04. Tree models/2.3 Random Forest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center> [Adult salaries](https://archive.ics.uci.edu/ml/datasets/adult): Random Forest\n",
    "### <center>Predict whether income exceeds $50K/yr based on census data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing   import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble        import RandomForestClassifier\n",
    "from sklearn.metrics         import accuracy_score, balanced_accuracy_score, plot_confusion_matrix\n",
    "from sklearn.inspection      import permutation_importance, plot_partial_dependence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Leer CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "##### DOWNLOAD DATASET. ONLY IN COLAB !!!\n",
    "#!wget -P ./../data/ https://raw.githubusercontent.com/SaturdaysAI-Murcia/machine-learning/master/data/adult.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../data/adult.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>101320</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>1902</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;=50k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>236746</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>10520</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;=50k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>96185</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;50k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>112847</td>\n",
       "      <td>Prof-school</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;=50k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>82297</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;50k</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt     education  education-num  \\\n",
       "0   49            Private  101320    Assoc-acdm           12.0   \n",
       "1   44            Private  236746       Masters           14.0   \n",
       "2   38            Private   96185       HS-grad            NaN   \n",
       "3   38       Self-emp-inc  112847   Prof-school           15.0   \n",
       "4   42   Self-emp-not-inc   82297       7th-8th            NaN   \n",
       "\n",
       "        marital-status        occupation    relationship                 race  \\\n",
       "0   Married-civ-spouse               NaN            Wife                White   \n",
       "1             Divorced   Exec-managerial   Not-in-family                White   \n",
       "2             Divorced               NaN       Unmarried                Black   \n",
       "3   Married-civ-spouse    Prof-specialty         Husband   Asian-Pac-Islander   \n",
       "4   Married-civ-spouse     Other-service            Wife                Black   \n",
       "\n",
       "       sex  capital-gain  capital-loss  hours-per-week  native-country salary  \n",
       "0   Female             0          1902              40   United-States  >=50k  \n",
       "1     Male         10520             0              45   United-States  >=50k  \n",
       "2   Female             0             0              32   United-States   <50k  \n",
       "3     Male             0             0              40   United-States  >=50k  \n",
       "4   Female             0             0              50   United-States   <50k  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Qué queremos predecir? -> `df.salary`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "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": [
    "sns.countplot(df.salary);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center> Pre-processing\n",
    "- Replace categories with their numeric codes\n",
    "- handle missing continuous values\n",
    "- Select target variable\n",
    "- Split train and validation sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna(axis='rows')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import numpy as np\n",
    "# from sklearn.impute import KNNImputer\n",
    "# \n",
    "# X = [[1, 2, np.nan], [3, 4, 3], [np.nan, 6, 5], [8, 8, 7]]\n",
    "# imputer = KNNImputer(n_neighbors=2)\n",
    "# print(imputer.fit_transform(X))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Numerical features:\n",
      " ['age' 'fnlwgt' 'education-num' 'capital-gain' 'capital-loss'\n",
      " 'hours-per-week']\n",
      "\n",
      "Categorical features:\n",
      " ['workclass' 'education' 'marital-status' 'occupation' 'relationship'\n",
      " 'race' 'sex' 'native-country' 'salary']\n",
      "\n",
      "Date/time features:\n",
      " []\n"
     ]
    }
   ],
   "source": [
    "cat  = df.select_dtypes(include=[object]).columns\n",
    "num  = df.select_dtypes(exclude=[object, 'datetime64','timedelta64']).columns\n",
    "time = df.select_dtypes(include=['datetime64']).columns\n",
    "\n",
    "print(\"\\nNumerical features:\\n\", num.values)\n",
    "print(\"\\nCategorical features:\\n\", cat.values)\n",
    "print(\"\\nDate/time features:\\n\", time.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>236746</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>10520</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38</td>\n",
       "      <td>5</td>\n",
       "      <td>112847</td>\n",
       "      <td>14</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>63210</td>\n",
       "      <td>11</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>46</td>\n",
       "      <td>4</td>\n",
       "      <td>328216</td>\n",
       "      <td>11</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>746432</td>\n",
       "      <td>11</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  workclass  fnlwgt  education  education-num  marital-status  \\\n",
       "1    44          4  236746         12           14.0               0   \n",
       "3    38          5  112847         14           15.0               2   \n",
       "5    20          4   63210         11            9.0               4   \n",
       "8    46          4  328216         11            9.0               2   \n",
       "21   26          4  746432         11            9.0               4   \n",
       "\n",
       "    occupation  relationship  race  sex  capital-gain  capital-loss  \\\n",
       "1            4             1     4    1         10520             0   \n",
       "3           10             0     1    1             0             0   \n",
       "5            6             3     4    1             0             0   \n",
       "8            3             0     4    1             0             0   \n",
       "21           6             3     2    1             0             0   \n",
       "\n",
       "    hours-per-week  native-country  salary  \n",
       "1               45              39       1  \n",
       "3               40              39       1  \n",
       "5               15              39       0  \n",
       "8               40              39       1  \n",
       "21              48              39       0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[cat] = df[cat].apply(LabelEncoder().fit_transform)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split Train (80%) and validation (20%) ¡STRATIFIED!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop(\"salary\", axis=\"columns\")\n",
    "y = df[\"salary\"]\n",
    "\n",
    "x_train, x_valid, y_train, y_valid = train_test_split(x, y,\n",
    "                                                      test_size=0.2,\n",
    "                                                      stratify=y, \n",
    "                                                      random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=-1, oob_score=False, random_state=None, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier(n_jobs=-1)\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate Random Forest\n",
    "Use Random Forest to prect new data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, ..., 0, 0, 1])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = model.predict(x_valid)\n",
    "preds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show some **metrics**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (Caution): 85.3999685682854\n",
      "Balanced accuracy:  76.94782149243366\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Accuracy (Caution):\", accuracy_score(y_valid, preds)*100)\n",
    "print(\"Balanced accuracy: \", balanced_accuracy_score(y_valid, preds)*100)\n",
    "plot_confusion_matrix(model, x_valid, y_valid, display_labels=[\"<50k\", \">=50k\"],\n",
    "                      cmap=plt.cm.Blues, normalize=\"pred\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (ensemble tree specific) Feature importance\n",
    "Este grafico nos mustra las varaibles más importantes en la que se fija nuestro modelo\n",
    "<font color='red'>Caution: Can inflate the importance of numerical features.</font> \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "fi = pd.DataFrame({\n",
    "      'feature':     x.columns,\n",
    "      'importance':  model.feature_importances_\n",
    "     }).sort_values('importance', ascending=False).reset_index(drop=True)\n",
    "\n",
    "# Plot\n",
    "sns.barplot(x=fi.importance, y=fi.feature).set_title(\"Feature Importance\",  fontsize=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [Permutation Feature Importance](https://christophm.github.io/interpretable-ml-book/feature-importance.html) (Better, but slower)\n",
    "Otra forma de calcular las varaibles más importantes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute Permutation Feature Importance\n",
    "pfi = permutation_importance(model, x_valid, y_valid, n_repeats=10, random_state=0, n_jobs=-1)\n",
    "\n",
    "# Clean data\n",
    "sorted_idx = pfi.importances_mean.argsort()[::-1]\n",
    "pfi_df = pd.DataFrame(data=pfi.importances[sorted_idx].T, columns=x_valid.columns[sorted_idx])\n",
    "\n",
    "# Plot (This can be barplot, boxplot, violinplot,...)\n",
    "plt.figure(figsize=(12,4))\n",
    "sns.barplot(data=pfi_df, orient=\"h\").set_title(\"Permutation Feature Importance (validation set)\",  fontsize=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [Partial Dependence Plot](https://christophm.github.io/interpretable-ml-book/pdp.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 4))\n",
    "plot_partial_dependence(estimator=model,\n",
    "                        X=x_train,          # raw predictors data.\n",
    "                        features=[\"capital-gain\", \"education-num\", \"relationship\", \"age\"],\n",
    "                        grid_resolution=100, # number of values to plot on x axis\n",
    "                        n_cols=4,\n",
    "                        ax=ax,\n",
    "                        n_jobs=-1           # Faster with all CPU cores\n",
    "                       );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 4))\n",
    "plot_partial_dependence(estimator=model,\n",
    "                        X=x_train,\n",
    "                        features=[\"education-num\", \"age\", (\"age\", \"education-num\")],\n",
    "                        grid_resolution=10,\n",
    "                        ax=ax,\n",
    "                        n_jobs=-1\n",
    "                       );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center>FIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.7.3"
  },
  "name": "seminar13_optional_practice_trees_titanic.ipynb"
 },
 "nbformat": 4,
 "nbformat_minor": 4
}