcBRpjZb+Ra8zuBcfhHyx5mVlHXvczq69kHLz3TqneUJEmypKE6ZV31dtgr4J1ID3zatA8wysxea21DlyQkrWxmr0taDpiMm228JsmAfc3sTxFQdh+wu7m/9b7Al8zsEEmrlN65PK/5y2Z2Qdk9fgk8YWbXRCDdJGBzYB98aWOzaMMQPKHLJmY2W9IgfJbg8/h69iPA/viswbPAF8zs4VrPlx12kiRJy6m3w65rStzM3on10/XM7Ep5NqyerW3kEsgxkvaM7bVwA5XXaGrMUSvSu5oBSZFdgN0kHR/7y+JyMYC7rdEkBTwr2+zY3ha4xczeAZB0M26lehvwz2qdtQrmHz1X7NvsC0iSJEkWjXrNP0pTsSsD6wJrApfgwWdJHcSIdigeEf6upPF4ZwpNjTlqGYKMpHkDEgF7mdnfyu6/FS2PEq9UrglFWdcyq6+Xsq4kSZJ2ot4o8f8FtiF0x2b2DPDJ9mrUYkof4I3orDfEp54rUSvSu5oBSZExwNERIIakzets3wRgD0nLxxLInngAXJIkSdIFqLfD/sDMPiztRJRxjqYASYdX0ENX4i5gqYjCPg1YaIpZ0k/iPe+NpwudgXt0l4L7SgYkdwNPlV8fnIZnPJspNx85rZ7nMLNp+Ah+UtxjhJk9Ws+1JTZds0/zhZIkSZJFot6gs7OAN3EZ0dHAkXhg00/bt3ldG9VpmtGC+uo2Hilc09MW9rlu7po0/0iSJOkitGnQGXAS8B08e9j3gL8AIxa9eV2DkCzdhY8oN8dtLQ+MaetBVJZWjcfzdW8D3BZpROeZ2Tlx7lE801pf/APnx7jE6noz+1ncd39cf7103PtI4HRgOUnT8TXs/SqVM7OPK8jAFnh+S1oXuDDu/y5wqJk9JWkk8Ho85zRJbwNr4LniX5V0CHAx0AD8F/hB6O2H0VQOtlPr3nqSJEmyKDTn1tUPwMzmm9llZraPme0d24vLlPgGwKXmVqFvAUfGGvEFwN5mNgi4HO9QS6xkZjuY2bkV6vvQzLbHg/L+jK//bwIMi/zcn8NNObYxs4F4hPh+ZnYS8F6kFN2vWrm4xwrAY2a2lZnd3/T2XAocHe0+HriocG59YKiZ/TD2B+HysW9HOzGzTfHc8VdKKgXFbQ0cZGbZWSdJknQSzY2wbyXSX0q6ycz2av8mdTjPmVkpzeo1+Ij2LlpmolGkaDDyuJm9BCDpWVzKtS3eUU6OupfDE9GUs3ONckUZ2AIk9cbXu29Qo5nIMoUiRbMPaGrqsS3+kUKMyP+Jd/CwsByseM8Fsq5+/fpVKpIkSZK0Ac112EWpzzrt2ZBOpHymwKgtrYLaZhj1GIxcaWY/bqZdtcq9b5XXrXsAb8aIvBLtKutKt64kSZL2o7kocauyvTjRryShwqeC76d1JhrNcQ+wt9yqFEkrR1IagI9iOr65chUJs47ZkvaJayRpQJ3tKpp/rI8nW/lbzSuSJEmSDqO5DnuApLciQGmz2H5L0tuSFhcnpyeBg0JutTJwcTPSqqXi3KLyLp6be2zc825g9Th3KS7HGmVmTwA/q1KuFvsB34l2Pw7sXme7LgJ6SpqFT/kPM7MPmrkmSZIk6SDqknUtrkSU+Ggz26Q9rym7fghwvJl9rYXXtVi+VaOuRZV11WzDMquvZ6sf9Ju2aCIAc87Ytc3qSpIk6arUK+uqN3FK0sgZwLqSpks6G0DSCZImS5op6ZQ4tmXsLytpBUmPS9okrt8urj9O0jBJvytVLml0dOpImifpVEmPAFtLGiTpPklTJY2RtNCIW1JfSTdFeyZL2iaOD5d0qaSxwFVx3xsk3Y6P4iXpbEmPSZolNx1B0hBJ4yRdiwfSJUmSJJ1AvTrsxRIzm4NHg7eEk3CHq4EAknbBTTwG44Fbt0na3swmSLoNd8haDrjGzB6TdBKFEXbonKtRkm+drMouXqfTaIdZ4rfAeWZ2f8jyxgCfi3ODgG3N7L2479Y0unftBQwEBgCr4tHpE+K6wfHMsylDaf6RJEnSISzRHXYbsUv8Smk8e+Md+ATcc3oy8D4uF2sp9bp4FRkKbFSQda0oT+4CTWVc0FSutS3wx5jyflnSfcCWuDZ9UqXOGtL8I0mSpKPIDrv1CPiVmf2+wrmV8Q68F54prJI86r80XZpYtrBdr4tXkR64I1ixYyY68DaXdSVJkiQdQ3bYLedt3DWrxBjgtIjsnidpTeAjM/sPPvL8P2Bt4EzgqOL1sVZ9hG9qd9zBa3CV+y6QmpnZQzFFvr6ZPV5Wbmzcp7S+PtDMptfxXBOA70m6Ev/Q2B44AdiwjmsBN/+YkoFiSZIk7UJ22C3EzF6T9IDcCetOMzsh0og+FKPYecABkr4M/NfMrpXUE3hQ0k64ZeV/Q3Y1EXgDmAv8EngMmFblvh9K2hs4X1If/G/3G1y6VeQY4MKQgi2Fd8SHFwvI3dbK92/B17Rn4Jr7E83s33Ir0AXl2tLsJEmSJKmf7LAXgci9XeTPeKc4Du/0Pop/+0l6HLjRzLYCiI58TdxQZH7Ut18EgTWY2b6SRkpateTcpUYXr5dLTYhfJYvNzwCfija8BJwSx4fgHw334elTNwVelzQO/0g4HV97n49rxZ8sXPdiRJe/CpQ/e5IkSdIBZIfddmwAHGxmRwJI+mlEX/cE7pG0Ge4GdhnuePV3auckr8S3gTFmdnrUu3zxpBpNS6pFkq9kZjtE2ZE0moF8LOkC4FEz2yNmAq7Co8ahEF3ewvYmSZIkbUR22G3HP83s4cL+/4TkaSk8Q9lGeEDYbDN7BkDSNYQkqk4mA5dHx3xrhbXp5iLJyz8QimYg2wJ7AZjZvXJnsT5xrjy6fAFK848kSZIOIROntB0LIqklrY1bW+4ctp130Bj9XY/0aUHkuLznXRrAzCbgwWAvAFdLOrDsulIk+cD4bWpmu1RqY4X9SlHiVqFc0wJml5pZg5k19O2bOuwkSZL2Ijvs9mFFvJObK2k14Ctx/ClgbUnrxv63qlw/B5+GBs8F3gtAbv7xHzO7DPgDYX1aoDWmJUXzjyHAq2EmkiRJknQBFssOW9KGkfrz0ULnuKh1DZT01TqKriBpNICZzcATqTwOXA48EMffB97E17TvxyVclZYlLgN2kDQJ2IrGEe4QYLqkR/Hp698WL2rGtKT4TKeysJHIcKAhosvPAA6q45mTJEmSDmKxNP+I9J/LmdnPy44Lf+b5LahrGB69fVQz5YZQh6mHpPFRbkq9beguNDQ02JQpi91jJUmStCvqTPMPSf0lPSVpRJhJjJI0NPTLz0gaHOVWkHS53KTi0UgeUrp+oqRp8ftCHB8iabykG6P+USrk4IwyXwWOBb4bphX9JT0p6SJcvrSWpIslTZEbcpxSuHZLSQ9KmiFpUgRdnQrsGyP2fSUNjjKPxr8bNPMulpN0ndwI5Ho8r3jp3BxJq7bB+xom6WZJd0X5s+J4z5CIlQw9jovjI+WabiTtHHXNirqXKbTtlHj/s1TQYydJkiSdgJm1+Q/ojwdObYp/FEzFp4aFr8neGuV+Cewf2yvhsqcVcLnSsnF8PWBKbA/Bk4x8Oup9CJcbld9/OD6KLbVlPvD5wvmV49+ewHhgMzyw61lgyzi3Ij5dPQz4XeHaFYGlYnsocFOhbaMrtOUHwOWxvVm8l4bYn4MbbbT2fQ2LtvfBg9v+CayFr4PfXWjLSvHvSHzqfFngOTxjGriU69hC246O7SOBEc393QcNGmRJkiRJyyj1cc392nMNe7aZzTKffn4cuCcaNgvvoMBNM06SNB3vOJcF+uFBVpdJmgXcgEuiSkwys+ej3umFumpRSXI1DV9n3jjq3wB4ycwmA5jZW1Y5q1cf4AZ5prPz4vpabA9cE3XOBGZWKdea90WUn2u+Tv4EnkDlWWAdSRfIE7aUB5FtEPd9OvavjPaWuDn+nUqV9yzpsJitmPLKK69UfwtJkiRJq2hPHfYHhe35hf35hfsK2MvM/la8UNJwPKvXAHzE+X6Vej+mvmeoJLna0szekCcQWTbaUs+C/mnAODPbU1J/vONsjnrqbc372ooK7yWebwDwJeB/gf+hqR1nLcOPYpuqvmcruHU1NDQsfgERSZIkXYTOjhIfAxxdWoeWtHkc74OPducDB+BT121FLcnVGpK2jLZ8Qp5ju9zsow+ugwafim6OolxqE3xafFGp9r4qImlVoIeZ3YSbkJTLwJ4C+kv6bOwfgHtuJ0mSJF2Mzu6wT8Onv2fGFPNpcfwi4CBJD+PpM9vM3tGqS64+BPYFLpBLou7GR97jcH/p6fJUn2cBv5L0APV9SFwM9JbLpU4EJrWi+dXeVzXWBMbHFPpI4MeFc32BbwAH41P8s/DR/CWtaF+SJEnSTiyWsq6keVRDhqZFdOVKWVeSJEnLUWfKuroibSCdWmSpWZQ7RtITIe+6TlKPuG/fON9D0t9D5jVSLj0bJ+lZSTtEm56MNfdSnfMknSlpqqS/yiVn4+Oa3aJMT0lnx/PMlPS9uPwMYLuYOTgupGE3SLodGCvp6tKzRz2jSnUmSZIknUA9oeSLw4/Ol5q9CCxTqjf+/TmNMqpdaJSIjQSuK7TtrbJ2D4xyBnwltm8BxuJT5gOA6XH8MOBnsb0MMAVYmzIZGr4e/zyNkrcdCu+kDzCbkLOVPddhUeeUfv36WZIkSdIy6AKyrq7IbOs8qdlMYJSk/fEPB/APhpKBxyHAFYXytxfa9nJZu0v1fwjcFduzgPvM7KMKz3NgPM8jwCr4B0cl7jaz1wHM7D7gs5I+iec8v8kqTJNbmn8kSZJ0CEuavWZnSs12xTXOuwH/J2ljM3tO0sty/+mtiGjysjqL7Sxv60fRqTcpZ2bzI8K99DxHm9mYsucZUqGN5cF9V0ebvklTOViSJEnSwSxpI+x6aHOpmaQewFpmNg6PFF8J6B2nR+CJVf5kjd7UbckY4AhJf5C0kaT1Ja3AwnI1cCex4szBSDzNK2b2eDu0LUmSJKmT7LAXpj2kZj2Ba2I6/VHgPDN7M87dhnfeV1S7uJWMwDOfDQb+BPweH6HPBP4rz5t+XJRdh8JUv5m9DDzZjm1LkiRJ6qRLdNgdEcGNr/XOKI2czWyYmd0YTVgK+Hforx/AtdabAXfiiUVmAVuY2WbAScDWwF2SngIOBb4e99sST05ynNw85BPybGj34kFrHwGHmdkZkq6XG5UMAGbg6+Z7SeoJvAL8SK7d/pKZbVJ6V2Y2DJgS974pIr9vBM4ys3MUZh7AbEmXA73M7CfAa8AwM9sRT/wyHM9j/h5wLR5ctxpwdkSOryvph8BOwFGSrmvdXzlJkiRpFfVEprX3j86P4H4E2DO2l4369sKTp/TEO7J/4R7SFeukunlItbbtGc/5z6jzOdzJq2JUd4X3ZcA2sX85nm61lpnHeBpNRwz4emyfVbjfSGDv2B4af5Mflt53c3/HNP9IkiRpOXTDKPHZ1gkR3JI+AaxpZrcAmNn7ZvYu3gn/0cw+Np8avg/Yskad1cxDqrXtTuCT+PR6H2CCmb1H/VHdz5nZA7F9TbS3OTOPEh8Co2O7orGHmf0V+CuwdVlkexOU5h9JkiQdQleKEu+sCO5qBhi1jDEq1VnNPOS4Sm0zs/cljceNOfYF/li470JR3RUov5c10+YixejyWgYqlSLbm3TcluYfSZIkHUJXGmHXQ5tHcJvZW8DzkvaIOpeRtDxu2rFvZArri3dctfKAVzMPqdW26/Bc3tvFs5We8QhJvaKeUlR3Of0kbR3b3wLup/VmHgsix5uJbE+SJEk6mO7WYbeXWcgBwDER5PUg8Ck8c9hMPCDsXuBEM/t3tQqsunlIrbaNxT8E/hrXQ2NU97R4xlJUdzlPRr0zgZWBi829sOs285DUQOirQ5f9GHBCBK2tR/XI9iRJkqSDSfOPbkhEno+2QvR4G9Q5HJhnZucsah1p/pEkSdJylOYflekICZlqm4B8Vm7UMSOuX1fO2dGeWXIbz+bqXE7Sg1HPAglZlbaVJGSlNowMCdkQSaPjA+BwXI42XdJ2kmYXpuVXlDSntJ8kSZJ0PF0p6Kwj+SywDy6hmgx8G4+y3g34CbAH8FPgXjM7RNJKwCRJfwX+A3wxgsbWw4PFSl9GmwMb40YfDwDb4GvLRUYBZ5jZLZKWxT+avgEMxAPTVgUmS5pQo85JeIDZ981ssqQVcT11tbZdh0/X/0XS0sDOwBF4OlTMbI6kSyiMsCMgblfgVjw16U3mecqbIOmweI/069evjlefJEmSLApL3Ag7WBIlZDtJWgb4Co0SslqMwNfDiX8rZjuzNP9IkiTpEJbUEXZKyJrBzB6IKfYdgJ5m9lhz1yRJkiTtx5I6wq6H7iYhOwRYo0bbKknIilQyA7kK79wzl3iSJEknkx12dbqbhOxgfOq7JRKyIrcDe5aCzuLYKOD/UceIPEmSJGlfutWUeEQz34UHcn0e7+CuAE7B03zuZ2aTItHIBXhu8qWA4Wb257j+auBDSdOAo8xsWCkaG3gVWErSKDxn+ffK7v9Z4GJ8OnppfJ33J5LOxteGTX5aff0AABRSSURBVNK+ZnY9cCNwSkRnDwRuxtfIv4/nDN/DzP4haSQ+db1x1PkDMytFbp8GrFBo61HRjhPxjn8+3klPwfOAn4EHn+2I67TPjWt7AfuY2Sql6Pfiu4nH6xXtWBq4UNJeuMPXa8B9clOS0+LZkiRJko6mnoTjXeVH9zMJeTO2l8Edsk6Ja78P/MYaDTfuivuuBzxfqLtSW7+Cj8yXj/2VrczcI/bn4ClOAY4ERjTzbi7AP3jAO+2LgJeA6wt19qn190nzjyRJkpZDNzT/qJfZ1n0ivCeb2Utm9gHwD3xamrK2AvzJzOab2TO449eGNdo6FLgi7o2ZvV7jXd0c/xYNPqq9m4fw2YIfAZ8xsyOBHYDBks6UtJ2ZzS2/gdL8I0mSpEPoVlPiQXeN8K7WVqhs5FEx4pvqEeK17l98norvBnhS0iO49nqMpO+a2b2SBgFfBX4laayZndqkoWn+kSRJ0iF0xxF2PXTlCO9K7COph6R1gXWAv9Vo61jgkLg3klaO45WivCtR8d1IWgd41szOB24DNpO0BvCumV0DnANs0cLnSpIkSdqIxbXD7rIR3lX4Gz6VfidwuLmJx0JtlXQs/oFwGzBF0sfA8VHHSOCSiPJersa9qr2bfYHHYqp8Q1zStSme4W06nvntFy18riRJkqSNSPOPTiaixEeb2Y11lJ2DB5a9GvvzzKzLWF6m+UeSJEnL0ZJi/qHON/M4RtITkmZKui6ODZd0paSxctOMb0g6S27scZcaTTV2Br6Oy68ul6cORdLO0cZZpeOSjsETo4yTNK5w/9PlBiAPS1otjo2UdL7cHORZSXsXyp8Q72CmpFMK7+aOqOcxNZqPnFF4tkV28UqSJElaT7fvsIPPAr8FNsOnc0tmHsfjZh7QaOaxJa5TPluu1y4ZZmyBTwufX6h3c+BYPEJ7Hdx4o5yTgM3NbDPc8arEungA1+7ANcA4M9sU10nvKjf+GAlsbWafwYPCjigc3zfKLwUcEWvLLwI7mtmOcY8VgIfNbAA+VX5o4f6rxzv4Gq7PRtIuuERsMK4NHyRpe+DLwItmNsDcsvOuWBvfE9g4ni2nw5MkSTqRxaXD7hSpVzATGCVpf1wjXuJOc3erWXjA2F1xvNSmDaLdT8fxK/GAtWrHK/EhMDq2i9ItcE36fDN7AteHl97BLsCjwDT842a9aNPQMvnWW3hk+ghJ3wDerdSAlHUlSZJ0DItLh90SqdfA+PUzsydpKp9qwJOGVKq3ktQLfBR9ITAImCqpVOYD+P/t3XmwXGWZx/HvLwkkDoQkAlIMW9hiQIhg2AIOhkULRwyBCYqFBUEWgQFmWLUKR0GHTSzWIMgwFIoKMaRKAsgWDGDYERIyYQsSGLYC2RJgECE888f73txDp/v26Zt7e0l+n6que/r02+c8fXLT7z3nvM/7QO7sP4zuwQJdMfUmTaxScbuV8RVjV+HnWYVjsFlE/Hf+42AsqeM+S9IPI1X/2gGYTio3egtVhKt1mZk1xYrSYZfR56lekgYAG0TELOAU0sxhZQeBPQmMVJrulLzvu3pYD+VTt2q5lZQStnqOfz1Jn6mWvpXbDIuIP5BuC2yzHPs1M7Pl1IkTp/TWT4ALSOlMIk3duTcpfWq6pP2BWTSW6jUQ+LWkYaSz1/Mj4u0qY9OWEank5SHAtHxW/hBwWUR8UG19ftvlwM2SXincxy4tIm6TtAVwX47xXeDbpDEA50r6GPgQOIr0h8H1+Z66SFcizMysRZzWtRKTNChf+u4TTusyM2vcSpPW1a7aIN3s8LzNuZKmq3tmtKsknZdTw85pdP9mZtYaPsPuJ0rlMZ8hpYbNJ13angscCkwADomIiZLOBB6PiF9LGk6a1nRb0nzhH+dL55uTCoxsJ2k8cD2pHOfLwD3AyRExu2L/a0bEG3n5P4FXI+JipYla1gL2iYglje6/yuc8AjgCYMMNNxz7/PPP98nxMzNbWZQ9w16Z7mG3wsKImAcgaWm6WU4hG5nbfAWYIKlritGudLOXgSmStiGNAB9V2O6DEfFi3m5XutknOmxgq9xRdw2Eu7Xw2rSIWLIc+1/KxT/MzJrDHXb/alVlMUiTr0yMiLmSJpPqc3cpDqzrzf7NzKzJfA+79Uqnm+X0q9NLbnco8IrSNKgHVr4oaaKkLRvZf68+nZmZ9Ql32K1XurJYRLwM/Kjkdv8DeAC4nZTbXWkiaVa30vsvTApjZmZNtlJ12M0cuQ08n+flBiAiJgPHSLoA+G1+3w4R8T5wAmlQ2vvAepL2iYgFwHnAC8BWpDKXI4EpEbG3pMmSfg9sBPxI0jGSTsjx3g9MjYiNSYPcNge2lvQn0rziL5MGvp0L3Af8lNSBvwism9sNzHOIPwkMzvGd0+f/KGZmVsrKeMa0GbA/aWTzQ3QXCplAKhQyke5CId/pGjktaSbdhUKWjpwmTWcKaWR1ceT2Liw7EAxgtYjYWanoxpWkzrjW/gDGAWMi4s3cYRdtlfc7hDQi/XsRsa2k84GDSBPFXE6qsb1A0o7AzyNid0kzKJT1lHRHZTtg97yfUcCehYFqZmbWZCtjh93KkduQOnki4m5Ja+QOutb+AG6PiDdrfJZZEfEO8I6kRcANef08YIzS9KI7k2ZN63rP4MqNlGg3rVZnXZHWVSNMMzNbXitjh93KkduQ8psrn9fa3470PFVqvc8yAHg7IurNA16vXc0YnNZlZtYcK9U97Ab058jpb+ZtfhFYlEtZ1trfcomIxcBCpXnSUfL5/PLSQiJ12pmZWRtwh11d6ZHTvdj2W5LuJRX0OLTO/gBG53Su3poJHC5pLmnGtX3y+muBk/MgtU1JqV+HVmlnZmZtwFOTNpGkO4GTIqJ0hYzevKfi/c8B20XE6w28p1dFQVz8w8yscWWnJvUZdh19mQoGjAXG5PV1i3hImkQahf4bSXMkfUrSWEl3SfqzpFslrStpUN7v+Py+sySdIek44B+BWUrFPpD0bnH7SnOLly4KYmZmreEOu5zNgAtJne1oulPBTiKlgkF3atb2wG6k+tKr0Z0K9gXgC8DRhe1uC/w7aQKTTUipYEvllKuHgQPzgLCPgIuBSRExlpQWdkY+G54MXCrpy8BewOkRcRFpZPtuJetnd6VvndjD5zEzsxZYGUeJ90arU8G6fJaUe317PhkfCLwCEBHzJV1NSu0aFxF/78XnLFMU5IniG5zWZWbWHO6wy2l1KtjSzQHzI2Jcjde3Bt4G1ulhG8VBC0MqXqtbFGSZjTmty8ysKXxJvO/0VyrY0vQr4ClgbUnj8j5WkfS5vLwfsCawK3BRnpCl8v0Ar0raQtIAYN9efB4zM2sBd9h9p79Swa4CLsuXzAcCk0iDwuYCc4CdJa1FmiP80Ih4GphCuucO6ez3Zkmz8ojxM4AbgbfIl9Mb/DxmZtYCTuvqcI2kYPUmxasRTusyM2uc07r6UV+meqlO1a/KVK/c7k5JZ0q6C/g3SV+X9EDex0xJ6+R2a0q6La//Bem+dNc23i3s88bC+imSJuflsyU9LukxST/rtwNqZmZ1edBZ77W66tfwiPgSgKQRwE555PphwCnAiaTa2bMj4seSvpZjLUXSp0n3uEfn7Q6v0c6jxM3MmsAddu+1OtVramF5fWCqpHWBVYGFef2uwH4AEXGTpLca+HyLSSPar5B0E+m+9zI8StzMrDl8Sbz3Gkn12iY/NoyIJ4Dj6U712o7UyVbbbk+pXsXBaxcDUyJia+C7fDJdq14n+hGf/D0YApDvi+8ATCddLbilznbMzKwfucPuX/1Z9atoGPBSXj64sP5uUlEPJH0VGFHlvc8DW0oaLGkYsEduvzowLCL+QJqNrV6JTjMz60e+JN6/fgJcQEqNEvAcsDcp1Wu6UjnLWfSu6lfRacA0SS8B9wMb5/WnA9cozWN+F/C/lW+MiBck/Q54DFgAPJpfGgpcL2kI6UrB8csZo5mZLQendVmfcVqXmVnjnNZlZma2AnGHbWZm1gHcYZuZmXUAd9hmZmYdwB22mZlZB3CHbWZm1gHcYZuZmXUA52Fbn5H0DvBUq+MoaS2gX8qM9pNOireTYoXOireTYoXOireVsW4UEWvXa+SZzqwvPVUm+b8dSHq4U2KFzoq3k2KFzoq3k2KFzoq3E2L1JXEzM7MO4A7bzMysA7jDtr50easDaEAnxQqdFW8nxQqdFW8nxQqdFW/bx+pBZ2ZmZh3AZ9hmZmYdwB22NUzSXpKekvSMpO9XeX2wpKn59QckjWx+lEtjqRfrrpIekfSRpEmtiLEQS71YT5D0uKTHJN0haaNWxFmIp168R0qaJ2mOpNmStmxFnDmWHmMttJskKSS1dLRwiWM7WdJf87GdI+mwVsSZY6l7bCV9I//uzpf022bHWBFLvWN7fuG4Pi3p7VbEWVVE+OFH6QcwEPgLsAmwKjAX2LKizdHAZXn5AGBqG8c6EhgD/AqY1ObHdTfgH/LyUa06rg3Eu0ZheQJwS7vGmtsNBe4G7ge2a/NjOxmY0qoYG4x1c+BRYER+/pl2jrei/bHAla0+zl0Pn2Fbo3YAnomIZyPi78C1wD4VbfYBfpmXrwP2kKQmxtilbqwR8VxEPAZ83IL4isrEOisi/i8/vR9Yv8kxFpWJd3Hh6WpAqwbMlPmdBfgJ8FPgb80Mroqy8baDMrEeDlwSEW8BRMRrTY6xqNFj+y3gmqZEVoI7bGvUesALhecv5nVV20TER8AiYM2mRFcjjqxarO2i0VgPBW7u14h6VipeSf8q6S+kjvC4JsVWqW6skrYFNoiIG5sZWA1lfxf+Jd8euU7SBs0JbRllYh0FjJJ0j6T7Je3VtOiWVfr/Wb7ltDHwxybEVYo7bGtUtTPlyjOnMm2aoV3iKKN0rJK+DWwHnNuvEfWsVLwRcUlEbAp8D/hBv0dVXY+xShoAnA+c2LSIelbm2N4AjIyIMcBMuq9oNVuZWAeRLouPJ52xXiFpeD/HVUsj3wkHANdFxJJ+jKch7rCtUS8Cxb/m1wdertVG0iBgGPBmU6KrEUdWLdZ2USpWSXsCpwITIuKDJsVWTaPH9lpgYr9GVFu9WIcCWwF3SnoO2AmY0cKBZ3WPbUS8Ufj3/y9gbJNiq1T2++D6iPgwIhaS6g1s3qT4KjXye3sAbXQ5HNxhW+MeAjaXtLGkVUm/1DMq2swADs7Lk4A/Rh7B0WRlYm0XdWPNl21/QeqsW3kfEMrFW/xS/hqwoInxFfUYa0Qsioi1ImJkRIwkjQ+YEBEPtybcUsd23cLTCcATTYyvqMz/sd+TBkwiaS3SJfJnmxplt1LfCZI+C4wA7mtyfD1r9ag3PzrvAfwz8DRptOWped2PSV9yAEOAacAzwIPAJm0c6/akv7rfA94A5rdxrDOBV4E5+TGjzX8PLgTm51hnAZ9r11gr2t5JC0eJlzy2Z+VjOzcf29FtHKuA84DHgXnAAe18bPPz04CzWxlntYdnOjMzM+sAviRuZmbWAdxhm5mZdQB32GZmZh3AHbaZmVkHcIdtZmbWAdxhm1lVkpYUqhbN6U3VNUnDJR3d99Et3f5kSVP6a/uNkDRe0s6tjsNWXINaHYCZta33I2Kb5dzGcFL1tp838iZJA6ONpoSsJ8/oNx54F7i3tdHYispn2GZWmqSBks6V9FAuPPHdvH71XKP7kVwDu6sC0tnApvkM/dx8FnpjYXtTJE3Oy89J+qGk2cD+kjaVdIukP0v6k6TRdWK7StKlkmZJelbSlyRdKekJSVcV2l0q6eFcm/n0wvrnJJ0j6cH82Cyv3yh/tq465BsW9neepFnAVOBI4Pj8Wf9J0teV6sE/KmmmpHXy+07Lcd2Z4zyuEMNBeT9zJV2d160taXo+5g9J2qW3/37W2XyGbWa1fErSnLy8MCL2JVUJWxQR20saDNwj6TZSBaR9I2Jxnn7yfkkzgO8DW3WdqUsaX2eff4uIL+a2dwBHRsQCSTuSztJ3r/P+EbnNBFKBjF2Aw4CHJG0TEXNIs1u9KWkgcIekMZFKrAIsjogdJB0EXADsDUwBfhURv5T0HeAiuudFHwXsGRFLJJ0GvBsRP8vxjwB2ioiQdBhwCt0FRkaTpuscCjwl6dK8rVOBXSLidUmfzm0vBM6PiNn5j4VbgS3qHAdbAbnDNrNaql0S/wowRtKk/HwYqZDDi8CZknYl1RZfD1inF/ucCumMHdgZmKbuUuqDS7z/htxBzgNejYh5eXvzgZGkaVK/IekI0vffusCWQFeHfU3h5/l5eRywX16+mlQqtMu0Hi7drw9MzfN+rwosLLx2U6TiHR9Ieo10rHYnVYd6HSAiugrm7AlsWTgOa0gaGhHvlDgetgJxh21mjRBwbETc+omV6bL22sDYiPhQqerVkCrv/4hP3oqrbPNe/jkAeLsX99C7Klh9XFjuej5I0sbAScD2EfFWvlRejCFqLFNj/Xs12gBcDJwXETPylYXTqsQJsIT0Xawa+xwAjIuI93vYl60EfA/bzBpxK3CUpFUAJI2StBrpTPu13FnvBmyU279Duuzb5XnS2eJgScOAPartJCIWAwsl7Z/3I0mf74P41yB1sovyPeWvVrz+zcLPrkpN95KqOgEcCMyuse3KzzoMeCkvH7xs82XcQTr7XxOgcEn8NuCYrkaSlncgoHUon2GbWSOuIF1afkTpGu1fSfdzfwPcIOlh0mXnJyHVbZZ0j6T/AW6OiJMl/Y50CXoB8GgP+zoQuFTSD4BVSDW15y5P8BExV9KjpEpXzwL3VDQZLOkB0snMt/K644ArJZ2cP+8hNTZ/A3BdHnB3LOmMepqkl0glOzeuE9t8SWcAd0laQjo2k/P+L5H0GOk7+27SADdbybhal5kZaZQ4qazm662OxawaXxI3MzPrAD7DNjMz6wA+wzYzM+sA7rDNzMw6gDtsMzOzDuAO28zMrAO4wzYzM+sA7rDNzMw6wP8Dnb/4bT6x9FsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importances_cancer_data(cancer_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importances_cancer_data(cancer_tree1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importances:\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.04903686\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.78565282 0.03461496 0.         0.\n",
      " 0.         0.         0.         0.13069536 0.         0.        ]\n"
     ]
    }
   ],
   "source": [
    "print('Feature importances:\\n{}'.format(cancer_tree1.feature_importances_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}