Machine Learning para Business Intelligence/Clase 6/Scripts/6. Clasificación.ipynb

{
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  {
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   "source": [
    "# **Machine Learning para Business Intelligence** \n",
    "#### Profesor: Lucas Gómez Tobón"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clase 6: Modelos de clasificación "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Árboles y Random Forest"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los árboles de decisión son formas extremadamente intuitivas para clasificar o etiquetar objetos: simplemente hace una serie de preguntas diseñadas para concentrarse en la regresión o clasificación.\n",
    "Por ejemplo, si desea construir un árbol de decisión para clasificar una casa, puede construir el que se muestra aquí:\n",
    "\n",
    "<center>\n",
    "<img src=\"https://raw.githubusercontent.com/RodrigoLaraMolina/DPATTSrc/master/decision-tree-house.png\" alt=\"decision_tree\" style=\"width: 700px;\" align=\"center\" frameborder=\"200\"/>\n",
    "</center>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "<img src=\"img/regression_trees.png\" width=\"800\" class=\"center\">\n",
    "</center>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En esta clase vamos a ver los modelos de clasificación relacionados con árboles de clasificación. \n",
    "\n",
    "Cubriremos los siguientes temas:\n",
    "- Selección de hiper-parámetros.\n",
    "- Interpretación de los árboles.\n",
    "- Importancia de variables.\n",
    "- Tipos de ensambles: simulatáneos y secuenciales.\n",
    "- Regularización de árboles.\n",
    "- Evaluación de los modelos.\n",
    "\n",
    "Estudiaremos los siguientes tres modelos:\n",
    "- Árboles de clasificación\n",
    "- Random Forest (Bosques aleatorios)\n",
    "- Gradient Boosting Machine"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Contraceptive Method Choice Data Set\n",
    "Esta base es una muestra de una encuesta sobre anticonceptivos en 1987 en Indonesia. Las muestras son mujeres casadas que podían estar o no embarazadas y no lo sabían al momento de la entrevista. __El problema de predicción será predecir si el método anticonceptivo actual es:__\n",
    "\n",
    "0. No se usa\n",
    "1. Largo plazo\n",
    "2. Corto plazo\n",
    "\n",
    "Para ver una descripción más detallada vea: https://archive.ics.uci.edu/ml/datasets/Contraceptive+Method+Choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Wife's age (numerical)\n",
    "# 2. Wife's education (categorical) 1=low, 2, 3, 4=high\n",
    "# 3. Husband's education (categorical) 1=low, 2, 3, 4=high\n",
    "# 4. Number of children ever born (numerical)\n",
    "# 5. Wife's religion (binary) 0=Non-Islam, 1=Islam\n",
    "# 6. Wife's now working? (binary) 0=Yes, 1=No\n",
    "# 7. Husband's occupation (categorical) 1, 2, 3, 4\n",
    "# 8. Standard-of-living index (categorical) 1=low, 2, 3, 4=high\n",
    "# 9. Media exposure (binary) 0=Good, 1=Not good\n",
    "# 10. Contraceptive method used (class attribute) 1=No-use, 2=Long-term, 3=Short-term"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Cargamos los datos\n",
    "data = pd.read_csv('../Data/cmc.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>AgeWife</th>\n",
       "      <th>EducWife</th>\n",
       "      <th>EducHusband</th>\n",
       "      <th>children</th>\n",
       "      <th>IslamWife</th>\n",
       "      <th>WorkingWife</th>\n",
       "      <th>OccupationHusband</th>\n",
       "      <th>StandardLivingIndex</th>\n",
       "      <th>MediaExposure</th>\n",
       "      <th>Contraceptive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>24</td>\n",
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       "      <td>3</td>\n",
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       "      <th>2</th>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
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       "      <th>3</th>\n",
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       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
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       "      <th>5</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>8</th>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "   AgeWife  EducWife  EducHusband  children  IslamWife  WorkingWife  \\\n",
       "0       24         2            3         3          1            1   \n",
       "1       45         1            3        10          1            1   \n",
       "2       43         2            3         7          1            1   \n",
       "3       42         3            2         9          1            1   \n",
       "4       36         3            3         8          1            1   \n",
       "5       19         4            4         0          1            1   \n",
       "6       38         2            3         6          1            1   \n",
       "7       21         3            3         1          1            0   \n",
       "8       27         2            3         3          1            1   \n",
       "9       45         1            1         8          1            1   \n",
       "\n",
       "   OccupationHusband  StandardLivingIndex  MediaExposure  Contraceptive  \n",
       "0                  2                    3              0              1  \n",
       "1                  3                    4              0              1  \n",
       "2                  3                    4              0              1  \n",
       "3                  3                    3              0              1  \n",
       "4                  3                    2              0              1  \n",
       "5                  3                    3              0              1  \n",
       "6                  3                    2              0              1  \n",
       "7                  3                    2              0              1  \n",
       "8                  3                    4              0              1  \n",
       "9                  2                    2              1              1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Veo las primeras diez filas\n",
    "data.head(10) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1473, 10)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Estudio el tamaño de la base de datos ¿Cuántas variables hay? ¿Cuántas observaciones?\n",
    "data.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 1, 4], dtype=int64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.OccupationHusband.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgeWife                32.538357\n",
       "EducWife                2.958588\n",
       "EducHusband             3.429735\n",
       "children                3.261371\n",
       "IslamWife               0.850645\n",
       "WorkingWife             0.749491\n",
       "OccupationHusband       2.137814\n",
       "StandardLivingIndex     3.133741\n",
       "MediaExposure           0.073999\n",
       "Contraceptive           1.919891\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Veo las medias, que notan sobre el tipo de variables? Tiene sentido estudiar la media de TODAS las variables?\n",
    "data.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dummyficamos\n",
    "data = pd.get_dummies(data, columns = ['OccupationHusband', 'EducWife', 'EducHusband'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.427020\n",
       "3    0.346911\n",
       "2    0.226069\n",
       "Name: Contraceptive, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Estudiemos el desbalance de las clases\n",
    "data.Contraceptive.value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize = (20,6)) \n",
    "# Histograma de edad de la esposa para los Contraceptive == 1\n",
    "plt.hist(data.AgeWife.loc[data.Contraceptive == 1], alpha = 0.35, label = 'No-use', bins = 15) \n",
    "# Histograma de edad de la esposa para los Contraceptive==2\n",
    "plt.hist(data.AgeWife.loc[data.Contraceptive == 2], alpha = 0.35, label = 'Long-term', bins = 15) \n",
    "# Histograma de edad de la esposa para los Contraceptive==3\n",
    "plt.hist(data.AgeWife.loc[data.Contraceptive == 3], alpha = 0.35, label = 'Short-term', bins = 15) \n",
    "# Que aparezca la leyenda\n",
    "plt.legend(); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,6)) \n",
    "# Histograma de número de hijos para los Contraceptive==1\n",
    "plt.hist(data.children.loc[data.Contraceptive == 1], alpha = 0.35, label = 'No-use', bins = 15) \n",
    "# Histograma de número de hijos para los Contraceptive==2\n",
    "plt.hist(data.children.loc[data.Contraceptive == 2], alpha = 0.35, label = 'Long-term', bins = 15) \n",
    "# Histograma de número de hijos para los Contraceptive==3\n",
    "plt.hist(data.children.loc[data.Contraceptive == 3], alpha = 0.35, label = 'Short-term', bins = 15) \n",
    "plt.legend(); # Que aparezca la leyenda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Necesitamos separar la muestra en entrenamiento y testeo\n",
    "X = data.drop(columns = 'Contraceptive')\n",
    "y = data['Contraceptive']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 23)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El tamaño de la muestra de entrenamiento es  (1178, 18)\n",
      "El tamaño de la muestra de prueba es  (295, 18)\n",
      "El tamaño del vector de marca de entrenamiento es  (1178,)\n",
      "El tamaño del vector de marca de prueba es  (295,)\n"
     ]
    }
   ],
   "source": [
    "print('El tamaño de la muestra de entrenamiento es ', X_train.shape)\n",
    "print('El tamaño de la muestra de prueba es ', X_test.shape)\n",
    "print('El tamaño del vector de marca de entrenamiento es ', y_train.shape)\n",
    "print('El tamaño del vector de marca de prueba es ', y_test.shape)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Entrenamiento del Árbol"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "<img src=\"img/tree.png\" width = 600>\n",
    "</center>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hiperparámetros del Árbol**\n",
    "- Profundidad.\n",
    "- Mínimo de observaciones para separar.\n",
    "- Mínimo de observaciones en las hojas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importe de scikit-learn el módulo tree, el cual contiene árboles de clasificación\n",
    "from sklearn import tree "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=12, random_state=23)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Construyo el objeto que es el árbol de clasificación\n",
    "arbol = tree.DecisionTreeClassifier(max_depth = 12, random_state = 23) \n",
    "# Estimo el modelo\n",
    "arbol.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicción dentro y fuera de muestra\n",
    "y_hat_in_tree = arbol.predict(X_train)\n",
    "y_hat_out_tree = arbol.predict(X_test)\n",
    "\n",
    "# Predecimos las probabilidades\n",
    "y_prob_in_tree = arbol.predict_proba(X_train)\n",
    "y_prob_out_tree = arbol.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96.8\n",
      "96.8\n",
      "95.3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contraceptive</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>444</td>\n",
       "      <td>16</td>\n",
       "      <td>40</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29</td>\n",
       "      <td>220</td>\n",
       "      <td>27</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46</td>\n",
       "      <td>38</td>\n",
       "      <td>318</td>\n",
       "      <td>402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>519</td>\n",
       "      <td>274</td>\n",
       "      <td>385</td>\n",
       "      <td>1178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0            1    2    3   All\n",
       "Contraceptive                     \n",
       "1              444   16   40   500\n",
       "2               29  220   27   276\n",
       "3               46   38  318   402\n",
       "All            519  274  385  1178"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluemos nuestro modelo\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score\n",
    "import numpy as np\n",
    "\n",
    "# Que tanto aprendió dentro de la muestra?\n",
    "print(np.round(100*roc_auc_score(y_train==1 , y_prob_in_tree[:,0]), 1))\n",
    "print(np.round(100*roc_auc_score(y_train==2 , y_prob_in_tree[:,1]), 1))\n",
    "print(np.round(100*roc_auc_score(y_train==3 , y_prob_in_tree[:,2]), 1))\n",
    "\n",
    "# Matriz de confusión\n",
    "pd.crosstab(y_train, y_hat_in_tree, margins = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "72.0\n",
      "64.3\n",
      "64.7\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contraceptive</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80</td>\n",
       "      <td>19</td>\n",
       "      <td>30</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22</td>\n",
       "      <td>34</td>\n",
       "      <td>53</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>123</td>\n",
       "      <td>78</td>\n",
       "      <td>94</td>\n",
       "      <td>295</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0            1   2   3  All\n",
       "Contraceptive                  \n",
       "1               80  19  30  129\n",
       "2               21  25  11   57\n",
       "3               22  34  53  109\n",
       "All            123  78  94  295"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ¿Qué tanto aprendió el modelo de manera generalizada?\n",
    "print(np.round(100*roc_auc_score(y_test==1 , y_prob_out_tree[:,0]), 1))\n",
    "print(np.round(100*roc_auc_score(y_test==2 , y_prob_out_tree[:,1]), 1))\n",
    "print(np.round(100*roc_auc_score(y_test==3 , y_prob_out_tree[:,2]), 1))\n",
    "\n",
    "# Matriz de confusión\n",
    "pd.crosstab(y_test, y_hat_out_tree, margins = True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__¡¡¡HAY OVERFITTING!!!__ La diferencia tan grande entre el AUC de la curva ROC entre el entrenamiento y la prueba nos lo indica. Para corregir esto volvemos y cambiamos los hiperparámetros que nos permiten _podar (regularizar)_ los árboles. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=DecisionTreeClassifier(random_state=23),\n",
       "             param_grid={'max_depth': [3, 5, 7, 9, 11, 13, 15, 17]},\n",
       "             return_train_score=True, scoring='roc_auc_ovo')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "arbol = tree.DecisionTreeClassifier(random_state = 23) \n",
    "grilla_arbol = {\"max_depth\": [3, 5, 7, 9, 11, 13, 15, 17]}\n",
    "arbol_cv = GridSearchCV(arbol, grilla_arbol, scoring = \"roc_auc_ovo\", return_train_score = True, cv = 10)\n",
    "arbol_cv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_tree_results = pd.DataFrame(arbol_cv.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time',\n",
       "       'param_max_depth', 'params', 'split0_test_score', 'split1_test_score',\n",
       "       'split2_test_score', 'split3_test_score', 'split4_test_score',\n",
       "       'split5_test_score', 'split6_test_score', 'split7_test_score',\n",
       "       'split8_test_score', 'split9_test_score', 'mean_test_score',\n",
       "       'std_test_score', 'rank_test_score', 'split0_train_score',\n",
       "       'split1_train_score', 'split2_train_score', 'split3_train_score',\n",
       "       'split4_train_score', 'split5_train_score', 'split6_train_score',\n",
       "       'split7_train_score', 'split8_train_score', 'split9_train_score',\n",
       "       'mean_train_score', 'std_train_score'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_tree_results.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 5}"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arbol_cv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x1e0df742be0>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# Visualicemos\n",
    "fig, ax = plt.subplots(figsize = (20, 6))\n",
    "sns.despine()\n",
    "sns.lineplot(data = cv_tree_results, x = 'param_max_depth', y = 'mean_test_score', ax = ax, color = \"forestgreen\",\n",
    "             label = \"Prueba\")\n",
    "sns.lineplot(data = cv_tree_results, x = 'param_max_depth', y = 'mean_train_score', ax = ax, color = \"darkblue\",\n",
    "             label = \"Entrenamiento\")\n",
    "plt.legend()\n",
    "ax.set_xlabel(\"Máxima profundidad\")\n",
    "ax.set_ylabel(\"AUC\")\n",
    "plt.vlines(x = arbol_cv.best_params_[\"max_depth\"], ymin = 0.58, ymax = 1, color = \"red\", \n",
    "           linestyles = \"dashed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 2.50.0 (0)\n",
       " -->\n",
       "<!-- Title: Tree Pages: 1 -->\n",
       "<svg width=\"3220pt\" height=\"581pt\"\n",
       " viewBox=\"0.00 0.00 3220.00 581.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 577)\">\n",
       "<title>Tree</title>\n",
       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-577 3216,-577 3216,4 -4,4\"/>\n",
       "<!-- 0 -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>0</title>\n",
       "<path fill=\"#fcefe6\" stroke=\"black\" d=\"M585.5,-573C585.5,-573 421.5,-573 421.5,-573 415.5,-573 409.5,-567 409.5,-561 409.5,-561 409.5,-517 409.5,-517 409.5,-511 415.5,-505 421.5,-505 421.5,-505 585.5,-505 585.5,-505 591.5,-505 597.5,-511 597.5,-517 597.5,-517 597.5,-561 597.5,-561 597.5,-567 591.5,-573 585.5,-573\"/>\n",
       "<text text-anchor=\"middle\" x=\"503.5\" y=\"-557.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">children &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"503.5\" y=\"-542.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.648</text>\n",
       "<text text-anchor=\"middle\" x=\"503.5\" y=\"-527.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 100.0%</text>\n",
       "<text text-anchor=\"middle\" x=\"503.5\" y=\"-512.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.424, 0.234, 0.341]</text>\n",
       "</g>\n",
       "<!-- 1 -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>1</title>\n",
       "<path fill=\"#e6853f\" stroke=\"black\" d=\"M334,-469C334,-469 185,-469 185,-469 179,-469 173,-463 173,-457 173,-457 173,-413 173,-413 173,-407 179,-401 185,-401 185,-401 334,-401 334,-401 340,-401 346,-407 346,-413 346,-413 346,-457 346,-457 346,-463 340,-469 334,-469\"/>\n",
       "<text text-anchor=\"middle\" x=\"259.5\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">AgeWife &lt;= 18.0</text>\n",
       "<text text-anchor=\"middle\" x=\"259.5\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.053</text>\n",
       "<text text-anchor=\"middle\" x=\"259.5\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 6.2%</text>\n",
       "<text text-anchor=\"middle\" x=\"259.5\" y=\"-408.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.973, 0.0, 0.027]</text>\n",
       "</g>\n",
       "<!-- 0&#45;&gt;1 -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>0&#45;&gt;1</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M424.28,-504.88C399.97,-494.72 373.07,-483.48 348.17,-473.07\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"349.32,-469.75 338.74,-469.12 346.62,-476.21 349.32,-469.75\"/>\n",
       "<text text-anchor=\"middle\" x=\"348.23\" y=\"-488.55\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">True</text>\n",
       "</g>\n",
       "<!-- 8 -->\n",
       "<g id=\"node9\" class=\"node\">\n",
       "<title>8</title>\n",
       "<path fill=\"#fefaf7\" stroke=\"black\" d=\"M982,-469C982,-469 825,-469 825,-469 819,-469 813,-463 813,-457 813,-457 813,-413 813,-413 813,-407 819,-401 825,-401 825,-401 982,-401 982,-401 988,-401 994,-407 994,-413 994,-413 994,-457 994,-457 994,-463 988,-469 982,-469\"/>\n",
       "<text text-anchor=\"middle\" x=\"903.5\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">AgeWife &lt;= 37.5</text>\n",
       "<text text-anchor=\"middle\" x=\"903.5\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.656</text>\n",
       "<text text-anchor=\"middle\" x=\"903.5\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 93.8%</text>\n",
       "<text text-anchor=\"middle\" x=\"903.5\" y=\"-408.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.388, 0.25, 0.362]</text>\n",
       "</g>\n",
       "<!-- 0&#45;&gt;8 -->\n",
       "<g id=\"edge8\" class=\"edge\">\n",
       "<title>0&#45;&gt;8</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M597.53,-514.02C659.24,-498.29 739.88,-477.72 802.87,-461.66\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"804,-464.98 812.83,-459.12 802.27,-458.2 804,-464.98\"/>\n",
       "<text text-anchor=\"middle\" x=\"800.07\" y=\"-476.92\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">False</text>\n",
       "</g>\n",
       "<!-- 2 -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>2</title>\n",
       "<path fill=\"#8139e5\" stroke=\"black\" d=\"M131,-357.5C131,-357.5 12,-357.5 12,-357.5 6,-357.5 0,-351.5 0,-345.5 0,-345.5 0,-316.5 0,-316.5 0,-310.5 6,-304.5 12,-304.5 12,-304.5 131,-304.5 131,-304.5 137,-304.5 143,-310.5 143,-316.5 143,-316.5 143,-345.5 143,-345.5 143,-351.5 137,-357.5 131,-357.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"71.5\" y=\"-342.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"71.5\" y=\"-327.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 0.1%</text>\n",
       "<text text-anchor=\"middle\" x=\"71.5\" y=\"-312.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.0, 0.0, 1.0]</text>\n",
       "</g>\n",
       "<!-- 1&#45;&gt;2 -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>1&#45;&gt;2</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M198.46,-400.88C175.66,-388.51 149.89,-374.53 127.56,-362.41\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"129,-359.21 118.54,-357.52 125.66,-365.37 129,-359.21\"/>\n",
       "</g>\n",
       "<!-- 3 -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>3</title>\n",
       "<path fill=\"#e5833c\" stroke=\"black\" d=\"M346,-365C346,-365 173,-365 173,-365 167,-365 161,-359 161,-353 161,-353 161,-309 161,-309 161,-303 167,-297 173,-297 173,-297 346,-297 346,-297 352,-297 358,-303 358,-309 358,-309 358,-353 358,-353 358,-359 352,-365 346,-365\"/>\n",
       "<text text-anchor=\"middle\" x=\"259.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">OccupationHusband_4 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"259.5\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.027</text>\n",
       "<text text-anchor=\"middle\" x=\"259.5\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 6.1%</text>\n",
       "<text text-anchor=\"middle\" x=\"259.5\" y=\"-304.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.986, 0.0, 0.014]</text>\n",
       "</g>\n",
       "<!-- 1&#45;&gt;3 -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>1&#45;&gt;3</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M259.5,-400.88C259.5,-392.78 259.5,-383.98 259.5,-375.47\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"263,-375.3 259.5,-365.3 256,-375.3 263,-375.3\"/>\n",
       "</g>\n",
       "<!-- 4 -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>4</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M150,-253.5C150,-253.5 31,-253.5 31,-253.5 25,-253.5 19,-247.5 19,-241.5 19,-241.5 19,-212.5 19,-212.5 19,-206.5 25,-200.5 31,-200.5 31,-200.5 150,-200.5 150,-200.5 156,-200.5 162,-206.5 162,-212.5 162,-212.5 162,-241.5 162,-241.5 162,-247.5 156,-253.5 150,-253.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"90.5\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"90.5\" y=\"-223.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5.9%</text>\n",
       "<text text-anchor=\"middle\" x=\"90.5\" y=\"-208.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1.0, 0.0, 0.0]</text>\n",
       "</g>\n",
       "<!-- 3&#45;&gt;4 -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>3&#45;&gt;4</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M204.63,-296.88C184.31,-284.62 161.38,-270.78 141.43,-258.74\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"143.15,-255.69 132.78,-253.52 139.54,-261.68 143.15,-255.69\"/>\n",
       "</g>\n",
       "<!-- 5 -->\n",
       "<g id=\"node6\" class=\"node\">\n",
       "<title>5</title>\n",
       "<path fill=\"#ffffff\" stroke=\"black\" d=\"M327,-261C327,-261 192,-261 192,-261 186,-261 180,-255 180,-249 180,-249 180,-205 180,-205 180,-199 186,-193 192,-193 192,-193 327,-193 327,-193 333,-193 339,-199 339,-205 339,-205 339,-249 339,-249 339,-255 333,-261 327,-261\"/>\n",
       "<text text-anchor=\"middle\" x=\"259.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">EducHusband_2 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"259.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"259.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 0.2%</text>\n",
       "<text text-anchor=\"middle\" x=\"259.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.5, 0.0, 0.5]</text>\n",
       "</g>\n",
       "<!-- 3&#45;&gt;5 -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>3&#45;&gt;5</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M259.5,-296.88C259.5,-288.78 259.5,-279.98 259.5,-271.47\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"263,-271.3 259.5,-261.3 256,-271.3 263,-271.3\"/>\n",
       "</g>\n",
       "<!-- 6 -->\n",
       "<g id=\"node7\" class=\"node\">\n",
       "<title>6</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M158,-149.5C158,-149.5 39,-149.5 39,-149.5 33,-149.5 27,-143.5 27,-137.5 27,-137.5 27,-108.5 27,-108.5 27,-102.5 33,-96.5 39,-96.5 39,-96.5 158,-96.5 158,-96.5 164,-96.5 170,-102.5 170,-108.5 170,-108.5 170,-137.5 170,-137.5 170,-143.5 164,-149.5 158,-149.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"98.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"98.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 0.1%</text>\n",
       "<text text-anchor=\"middle\" x=\"98.5\" y=\"-104.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1.0, 0.0, 0.0]</text>\n",
       "</g>\n",
       "<!-- 5&#45;&gt;6 -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>5&#45;&gt;6</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M207.23,-192.88C187.96,-180.68 166.22,-166.9 147.27,-154.9\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"149.1,-151.92 138.78,-149.52 145.36,-157.83 149.1,-151.92\"/>\n",
       "</g>\n",
       "<!-- 7 -->\n",
       "<g id=\"node8\" class=\"node\">\n",
       "<title>7</title>\n",
       "<path fill=\"#8139e5\" stroke=\"black\" d=\"M319,-149.5C319,-149.5 200,-149.5 200,-149.5 194,-149.5 188,-143.5 188,-137.5 188,-137.5 188,-108.5 188,-108.5 188,-102.5 194,-96.5 200,-96.5 200,-96.5 319,-96.5 319,-96.5 325,-96.5 331,-102.5 331,-108.5 331,-108.5 331,-137.5 331,-137.5 331,-143.5 325,-149.5 319,-149.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"259.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"259.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 0.1%</text>\n",
       "<text text-anchor=\"middle\" x=\"259.5\" y=\"-104.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.0, 0.0, 1.0]</text>\n",
       "</g>\n",
       "<!-- 5&#45;&gt;7 -->\n",
       "<g id=\"edge7\" class=\"edge\">\n",
       "<title>5&#45;&gt;7</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M259.5,-192.88C259.5,-182.33 259.5,-170.6 259.5,-159.85\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"263,-159.52 259.5,-149.52 256,-159.52 263,-159.52\"/>\n",
       "</g>\n",
       "<!-- 9 -->\n",
       "<g id=\"node10\" class=\"node\">\n",
       "<title>9</title>\n",
       "<path fill=\"#eadefb\" stroke=\"black\" d=\"M982,-365C982,-365 825,-365 825,-365 819,-365 813,-359 813,-353 813,-353 813,-309 813,-309 813,-303 819,-297 825,-297 825,-297 982,-297 982,-297 988,-297 994,-303 994,-309 994,-309 994,-353 994,-353 994,-359 988,-365 982,-365\"/>\n",
       "<text text-anchor=\"middle\" x=\"903.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">EducWife_4 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"903.5\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.643</text>\n",
       "<text text-anchor=\"middle\" x=\"903.5\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 66.1%</text>\n",
       "<text text-anchor=\"middle\" x=\"903.5\" y=\"-304.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.33, 0.227, 0.443]</text>\n",
       "</g>\n",
       "<!-- 8&#45;&gt;9 -->\n",
       "<g id=\"edge9\" class=\"edge\">\n",
       "<title>8&#45;&gt;9</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M903.5,-400.88C903.5,-392.78 903.5,-383.98 903.5,-375.47\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"907,-375.3 903.5,-365.3 900,-375.3 907,-375.3\"/>\n",
       "</g>\n",
       "<!-- 24 -->\n",
       "<g id=\"node25\" class=\"node\">\n",
       "<title>24</title>\n",
       "<path fill=\"#f7d6bf\" stroke=\"black\" d=\"M2202.5,-365C2202.5,-365 2038.5,-365 2038.5,-365 2032.5,-365 2026.5,-359 2026.5,-353 2026.5,-353 2026.5,-309 2026.5,-309 2026.5,-303 2032.5,-297 2038.5,-297 2038.5,-297 2202.5,-297 2202.5,-297 2208.5,-297 2214.5,-303 2214.5,-309 2214.5,-309 2214.5,-353 2214.5,-353 2214.5,-359 2208.5,-365 2202.5,-365\"/>\n",
       "<text text-anchor=\"middle\" x=\"2120.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">EducWife_4 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"2120.5\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.601</text>\n",
       "<text text-anchor=\"middle\" x=\"2120.5\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 27.7%</text>\n",
       "<text text-anchor=\"middle\" x=\"2120.5\" y=\"-304.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.528, 0.304, 0.169]</text>\n",
       "</g>\n",
       "<!-- 8&#45;&gt;24 -->\n",
       "<g id=\"edge24\" class=\"edge\">\n",
       "<title>8&#45;&gt;24</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M994.48,-426.38C1215.78,-407.83 1781.24,-360.43 2015.94,-340.76\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"2016.52,-344.23 2026.19,-339.9 2015.93,-337.25 2016.52,-344.23\"/>\n",
       "</g>\n",
       "<!-- 10 -->\n",
       "<g id=\"node11\" class=\"node\">\n",
       "<title>10</title>\n",
       "<path fill=\"#eee4fc\" stroke=\"black\" d=\"M830.5,-261C830.5,-261 666.5,-261 666.5,-261 660.5,-261 654.5,-255 654.5,-249 654.5,-249 654.5,-205 654.5,-205 654.5,-199 660.5,-193 666.5,-193 666.5,-193 830.5,-193 830.5,-193 836.5,-193 842.5,-199 842.5,-205 842.5,-205 842.5,-249 842.5,-249 842.5,-255 836.5,-261 830.5,-261\"/>\n",
       "<text text-anchor=\"middle\" x=\"748.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">children &lt;= 2.5</text>\n",
       "<text text-anchor=\"middle\" x=\"748.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.613</text>\n",
       "<text text-anchor=\"middle\" x=\"748.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 41.0%</text>\n",
       "<text text-anchor=\"middle\" x=\"748.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.383, 0.151, 0.466]</text>\n",
       "</g>\n",
       "<!-- 9&#45;&gt;10 -->\n",
       "<g id=\"edge10\" class=\"edge\">\n",
       "<title>9&#45;&gt;10</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M853.18,-296.88C838.62,-287.3 822.6,-276.76 807.56,-266.86\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"809.11,-263.7 798.84,-261.12 805.27,-269.55 809.11,-263.7\"/>\n",
       "</g>\n",
       "<!-- 17 -->\n",
       "<g id=\"node18\" class=\"node\">\n",
       "<title>17</title>\n",
       "<path fill=\"#f4eefd\" stroke=\"black\" d=\"M1192.5,-261C1192.5,-261 1028.5,-261 1028.5,-261 1022.5,-261 1016.5,-255 1016.5,-249 1016.5,-249 1016.5,-205 1016.5,-205 1016.5,-199 1022.5,-193 1028.5,-193 1028.5,-193 1192.5,-193 1192.5,-193 1198.5,-193 1204.5,-199 1204.5,-205 1204.5,-205 1204.5,-249 1204.5,-249 1204.5,-255 1198.5,-261 1192.5,-261\"/>\n",
       "<text text-anchor=\"middle\" x=\"1110.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">children &lt;= 2.5</text>\n",
       "<text text-anchor=\"middle\" x=\"1110.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.653</text>\n",
       "<text text-anchor=\"middle\" x=\"1110.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 25.1%</text>\n",
       "<text text-anchor=\"middle\" x=\"1110.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.243, 0.351, 0.405]</text>\n",
       "</g>\n",
       "<!-- 9&#45;&gt;17 -->\n",
       "<g id=\"edge17\" class=\"edge\">\n",
       "<title>9&#45;&gt;17</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M970.71,-296.88C990.97,-286.9 1013.35,-275.87 1034.15,-265.62\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1035.85,-268.68 1043.28,-261.12 1032.76,-262.41 1035.85,-268.68\"/>\n",
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       "<!-- 11 -->\n",
       "<g id=\"node12\" class=\"node\">\n",
       "<title>11</title>\n",
       "<path fill=\"#f9e1d0\" stroke=\"black\" d=\"M518,-157C518,-157 361,-157 361,-157 355,-157 349,-151 349,-145 349,-145 349,-101 349,-101 349,-95 355,-89 361,-89 361,-89 518,-89 518,-89 524,-89 530,-95 530,-101 530,-101 530,-145 530,-145 530,-151 524,-157 518,-157\"/>\n",
       "<text text-anchor=\"middle\" x=\"439.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">AgeWife &lt;= 30.5</text>\n",
       "<text text-anchor=\"middle\" x=\"439.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.595</text>\n",
       "<text text-anchor=\"middle\" x=\"439.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 17.3%</text>\n",
       "<text text-anchor=\"middle\" x=\"439.5\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.51, 0.132, 0.358]</text>\n",
       "</g>\n",
       "<!-- 10&#45;&gt;11 -->\n",
       "<g id=\"edge11\" class=\"edge\">\n",
       "<title>10&#45;&gt;11</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M654.11,-194.84C617.94,-182.9 576.57,-169.24 539.86,-157.13\"/>\n",
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       "<!-- 14 -->\n",
       "<g id=\"node15\" class=\"node\">\n",
       "<title>14</title>\n",
       "<path fill=\"#d2b8f6\" stroke=\"black\" d=\"M835,-157C835,-157 662,-157 662,-157 656,-157 650,-151 650,-145 650,-145 650,-101 650,-101 650,-95 656,-89 662,-89 662,-89 835,-89 835,-89 841,-89 847,-95 847,-101 847,-101 847,-145 847,-145 847,-151 841,-157 835,-157\"/>\n",
       "<text text-anchor=\"middle\" x=\"748.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">OccupationHusband_1 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"748.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.592</text>\n",
       "<text text-anchor=\"middle\" x=\"748.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 23.7%</text>\n",
       "<text text-anchor=\"middle\" x=\"748.5\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.29, 0.165, 0.545]</text>\n",
       "</g>\n",
       "<!-- 10&#45;&gt;14 -->\n",
       "<g id=\"edge14\" class=\"edge\">\n",
       "<title>10&#45;&gt;14</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M748.5,-192.88C748.5,-184.78 748.5,-175.98 748.5,-167.47\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"752,-167.3 748.5,-157.3 745,-167.3 752,-167.3\"/>\n",
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       "<!-- 12 -->\n",
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       "<title>12</title>\n",
       "<path fill=\"#fcf2ea\" stroke=\"black\" d=\"M263.5,-53C263.5,-53 99.5,-53 99.5,-53 93.5,-53 87.5,-47 87.5,-41 87.5,-41 87.5,-12 87.5,-12 87.5,-6 93.5,0 99.5,0 99.5,0 263.5,0 263.5,0 269.5,0 275.5,-6 275.5,-12 275.5,-12 275.5,-41 275.5,-41 275.5,-47 269.5,-53 263.5,-53\"/>\n",
       "<text text-anchor=\"middle\" x=\"181.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.6</text>\n",
       "<text text-anchor=\"middle\" x=\"181.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 14.9%</text>\n",
       "<text text-anchor=\"middle\" x=\"181.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.469, 0.126, 0.406]</text>\n",
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       "<!-- 11&#45;&gt;12 -->\n",
       "<g id=\"edge12\" class=\"edge\">\n",
       "<title>11&#45;&gt;12</title>\n",
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       "<!-- 13 -->\n",
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       "<title>13</title>\n",
       "<path fill=\"#eda673\" stroke=\"black\" d=\"M469.5,-53C469.5,-53 305.5,-53 305.5,-53 299.5,-53 293.5,-47 293.5,-41 293.5,-41 293.5,-12 293.5,-12 293.5,-6 299.5,0 305.5,0 305.5,0 469.5,0 469.5,0 475.5,0 481.5,-6 481.5,-12 481.5,-12 481.5,-41 481.5,-41 481.5,-47 475.5,-53 469.5,-53\"/>\n",
       "<text text-anchor=\"middle\" x=\"387.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.39</text>\n",
       "<text text-anchor=\"middle\" x=\"387.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2.5%</text>\n",
       "<text text-anchor=\"middle\" x=\"387.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.759, 0.172, 0.069]</text>\n",
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       "<!-- 11&#45;&gt;13 -->\n",
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       "<title>11&#45;&gt;13</title>\n",
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       "<!-- 15 -->\n",
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       "<title>15</title>\n",
       "<path fill=\"#cfb3f5\" stroke=\"black\" d=\"M675.5,-53C675.5,-53 511.5,-53 511.5,-53 505.5,-53 499.5,-47 499.5,-41 499.5,-41 499.5,-12 499.5,-12 499.5,-6 505.5,0 511.5,0 511.5,0 675.5,0 675.5,0 681.5,0 687.5,-6 687.5,-12 687.5,-12 687.5,-41 687.5,-41 687.5,-47 681.5,-53 675.5,-53\"/>\n",
       "<text text-anchor=\"middle\" x=\"593.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.57</text>\n",
       "<text text-anchor=\"middle\" x=\"593.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 20.5%</text>\n",
       "<text text-anchor=\"middle\" x=\"593.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.299, 0.133, 0.568]</text>\n",
       "</g>\n",
       "<!-- 14&#45;&gt;15 -->\n",
       "<g id=\"edge15\" class=\"edge\">\n",
       "<title>14&#45;&gt;15</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M694.29,-88.95C678.08,-79.07 660.38,-68.28 644.4,-58.53\"/>\n",
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       "<!-- 16 -->\n",
       "<g id=\"node17\" class=\"node\">\n",
       "<title>16</title>\n",
       "<path fill=\"#faf7fe\" stroke=\"black\" d=\"M881.5,-53C881.5,-53 717.5,-53 717.5,-53 711.5,-53 705.5,-47 705.5,-41 705.5,-41 705.5,-12 705.5,-12 705.5,-6 711.5,0 717.5,0 717.5,0 881.5,0 881.5,0 887.5,0 893.5,-6 893.5,-12 893.5,-12 893.5,-41 893.5,-41 893.5,-47 887.5,-53 881.5,-53\"/>\n",
       "<text text-anchor=\"middle\" x=\"799.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.652</text>\n",
       "<text text-anchor=\"middle\" x=\"799.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3.2%</text>\n",
       "<text text-anchor=\"middle\" x=\"799.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.237, 0.368, 0.395]</text>\n",
       "</g>\n",
       "<!-- 14&#45;&gt;16 -->\n",
       "<g id=\"edge16\" class=\"edge\">\n",
       "<title>14&#45;&gt;16</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M766.34,-88.95C771.03,-80.26 776.1,-70.86 780.83,-62.09\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"783.94,-63.7 785.61,-53.24 777.78,-60.37 783.94,-63.7\"/>\n",
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       "<!-- 18 -->\n",
       "<g id=\"node19\" class=\"node\">\n",
       "<title>18</title>\n",
       "<path fill=\"#eee5fc\" stroke=\"black\" d=\"M1197,-157C1197,-157 1024,-157 1024,-157 1018,-157 1012,-151 1012,-145 1012,-145 1012,-101 1012,-101 1012,-95 1018,-89 1024,-89 1024,-89 1197,-89 1197,-89 1203,-89 1209,-95 1209,-101 1209,-101 1209,-145 1209,-145 1209,-151 1203,-157 1197,-157\"/>\n",
       "<text text-anchor=\"middle\" x=\"1110.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">OccupationHusband_1 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"1110.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.659</text>\n",
       "<text text-anchor=\"middle\" x=\"1110.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 14.1%</text>\n",
       "<text text-anchor=\"middle\" x=\"1110.5\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.313, 0.283, 0.404]</text>\n",
       "</g>\n",
       "<!-- 17&#45;&gt;18 -->\n",
       "<g id=\"edge18\" class=\"edge\">\n",
       "<title>17&#45;&gt;18</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1110.5,-192.88C1110.5,-184.78 1110.5,-175.98 1110.5,-167.47\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1114,-167.3 1110.5,-157.3 1107,-167.3 1114,-167.3\"/>\n",
       "</g>\n",
       "<!-- 21 -->\n",
       "<g id=\"node22\" class=\"node\">\n",
       "<title>21</title>\n",
       "<path fill=\"#f5fef8\" stroke=\"black\" d=\"M1499.5,-157C1499.5,-157 1335.5,-157 1335.5,-157 1329.5,-157 1323.5,-151 1323.5,-145 1323.5,-145 1323.5,-101 1323.5,-101 1323.5,-95 1329.5,-89 1335.5,-89 1335.5,-89 1499.5,-89 1499.5,-89 1505.5,-89 1511.5,-95 1511.5,-101 1511.5,-101 1511.5,-145 1511.5,-145 1511.5,-151 1505.5,-157 1499.5,-157\"/>\n",
       "<text text-anchor=\"middle\" x=\"1417.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">AgeWife &lt;= 30.5</text>\n",
       "<text text-anchor=\"middle\" x=\"1417.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.618</text>\n",
       "<text text-anchor=\"middle\" x=\"1417.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 11.0%</text>\n",
       "<text text-anchor=\"middle\" x=\"1417.5\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.154, 0.438, 0.408]</text>\n",
       "</g>\n",
       "<!-- 17&#45;&gt;21 -->\n",
       "<g id=\"edge21\" class=\"edge\">\n",
       "<title>17&#45;&gt;21</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1204.7,-194.7C1239.17,-183.25 1278.37,-170.23 1313.64,-158.51\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1315.1,-161.71 1323.48,-155.24 1312.89,-155.07 1315.1,-161.71\"/>\n",
       "</g>\n",
       "<!-- 19 -->\n",
       "<g id=\"node20\" class=\"node\">\n",
       "<title>19</title>\n",
       "<path fill=\"#d6bef6\" stroke=\"black\" d=\"M1087.5,-53C1087.5,-53 923.5,-53 923.5,-53 917.5,-53 911.5,-47 911.5,-41 911.5,-41 911.5,-12 911.5,-12 911.5,-6 917.5,0 923.5,0 923.5,0 1087.5,0 1087.5,0 1093.5,0 1099.5,-6 1099.5,-12 1099.5,-12 1099.5,-41 1099.5,-41 1099.5,-47 1093.5,-53 1087.5,-53\"/>\n",
       "<text text-anchor=\"middle\" x=\"1005.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.611</text>\n",
       "<text text-anchor=\"middle\" x=\"1005.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 6.9%</text>\n",
       "<text text-anchor=\"middle\" x=\"1005.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.284, 0.198, 0.519]</text>\n",
       "</g>\n",
       "<!-- 18&#45;&gt;19 -->\n",
       "<g id=\"edge19\" class=\"edge\">\n",
       "<title>18&#45;&gt;19</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1073.78,-88.95C1063.41,-79.62 1052.14,-69.47 1041.79,-60.16\"/>\n",
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       "</g>\n",
       "<!-- 20 -->\n",
       "<g id=\"node21\" class=\"node\">\n",
       "<title>20</title>\n",
       "<path fill=\"#f8fefa\" stroke=\"black\" d=\"M1293.5,-53C1293.5,-53 1129.5,-53 1129.5,-53 1123.5,-53 1117.5,-47 1117.5,-41 1117.5,-41 1117.5,-12 1117.5,-12 1117.5,-6 1123.5,0 1129.5,0 1129.5,0 1293.5,0 1293.5,0 1299.5,0 1305.5,-6 1305.5,-12 1305.5,-12 1305.5,-41 1305.5,-41 1305.5,-47 1299.5,-53 1293.5,-53\"/>\n",
       "<text text-anchor=\"middle\" x=\"1211.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.664</text>\n",
       "<text text-anchor=\"middle\" x=\"1211.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 7.2%</text>\n",
       "<text text-anchor=\"middle\" x=\"1211.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.341, 0.365, 0.294]</text>\n",
       "</g>\n",
       "<!-- 18&#45;&gt;20 -->\n",
       "<g id=\"edge20\" class=\"edge\">\n",
       "<title>18&#45;&gt;20</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1145.82,-88.95C1155.8,-79.62 1166.64,-69.47 1176.59,-60.16\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1179.08,-62.62 1183.99,-53.24 1174.3,-57.51 1179.08,-62.62\"/>\n",
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       "<!-- 22 -->\n",
       "<g id=\"node23\" class=\"node\">\n",
       "<title>22</title>\n",
       "<path fill=\"#e4d4f9\" stroke=\"black\" d=\"M1499.5,-53C1499.5,-53 1335.5,-53 1335.5,-53 1329.5,-53 1323.5,-47 1323.5,-41 1323.5,-41 1323.5,-12 1323.5,-12 1323.5,-6 1329.5,0 1335.5,0 1335.5,0 1499.5,0 1499.5,0 1505.5,0 1511.5,-6 1511.5,-12 1511.5,-12 1511.5,-41 1511.5,-41 1511.5,-47 1505.5,-53 1499.5,-53\"/>\n",
       "<text text-anchor=\"middle\" x=\"1417.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.631</text>\n",
       "<text text-anchor=\"middle\" x=\"1417.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2.9%</text>\n",
       "<text text-anchor=\"middle\" x=\"1417.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.324, 0.206, 0.471]</text>\n",
       "</g>\n",
       "<!-- 21&#45;&gt;22 -->\n",
       "<g id=\"edge22\" class=\"edge\">\n",
       "<title>21&#45;&gt;22</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1417.5,-88.95C1417.5,-80.72 1417.5,-71.85 1417.5,-63.48\"/>\n",
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       "<!-- 23 -->\n",
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       "<title>23</title>\n",
       "<path fill=\"#d3f9e3\" stroke=\"black\" d=\"M1705.5,-53C1705.5,-53 1541.5,-53 1541.5,-53 1535.5,-53 1529.5,-47 1529.5,-41 1529.5,-41 1529.5,-12 1529.5,-12 1529.5,-6 1535.5,0 1541.5,0 1541.5,0 1705.5,0 1705.5,0 1711.5,0 1717.5,-6 1717.5,-12 1717.5,-12 1717.5,-41 1717.5,-41 1717.5,-47 1711.5,-53 1705.5,-53\"/>\n",
       "<text text-anchor=\"middle\" x=\"1623.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.571</text>\n",
       "<text text-anchor=\"middle\" x=\"1623.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 8.1%</text>\n",
       "<text text-anchor=\"middle\" x=\"1623.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.094, 0.521, 0.385]</text>\n",
       "</g>\n",
       "<!-- 21&#45;&gt;23 -->\n",
       "<g id=\"edge23\" class=\"edge\">\n",
       "<title>21&#45;&gt;23</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1489.55,-88.95C1511.99,-78.66 1536.58,-67.37 1558.51,-57.32\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1560.09,-60.44 1567.72,-53.09 1557.18,-54.08 1560.09,-60.44\"/>\n",
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       "<!-- 25 -->\n",
       "<g id=\"node26\" class=\"node\">\n",
       "<title>25</title>\n",
       "<path fill=\"#efb082\" stroke=\"black\" d=\"M2202.5,-261C2202.5,-261 2038.5,-261 2038.5,-261 2032.5,-261 2026.5,-255 2026.5,-249 2026.5,-249 2026.5,-205 2026.5,-205 2026.5,-199 2032.5,-193 2038.5,-193 2038.5,-193 2202.5,-193 2202.5,-193 2208.5,-193 2214.5,-199 2214.5,-205 2214.5,-205 2214.5,-249 2214.5,-249 2214.5,-255 2208.5,-261 2202.5,-261\"/>\n",
       "<text text-anchor=\"middle\" x=\"2120.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">MediaExposure &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"2120.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.468</text>\n",
       "<text text-anchor=\"middle\" x=\"2120.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 15.6%</text>\n",
       "<text text-anchor=\"middle\" x=\"2120.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.696, 0.179, 0.125]</text>\n",
       "</g>\n",
       "<!-- 24&#45;&gt;25 -->\n",
       "<g id=\"edge25\" class=\"edge\">\n",
       "<title>24&#45;&gt;25</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M2120.5,-296.88C2120.5,-288.78 2120.5,-279.98 2120.5,-271.47\"/>\n",
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       "<!-- 32 -->\n",
       "<g id=\"node33\" class=\"node\">\n",
       "<title>32</title>\n",
       "<path fill=\"#d3f9e3\" stroke=\"black\" d=\"M2733,-261C2733,-261 2576,-261 2576,-261 2570,-261 2564,-255 2564,-249 2564,-249 2564,-205 2564,-205 2564,-199 2570,-193 2576,-193 2576,-193 2733,-193 2733,-193 2739,-193 2745,-199 2745,-205 2745,-205 2745,-249 2745,-249 2745,-255 2739,-261 2733,-261\"/>\n",
       "<text text-anchor=\"middle\" x=\"2654.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">children &lt;= 1.5</text>\n",
       "<text text-anchor=\"middle\" x=\"2654.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.637</text>\n",
       "<text text-anchor=\"middle\" x=\"2654.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 12.1%</text>\n",
       "<text text-anchor=\"middle\" x=\"2654.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.31, 0.465, 0.225]</text>\n",
       "</g>\n",
       "<!-- 24&#45;&gt;32 -->\n",
       "<g id=\"edge32\" class=\"edge\">\n",
       "<title>24&#45;&gt;32</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M2214.74,-312C2309.98,-293.81 2456.6,-265.8 2554.02,-247.19\"/>\n",
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       "<!-- 26 -->\n",
       "<g id=\"node27\" class=\"node\">\n",
       "<title>26</title>\n",
       "<path fill=\"#f1bc96\" stroke=\"black\" d=\"M2098,-157C2098,-157 1941,-157 1941,-157 1935,-157 1929,-151 1929,-145 1929,-145 1929,-101 1929,-101 1929,-95 1935,-89 1941,-89 1941,-89 2098,-89 2098,-89 2104,-89 2110,-95 2110,-101 2110,-101 2110,-145 2110,-145 2110,-151 2104,-157 2098,-157\"/>\n",
       "<text text-anchor=\"middle\" x=\"2019.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">AgeWife &lt;= 46.5</text>\n",
       "<text text-anchor=\"middle\" x=\"2019.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.53</text>\n",
       "<text text-anchor=\"middle\" x=\"2019.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 12.5%</text>\n",
       "<text text-anchor=\"middle\" x=\"2019.5\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.633, 0.218, 0.15]</text>\n",
       "</g>\n",
       "<!-- 25&#45;&gt;26 -->\n",
       "<g id=\"edge26\" class=\"edge\">\n",
       "<title>25&#45;&gt;26</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M2087.71,-192.88C2078.8,-183.89 2069.05,-174.04 2059.78,-164.68\"/>\n",
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       "<title>29</title>\n",
       "<path fill=\"#e68844\" stroke=\"black\" d=\"M2304.5,-157C2304.5,-157 2140.5,-157 2140.5,-157 2134.5,-157 2128.5,-151 2128.5,-145 2128.5,-145 2128.5,-101 2128.5,-101 2128.5,-95 2134.5,-89 2140.5,-89 2140.5,-89 2304.5,-89 2304.5,-89 2310.5,-89 2316.5,-95 2316.5,-101 2316.5,-101 2316.5,-145 2316.5,-145 2316.5,-151 2310.5,-157 2304.5,-157\"/>\n",
       "<text text-anchor=\"middle\" x=\"2222.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">StandardLivingIndex &lt;= 3.5</text>\n",
       "<text text-anchor=\"middle\" x=\"2222.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.104</text>\n",
       "<text text-anchor=\"middle\" x=\"2222.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3.1%</text>\n",
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      "text/plain": [
       "<graphviz.sources.Source at 0x1e0e172f100>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Graficamos el árbol\n",
    "import graphviz\n",
    "\n",
    "dot_data = tree.export_graphviz(arbol_cv.best_estimator_, out_file = None, \n",
    "                                feature_names = X_train.columns,\n",
    "                                rounded = True, proportion = True, \n",
    "                                filled = True)\n",
    "\n",
    "graph = graphviz.Source(dot_data, format = \"png\") \n",
    "graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'arbol.png'"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph.render(filename = 'arbol')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Cuáles son las variables más importantes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Importancia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>StandardLivingIndex</th>\n",
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       "    <tr>\n",
       "      <th>OccupationHusband_4</th>\n",
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       "      <th>EducHusband_3</th>\n",
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       "    <tr>\n",
       "      <th>MediaExposure</th>\n",
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       "      <th>OccupationHusband_1</th>\n",
       "      <td>0.041885</td>\n",
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       "      <th>EducWife_4</th>\n",
       "      <td>0.204560</td>\n",
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       "      <th>AgeWife</th>\n",
       "      <td>0.305660</td>\n",
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       "      <th>children</th>\n",
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       "                     Importancia\n",
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       "EducHusband_1           0.000000\n",
       "EducWife_3              0.000000\n",
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       "EducHusband_3           0.012303\n",
       "MediaExposure           0.029284\n",
       "OccupationHusband_1     0.041885\n",
       "EducWife_4              0.204560\n",
       "AgeWife                 0.305660\n",
       "children                0.387693"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creamos un dataframe con las importancias\n",
    "importancia_arbol = pd.DataFrame(arbol_cv.best_estimator_.feature_importances_, \n",
    "                                 index = X_train.columns, \n",
    "                                 columns = ['Importancia']).sort_values(\"Importancia\", ascending = True)\n",
    "importancia_arbol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualización de la importancia\n",
    "importancia_arbol.plot(kind = 'barh', figsize=(20, 5))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Entrenamiento del Random Forest\n",
    "\n",
    "**Hiperparámetros**\n",
    "- Número de árboles\n",
    "- Máximo número de variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importo la función para estimar RandomForest\n",
    "from sklearn.ensemble import RandomForestClassifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=6, random_state=23)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Defino el RandomForest\n",
    "rf = RandomForestClassifier(max_depth = 6, n_estimators = 100, random_state = 23)\n",
    "# Estimo el modelo\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicción dentro y fuera de muestra\n",
    "y_hat_in_rf = rf.predict(X_train)\n",
    "y_hat_out_rf = rf.predict(X_test)\n",
    "\n",
    "# Predecimos las probabilidades\n",
    "y_prob_in_rf = rf.predict_proba(X_train)\n",
    "y_prob_out_rf = rf.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "84.9\n",
      "83.6\n",
      "81.2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>col_0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contraceptive</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>356</td>\n",
       "      <td>45</td>\n",
       "      <td>99</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>59</td>\n",
       "      <td>143</td>\n",
       "      <td>74</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>71</td>\n",
       "      <td>68</td>\n",
       "      <td>263</td>\n",
       "      <td>402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>486</td>\n",
       "      <td>256</td>\n",
       "      <td>436</td>\n",
       "      <td>1178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0            1    2    3   All\n",
       "Contraceptive                     \n",
       "1              356   45   99   500\n",
       "2               59  143   74   276\n",
       "3               71   68  263   402\n",
       "All            486  256  436  1178"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Que tanto aprendió dentro de la muestra?\n",
    "print(np.round(100*roc_auc_score(y_train==1 , y_prob_in_rf[:,0]), 1))\n",
    "print(np.round(100*roc_auc_score(y_train==2 , y_prob_in_rf[:,1]), 1))\n",
    "print(np.round(100*roc_auc_score(y_train==3 , y_prob_in_rf[:,2]), 1))\n",
    "\n",
    "# Matriz de confusión\n",
    "pd.crosstab(y_train, y_hat_in_rf, margins = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79.0\n",
      "71.9\n",
      "68.2\n"
     ]
    },
    {
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       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contraceptive</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>89</td>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "      <td>24</td>\n",
       "      <td>53</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>136</td>\n",
       "      <td>56</td>\n",
       "      <td>103</td>\n",
       "      <td>295</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0            1   2    3  All\n",
       "Contraceptive                   \n",
       "1               89  10   30  129\n",
       "2               15  22   20   57\n",
       "3               32  24   53  109\n",
       "All            136  56  103  295"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ¿Qué tanto aprendió el modelo de manera generalizada?\n",
    "print(np.round(100*roc_auc_score(y_test==1 , y_prob_out_rf[:,0]), 1))\n",
    "print(np.round(100*roc_auc_score(y_test==2 , y_prob_out_rf[:,1]), 1))\n",
    "print(np.round(100*roc_auc_score(y_test==3 , y_prob_out_rf[:,2]), 1))\n",
    "\n",
    "# Matriz de confusión\n",
    "pd.crosstab(y_test, y_hat_out_rf, margins = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=RandomForestClassifier(random_state=23),\n",
       "             param_grid={'max_depth': [3, 5, 7, 9, 11, 13, 15, 17]},\n",
       "             return_train_score=True, scoring='roc_auc_ovo')"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators = 100, random_state = 23)\n",
    "grilla_rf = {\"max_depth\": [3, 5, 7, 9, 11, 13, 15, 17]}\n",
    "rf_cv = GridSearchCV(rf, grilla_rf, scoring = \"roc_auc_ovo\", return_train_score = True, cv = 10)\n",
    "rf_cv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_rf_results = pd.DataFrame(rf_cv.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x1e0e17d3f10>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualicemos\n",
    "fig, ax = plt.subplots(figsize = (20, 6))\n",
    "sns.despine()\n",
    "sns.lineplot(data = cv_rf_results, x = 'param_max_depth', y = 'mean_test_score', ax = ax, color = \"forestgreen\",\n",
    "             label = \"Prueba\")\n",
    "sns.lineplot(data = cv_rf_results, x = 'param_max_depth', y = 'mean_train_score', ax = ax, color = \"darkblue\",\n",
    "             label = \"Entrenamiento\")\n",
    "plt.legend()\n",
    "ax.set_xlabel(\"Máxima profundidad\")\n",
    "ax.set_ylabel(\"AUC\")\n",
    "plt.vlines(x = rf_cv.best_params_[\"max_depth\"], ymin = 0.58, ymax = 1, color = \"red\", \n",
    "           linestyles = \"dashed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>Importancia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OccupationHusband_4</th>\n",
       "      <td>0.008540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EducHusband_1</th>\n",
       "      <td>0.014720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OccupationHusband_2</th>\n",
       "      <td>0.016541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EducWife_3</th>\n",
       "      <td>0.019510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EducHusband_2</th>\n",
       "      <td>0.020025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IslamWife</th>\n",
       "      <td>0.020178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OccupationHusband_3</th>\n",
       "      <td>0.021818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EducWife_2</th>\n",
       "      <td>0.022629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EducHusband_3</th>\n",
       "      <td>0.024677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EducHusband_4</th>\n",
       "      <td>0.025513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EducWife_1</th>\n",
       "      <td>0.025833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WorkingWife</th>\n",
       "      <td>0.027177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MediaExposure</th>\n",
       "      <td>0.037492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OccupationHusband_1</th>\n",
       "      <td>0.046241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StandardLivingIndex</th>\n",
       "      <td>0.066309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EducWife_4</th>\n",
       "      <td>0.075255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AgeWife</th>\n",
       "      <td>0.254162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <td>0.273381</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Importancia\n",
       "OccupationHusband_4     0.008540\n",
       "EducHusband_1           0.014720\n",
       "OccupationHusband_2     0.016541\n",
       "EducWife_3              0.019510\n",
       "EducHusband_2           0.020025\n",
       "IslamWife               0.020178\n",
       "OccupationHusband_3     0.021818\n",
       "EducWife_2              0.022629\n",
       "EducHusband_3           0.024677\n",
       "EducHusband_4           0.025513\n",
       "EducWife_1              0.025833\n",
       "WorkingWife             0.027177\n",
       "MediaExposure           0.037492\n",
       "OccupationHusband_1     0.046241\n",
       "StandardLivingIndex     0.066309\n",
       "EducWife_4              0.075255\n",
       "AgeWife                 0.254162\n",
       "children                0.273381"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Estudiamos la importancia de las variables en el RandomForest\n",
    "importancia_rf = pd.DataFrame(rf_cv.best_estimator_.feature_importances_, \n",
    "                            index = X_train.columns, \n",
    "                            columns = ['Importancia']).sort_values(\"Importancia\", ascending = True)\n",
    "importancia_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualización de la importancia\n",
    "importancia_rf.plot(kind = 'barh', figsize = (20,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "auc_promedio_in_tree = roc_auc_score(y_train, y_prob_in_tree, multi_class = \"ovr\")\n",
    "auc_promedio_out_tree = roc_auc_score(y_test, y_prob_out_tree, multi_class = \"ovr\")\n",
    "auc_promedio_in_rf = roc_auc_score(y_train, y_prob_in_rf, multi_class = \"ovr\")\n",
    "auc_promedio_out_rf = roc_auc_score(y_test, y_prob_out_rf, multi_class = \"ovr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultados = pd.DataFrame({\n",
    "    \"Modelo\": [\"Árbol de Decisión\", \"Árbol de Decisión\", \"Random Forest\", \"Random Forest\"],\n",
    "    \"Muestra\": [\"In sample\", \"Out of sample\", \"In sample\", \"Out of sample\"],\n",
    "    \"AUC\": [auc_promedio_in_tree, auc_promedio_out_tree, auc_promedio_in_rf, auc_promedio_out_rf]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'AUC promedio')"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (10, 4))\n",
    "sns.despine()\n",
    "sns.barplot(data = resultados, x = \"Modelo\", y = \"AUC\", hue = \"Muestra\", \n",
    "            palette = [\"darkblue\", \"forestgreen\"], ax = ax)\n",
    "ax.bar_label(ax.containers[0], fmt = '%.2f', label_type = 'center', color = \"white\")\n",
    "ax.bar_label(ax.containers[1], fmt = '%.2f', label_type = 'center', color = \"white\")\n",
    "plt.xlabel(\"Modelo\")\n",
    "plt.ylabel(\"AUC promedio\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tarea\n",
    "Aplique el modelo de Árboles de Decisión y de Random Forest a la base del Taller de Regularización."
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "9d1551fa46f9e82a7abab90bc4adcde7b723fa4e9951d0f8f7b40f50696411b2"
  },
  "kernelspec": {
   "display_name": "Python 3.8.5 ('base')",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}