Machine Learning with Tree-Based Models in Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision tree for classification\n",
    "\n",
    "The advantage of trees compared to linear techniques is that they are able to capture non-linear relationships between features & labels.\n",
    "Their objective is to infer labels of unlabeled data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  diagnosis  radius_mean  concave points_mean\n",
      "0         M        17.99              0.14710\n",
      "1         M        20.57              0.07017\n",
      "2         M        19.69              0.12790\n",
      "3         M        11.42              0.10520\n",
      "4         M        20.29              0.10430\n",
      "5         M        12.45              0.08089\n",
      "6         M        18.25              0.07400\n",
      "7         M        13.71              0.05985\n",
      "8         M        13.00              0.09353\n",
      "9         M        12.46              0.08543\n"
     ]
    }
   ],
   "source": [
    "import sys, os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "path = 'C:/Users/T0230575/source/repos/DataCamp__/Python/_data/'\n",
    "os.chdir(path)\n",
    "\n",
    "df = pd.read_csv('breastcancer.csv')\n",
    "\n",
    "response = ['diagnosis']\n",
    "expla = ['radius_mean','concave points_mean']\n",
    "\n",
    "X = df.loc[:, expla]\n",
    "#y = df.loc[:, response]\n",
    "\n",
    "y = df['diagnosis']\n",
    "y = y.map({'M':1, 'B':0})\n",
    "\n",
    "print(df_reduced.head(n=10))\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 1 0 0]\n"
     ]
    }
   ],
   "source": [
    "# Import DecisionTreeClassifier from sklearn.tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "SEED = 1\n",
    "\n",
    "# Instantiate a DecisionTreeClassifier 'dt' with a maximum depth of 6\n",
    "dt = DecisionTreeClassifier(max_depth=6, random_state=SEED)\n",
    "\n",
    "# Fit dt to the training set\n",
    "dt.fit(X_train, y_train)\n",
    "\n",
    "# Predict test set labels\n",
    "y_pred = dt.predict(X_test)\n",
    "print(y_pred[0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You've just trained your first classification tree! You can see the first five predictions made by the fitted tree on the test set in the console. In the next exercise, you'll evaluate the tree's performance on the entire test set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the classification tree\n",
    "Now that you've fit your first classification tree, it's time to evaluate its performance on the test set. You'll do so using the accuracy metric which corresponds to the fraction of correct predictions made on the test set.\n",
    "\n",
    "The trained model dt from the previous exercise is loaded in your workspace along with the test set features matrix X_test and the array of labels y_test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set accuracy: 0.89\n"
     ]
    }
   ],
   "source": [
    "# Import accuracy_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Predict test set labels\n",
    "y_pred = dt.predict(X_test)\n",
    "\n",
    "# Compute test set accuracy  \n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "print(\"Test set accuracy: {:.2f}\".format(acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using only two features, your tree was able to achieve an accuracy of 89%!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic regression vs classification tree\n",
    "A classification tree divides the feature space into rectangular regions. In contrast, a linear model such as logistic regression produces only a single linear decision boundary dividing the feature space into two decision regions.\n",
    "\n",
    "We have written a custom function called plot_labeled_decision_regions() that you can use to plot the decision regions of a list containing two trained classifiers. You can type help(plot_labeled_decision_regions) in the IPython shell to learn more about this function.\n",
    "\n",
    "X_train, X_test, y_train, y_test, the model dt that you've trained in an earlier exercise , as well as the function plot_labeled_decision_regions() are available in your workspace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.plotting import plot_decision_regions\n",
    "\n",
    "def plot_labeled_decision_regions(X,y, models):\n",
    "    '''Function producing a scatter plot of the instances contained \n",
    "    in the 2D dataset (X,y) along with the decision \n",
    "    regions of two trained classification models contained in the\n",
    "    list 'models'.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    X: pandas DataFrame corresponding to two numerical features \n",
    "    y: pandas Series corresponding the class labels\n",
    "    models: list containing two trained classifiers \n",
    "    \n",
    "    '''\n",
    "    if len(models) != 2:\n",
    "        raise Exception('''Models should be a list containing only two trained classifiers.''')\n",
    "    if not isinstance(X, pd.DataFrame):\n",
    "        raise Exception('''X has to be a pandas DataFrame with two numerical features.''')\n",
    "    if not isinstance(y, pd.Series):\n",
    "        raise Exception('''y has to be a pandas Series corresponding to the labels.''')\n",
    "    fig, ax = plt.subplots(1, 2, figsize=(10.0, 5), sharey=True)\n",
    "    for i, model in enumerate(models):\n",
    "        plot_decision_regions(X.values, y.values, model, legend= 2, ax = ax[i])\n",
    "        ax[i].set_title(model.__class__.__name__)\n",
    "        ax[i].set_xlabel(X.columns[0])\n",
    "        if i == 0:\n",
    "            ax[i].set_ylabel(X.columns[1])\n",
    "            ax[i].set_ylim(X.values[:,1].min(), X.values[:,1].max())\n",
    "            ax[i].set_xlim(X.values[:,0].min(), X.values[:,0].max())\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import LogisticRegression from sklearn.linear_model\n",
    "from sklearn.linear_model import  LogisticRegression\n",
    "\n",
    "# Instatiate logreg\n",
    "logreg = LogisticRegression(random_state=1)\n",
    "\n",
    "# Fit logreg to the training set\n",
    "logreg.fit(X_train, y_train)\n",
    "\n",
    "# Define a list called clfs containing the two classifiers logreg and dt\n",
    "clfs = [logreg, dt]\n",
    "\n",
    "# Review the decision regions of the two classifiers\n",
    "plot_labeled_decision_regions(X_test, y_test, clfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "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>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0  ...          17.33           184.60      2019.0            0.1622   \n",
       "1  ...          23.41           158.80      1956.0            0.1238   \n",
       "2  ...          25.53           152.50      1709.0            0.1444   \n",
       "3  ...          26.50            98.87       567.7            0.2098   \n",
       "4  ...          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wbc = pd.read_csv('wbc.csv')\n",
    "wbc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = wbc[['radius_mean', 'concave points_mean']]\n",
    "y = wbc['diagnosis']\n",
    "y = y.map({'M':1, 'B':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 0 1 0]\n",
      "Test set accuracy: 0.89\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Instantiate a DecisionTreeClassifier 'dt' with a maximum depth of 6\n",
    "dt = DecisionTreeClassifier(max_depth=6, random_state=1)\n",
    "\n",
    "# Fit dt to the training set\n",
    "dt.fit(X_train, y_train)\n",
    "\n",
    "# Predict test set labels\n",
    "y_pred = dt.predict(X_test)\n",
    "print(y_pred[0:5])\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Predict test set labels\n",
    "y_pred = dt.predict(X_test)\n",
    "\n",
    "# Compute test set accuracy\n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "print(\"Test set accuracy: {:.2f}\".format(acc))\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Instantiate logreg\n",
    "logreg = LogisticRegression(random_state=1)\n",
    "\n",
    "# Fit logreg to the training set\n",
    "logreg.fit(X_train, y_train)\n",
    "\n",
    "# Define a list called clfs containing the two classifiers logreg and dt\n",
    "clfs = [logreg, dt]\n",
    "\n",
    "# Review the decision regions of the two classifier\n",
    "plot_labeled_decision_regions(X_test, y_test, clfs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the decision boundary produced by logistic regression is linear while the boundaries produced by the classification tree divide the feature space into rectangular regions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification tree Learning\n",
    "- Building Blocks of a Decision-Tree\n",
    "    - Decision-Tree: data structure consisting of a hierarchy of nodes\n",
    "    - Node: question or prediction\n",
    "    - Three kinds of nodes\n",
    "        - **Root**: no parent node, question giving rise to two children nodes.\n",
    "        - **Internal node**: one parent node, question giving rise to two children nodes.\n",
    "        - **Leaf**: one parent node, no children nodes --> prediction.\n",
    "- Information Gain (IG)\n",
    "![information gain](./image/ig.png)\n",
    "$$ IG(\\underbrace{f}_{\\text{feature}}, \\underbrace{sp}_{\\text{split-point}} ) = I(\\text{parent}) - \\big( \\frac{N_{\\text{left}}}{N}I(\\text{left}) + \\frac{N_{\\text{right}}}{N}I(\\text{right})  \\big) $$\n",
    "    - Criteria to measure the impurity of a note $I(\\text{node})$:\n",
    "        - gini index\n",
    "        - entropy\n",
    "        - etc...\n",
    "- Classification-Tree Learning\n",
    "    - Nodes are grown recursively.\n",
    "    - At each node, split the data based on:\n",
    "        - feature $f$ and split-point $sp$ to maximize $IG(\\text{node})$.\n",
    "        - If $IG(\\text{node}) = 0$, declare the node a leaf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using entropy as a criterion\n",
    "In this exercise, you'll train a classification tree on the Wisconsin Breast Cancer dataset using entropy as an information criterion. You'll do so using all the 30 features in the dataset, which is split into 80% train and 20% test.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=8,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=1,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Instantiate dt_entropy, set 'entropy' as the information criterion\n",
    "dt_entropy = DecisionTreeClassifier(max_depth=8, criterion='entropy', random_state=1)\n",
    "\n",
    "# Fit dt_entropy to the training set\n",
    "dt_entropy.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Entropy vs Gini index\n",
    "In this exercise you'll compare the test set accuracy of dt_entropy to the accuracy of another tree named ```dt_gini```. The tree ```dt_gini``` was trained on the same dataset using the same parameters except for the information criterion which was set to the gini index using the keyword ```'gini'```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=8,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=1,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_gini = DecisionTreeClassifier(max_depth=8, criterion='gini', random_state=1)\n",
    "dt_gini.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy achieved by using entropy:  0.8947368421052632\n",
      "Accuracy achieved by using gini:  0.8859649122807017\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Use dt_entropy to predict test set labels\n",
    "y_pred = dt_entropy.predict(X_test)\n",
    "y_pred_gini = dt_gini.predict(X_test)\n",
    "\n",
    "# Evaluate accuracy_entropy\n",
    "accuracy_entropy = accuracy_score(y_test, y_pred)\n",
    "accuracy_gini = accuracy_score(y_test, y_pred_gini)\n",
    "\n",
    "# Print accuracy_entropy\n",
    "print(\"Accuracy achieved by using entropy: \", accuracy_entropy)\n",
    "\n",
    "# Print accuracy_gini\n",
    "print(\"Accuracy achieved by using gini: \", accuracy_gini)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the two models achieve exactly the same accuracy. Most of the time, the gini index and entropy lead to the same results. The gini index is slightly faster to compute and is the default criterion used in the DecisionTreeClassifier model of scikit-learn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision tree for regression\n",
    "- Information Criterion for Regression Tree\n",
    "$$ I(\\text{node}) = \\underbrace{\\text{MSE}(\\text{node})}_{\\text{mean-squared-error}} = \\dfrac{1}{N_{\\text{node}}} \\sum_{i \\in \\text{node}} \\big(y^{(i)} - \\hat{y}_{\\text{node}}  \\big)^2 $$\n",
    "$$ \\underbrace{\\hat{y}_{\\text{node}}}_{\\text{mean-target-value}} = \\dfrac{1}{N_{\\text{node}}} \\sum_{i \\in \\text{node}}y^{(i)}$$\n",
    "- Prediction\n",
    "$$ \\hat{y}_{\\text{pred}}(\\text{leaf}) = \\dfrac{1}{N_{\\text{leaf}}} \\sum_{i \\in \\text{leaf}} y^{(i)}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train your first regression tree\n",
    "In this exercise, you'll train a regression tree to predict the mpg (miles per gallon) consumption of cars in the [auto-mpg dataset](https://www.kaggle.com/uciml/autompg-dataset) using all the six available features.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>displ</th>\n",
       "      <th>hp</th>\n",
       "      <th>weight</th>\n",
       "      <th>accel</th>\n",
       "      <th>origin</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>88</td>\n",
       "      <td>3139</td>\n",
       "      <td>14.5</td>\n",
       "      <td>US</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>193</td>\n",
       "      <td>4732</td>\n",
       "      <td>18.5</td>\n",
       "      <td>US</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.1</td>\n",
       "      <td>91.0</td>\n",
       "      <td>60</td>\n",
       "      <td>1800</td>\n",
       "      <td>16.4</td>\n",
       "      <td>Asia</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.5</td>\n",
       "      <td>250.0</td>\n",
       "      <td>98</td>\n",
       "      <td>3525</td>\n",
       "      <td>19.0</td>\n",
       "      <td>US</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34.3</td>\n",
       "      <td>97.0</td>\n",
       "      <td>78</td>\n",
       "      <td>2188</td>\n",
       "      <td>15.8</td>\n",
       "      <td>Europe</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  displ   hp  weight  accel  origin  size\n",
       "0  18.0  250.0   88    3139   14.5      US  15.0\n",
       "1   9.0  304.0  193    4732   18.5      US  20.0\n",
       "2  36.1   91.0   60    1800   16.4    Asia  10.0\n",
       "3  18.5  250.0   98    3525   19.0      US  15.0\n",
       "4  34.3   97.0   78    2188   15.8  Europe  10.0"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg = pd.read_csv('auto.csv')\n",
    "mpg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpg = pd.get_dummies(mpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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></th>\n",
       "      <th>mpg</th>\n",
       "      <th>displ</th>\n",
       "      <th>hp</th>\n",
       "      <th>weight</th>\n",
       "      <th>accel</th>\n",
       "      <th>size</th>\n",
       "      <th>origin_Asia</th>\n",
       "      <th>origin_Europe</th>\n",
       "      <th>origin_US</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>88</td>\n",
       "      <td>3139</td>\n",
       "      <td>14.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>193</td>\n",
       "      <td>4732</td>\n",
       "      <td>18.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.1</td>\n",
       "      <td>91.0</td>\n",
       "      <td>60</td>\n",
       "      <td>1800</td>\n",
       "      <td>16.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.5</td>\n",
       "      <td>250.0</td>\n",
       "      <td>98</td>\n",
       "      <td>3525</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34.3</td>\n",
       "      <td>97.0</td>\n",
       "      <td>78</td>\n",
       "      <td>2188</td>\n",
       "      <td>15.8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  displ   hp  weight  accel  size  origin_Asia  origin_Europe  \\\n",
       "0  18.0  250.0   88    3139   14.5  15.0            0              0   \n",
       "1   9.0  304.0  193    4732   18.5  20.0            0              0   \n",
       "2  36.1   91.0   60    1800   16.4  10.0            1              0   \n",
       "3  18.5  250.0   98    3525   19.0  15.0            0              0   \n",
       "4  34.3   97.0   78    2188   15.8  10.0            0              1   \n",
       "\n",
       "   origin_US  \n",
       "0          1  \n",
       "1          1  \n",
       "2          0  \n",
       "3          1  \n",
       "4          0  "
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = mpg.drop('mpg', axis='columns')\n",
    "y = mpg['mpg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=8, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=0.13,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=3, splitter='best')"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "# Instantiate dt\n",
    "dt = DecisionTreeRegressor(max_depth=8, min_samples_leaf=0.13, random_state=3)\n",
    "\n",
    "# Fit dt to the training set\n",
    "dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the regression tree\n",
    "In this exercise, you will evaluate the test set performance of ```dt``` using the Root Mean Squared Error (RMSE) metric. The RMSE of a model measures, on average, how much the model's predictions differ from the actual labels. The RMSE of a model can be obtained by computing the square root of the model's Mean Squared Error (MSE).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set RMSE of dt: 4.37\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Compute y_pred\n",
    "y_pred = dt.predict(X_test)\n",
    "\n",
    "# Compute mse_dt\n",
    "mse_dt = mean_squared_error(y_test, y_pred)\n",
    "\n",
    "# Compute rmse_dt\n",
    "rmse_dt = mse_dt ** (1/2)\n",
    "\n",
    "# Print rmse_dt\n",
    "print(\"Test set RMSE of dt: {:.2f}\".format(rmse_dt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear regression vs regression tree\n",
    "In this exercise, you'll compare the test set RMSE of ```dt``` to that achieved by a linear regression model. We have already instantiated a linear regression model ```lr``` and trained it on the same dataset as ```dt```.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr = LinearRegression()\n",
    "\n",
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression test set RMSE: 5.10\n",
      "Regression Tree test set RMSE: 4.37\n"
     ]
    }
   ],
   "source": [
    "# Predict test set labels\n",
    "y_pred_lr = lr.predict(X_test)\n",
    "\n",
    "# Compute mse_lr\n",
    "mse_lr = MSE(y_test, y_pred_lr)\n",
    "\n",
    "# Compute rmse_lr\n",
    "rmse_lr = mse_lr ** 0.5\n",
    "\n",
    "# Print rmse_lr\n",
    "print(\"Linear Regression test set RMSE: {:.2f}\".format(rmse_lr))\n",
    "\n",
    "# Print rmse_dt\n",
    "print(\"Regression Tree test set RMSE: {:.2f}\".format(rmse_dt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Bias-Variance Tradeoff\n",
    "\n",
    "Difficulties in modeling :\n",
    "1. Over-fitting : when the model fits the noise from the training set.\n",
    "\n",
    "High test-set error, low training-set error. Typical over-fitting.\n",
    "\n",
    "2. Under-fitting : when the model is not flexible enough to approximate new unseen data.\n",
    "\n",
    "Training set error roughly equal to the one of the test set, but both errors are high relatively.\n",
    "Complex dependency is not capture.\n",
    "\n",
    "\n",
    "Generalization Error tells us how well does a model fits onto unseen data. It drives us to find a balance between bias and variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generalization Error\n",
    "- Supervised Learning - Under the Hood\n",
    "    - supervised learning: $y = f(x)$, $f$ is unknown.\n",
    "    - Goals\n",
    "        - Find a model $\\hat{f}$ that best approximates: $f: \\hat{f} \\approx f$\n",
    "        - $\\hat{f}$ can be Logistic Regression, Decision Tree, Neural Network,...\n",
    "        - Discard noise as much as possible\n",
    "        - **End goal**: $\\hat{f}$ should achieve a low predictive error on unseen datasets.\n",
    "- Difficulties in Approximating $f$\n",
    "    - Overfitting: $\\hat{f}(x)$ fits the training set noise.\n",
    "    - Underfitting: $\\hat{f}$ is not flexible enough to approximate $f$.\n",
    "- Generalization Error\n",
    "    - Generalization Error of $\\hat{f}$: Does $\\hat{f}$ generalize well on unseen data?\n",
    "    - It can be decomposed as follows:\n",
    "    $$ \\hat{f} = \\text{bias}^2 + \\text{variance} +\\text{irreducible error} $$\n",
    "    - Bias: error term that tells you, on average, how much $\\hat{f} \\neq f$.\n",
    "![bias](image/bias.png)\n",
    "    - Variance: tells you how much $\\hat{f}$ is inconsistent over different training sets\n",
    "![variance](image/variance.png)\n",
    "- Model Complexity\n",
    "    - Model Complexity: sets the flexibility of $\\hat{f}$\n",
    "    - Example: Maximum tree depth, Minimum samples per leaf, ...\n",
    "- Bias-Variance Tradeoff\n",
    "![](image/ge.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diagnose bias and variance problems\n",
    "- Estimating the Generalization Error\n",
    "    - How do we estimate the generalization error of a model?\n",
    "        - Cannot be done directly because:\n",
    "            - $f$ is unknown\n",
    "            - usually you only have one dataset\n",
    "            - noise is unpredictable.\n",
    "    - Solution\n",
    "        - Split the data to training and test sets\n",
    "        - fit $\\hat{f}$ to the training set\n",
    "        - evaluate the error of $\\hat{f}$ on the unseen test set\n",
    "        - generalization error of $\\hat{f} \\approx$ test set error of $\\hat{f}$\n",
    "- Better model Evaluation with Cross-Validation\n",
    "    - Test set should not be touched until we are confident about $\\hat{f}$'s performance\n",
    "    - Evaluating $\\hat{f}$ on training set: biased estimate, $\\hat{f}$ has already seen all training points\n",
    "    - Solution: Cross-Validation (CV)\n",
    "        - K-Fold CV\n",
    "        - Hold-Out CV\n",
    "- K-Fold CV\n",
    "    $$\\text{CV error} = \\dfrac{E_1 + \\cdots + E_{10}}{10} $$\n",
    "- Diagnose Variance Problems\n",
    "    - If $\\hat{f}$ suffers from **high variance**: CV error of $\\hat{f} >$ training set error of $\\hat{f}$\n",
    "        - $\\hat{f}$ is said to overfit the training set. To remedy overfitting:\n",
    "            - Decrease model complexity\n",
    "            - Gather more data, ...\n",
    "- Diagnose Bias Problems\n",
    "    - if $\\hat{f}$ suffers from high bias: CV error of $\\hat{f} \\approx$ training set error of $\\hat{f} >>$ desired error.\n",
    "    - $\\hat{f}$ is said to underfit the training set. To remedy underfitting:\n",
    "        - Increase model complexity\n",
    "        - Gather more relevant features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"C:/Users/T0230575/Desktop/Datalab LAS/Documentation/Method/bias-variance-tradeoff.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](bias-variance-tradeoff.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Instantiate the model\n",
    "In the following set of exercises, you'll diagnose the bias and variance problems of a regression tree. The regression tree you'll define in this exercise will be used to predict the mpg consumption of cars from the auto dataset using all available features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Complexity, bias and variance\n",
    "In the video, you saw how the complexity of a model labeled \n",
    " influences the bias and variance terms of its generalization error.\n",
    "Which of the following correctly describes the relationship between \n",
    "'s complexity and \n",
    "'s bias and variance terms?\n",
    "\n",
    "As the complexity of \n",
    " increases, the bias term decreases while the variance term increases.\n",
    "press"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"C:/Users/T0230575/Desktop/Datalab LAS/Documentation/Method/bias-variance-tradeoff2.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"C:/Users/T0230575/Desktop/Datalab LAS/Documentation/Method/bias-variance-tradeoff3.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " B suffers from high bias and underfits the training set.\n",
    " A suffers from high variance and overfits the training set.\n",
    " Model B is not able to capture the nonlinear dependence of mpg on displ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diagnose bias and variance problems\n",
    "\n",
    "How do we estimate the generalization error of a model ?\n",
    "Cannot be done directly because the test data is unknown, we have one dataset generally, and noise is unpredictable/random.\n",
    "Solution : split data into train/test sets then cross-validation (k-fold / hold-out).\n",
    "\n",
    "1. Variance problems.\n",
    "High variance often means over-fitting = to remedy, need to reduce model complexity, decrease max depth, increase min samples per leaf, gather more data etc.\n",
    "\n",
    "2. Bias problems.\n",
    "Bias often means under-fitting = to remedy, increase model complexity, increase max depth, decrease min samples per left, gather more features to resume the problem etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diagnose bias and variance problems\n",
    "- Estimating the Generalization Error\n",
    "    - How do we estimate the generalization error of a model?\n",
    "        - Cannot be done directly because:\n",
    "            - $f$ is unknown\n",
    "            - usually you only have one dataset\n",
    "            - noise is unpredictable.\n",
    "    - Solution\n",
    "        - Split the data to training and test sets\n",
    "        - fit $\\hat{f}$ to the training set\n",
    "        - evaluate the error of $\\hat{f}$ on the unseen test set\n",
    "        - generalization error of $\\hat{f} \\approx$ test set error of $\\hat{f}$\n",
    "- Better model Evaluation with Cross-Validation\n",
    "    - Test set should not be touched until we are confident about $\\hat{f}$'s performance\n",
    "    - Evaluating $\\hat{f}$ on training set: biased estimate, $\\hat{f}$ has already seen all training points\n",
    "    - Solution: Cross-Validation (CV)\n",
    "        - K-Fold CV\n",
    "        - Hold-Out CV\n",
    "- K-Fold CV\n",
    "    $$\\text{CV error} = \\dfrac{E_1 + \\cdots + E_{10}}{10} $$\n",
    "- Diagnose Variance Problems\n",
    "    - If $\\hat{f}$ suffers from **high variance**: CV error of $\\hat{f} >$ training set error of $\\hat{f}$\n",
    "        - $\\hat{f}$ is said to overfit the training set. To remedy overfitting:\n",
    "            - Decrease model complexity\n",
    "            - Gather more data, ...\n",
    "- Diagnose Bias Problems\n",
    "    - if $\\hat{f}$ suffers from high bias: CV error of $\\hat{f} \\approx$ training set error of $\\hat{f} >>$ desired error.\n",
    "    - $\\hat{f}$ is said to underfit the training set. To remedy underfitting:\n",
    "        - Increase model complexity\n",
    "        - Gather more relevant features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Instantiate the model\n",
    "In the following set of exercises, you'll diagnose the bias and variance problems of a regression tree. The regression tree you'll define in this exercise will be used to predict the mpg consumption of cars from the auto dataset using all available features.\n",
    "\n",
    "We have already processed the data and loaded the features matrix X and the array y in your workspace. In addition, the DecisionTreeRegressor class was imported from sklearn.tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>displ</th>\n",
       "      <th>hp</th>\n",
       "      <th>weight</th>\n",
       "      <th>accel</th>\n",
       "      <th>origin</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>88</td>\n",
       "      <td>3139</td>\n",
       "      <td>14.5</td>\n",
       "      <td>US</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>193</td>\n",
       "      <td>4732</td>\n",
       "      <td>18.5</td>\n",
       "      <td>US</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.1</td>\n",
       "      <td>91.0</td>\n",
       "      <td>60</td>\n",
       "      <td>1800</td>\n",
       "      <td>16.4</td>\n",
       "      <td>Asia</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.5</td>\n",
       "      <td>250.0</td>\n",
       "      <td>98</td>\n",
       "      <td>3525</td>\n",
       "      <td>19.0</td>\n",
       "      <td>US</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34.3</td>\n",
       "      <td>97.0</td>\n",
       "      <td>78</td>\n",
       "      <td>2188</td>\n",
       "      <td>15.8</td>\n",
       "      <td>Europe</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  displ   hp  weight  accel  origin  size\n",
       "0  18.0  250.0   88    3139   14.5      US  15.0\n",
       "1   9.0  304.0  193    4732   18.5      US  20.0\n",
       "2  36.1   91.0   60    1800   16.4    Asia  10.0\n",
       "3  18.5  250.0   98    3525   19.0      US  15.0\n",
       "4  34.3   97.0   78    2188   15.8  Europe  10.0"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg = pd.read_csv('auto.csv')\n",
    "mpg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>mpg</th>\n",
       "      <th>displ</th>\n",
       "      <th>hp</th>\n",
       "      <th>weight</th>\n",
       "      <th>accel</th>\n",
       "      <th>size</th>\n",
       "      <th>origin_Asia</th>\n",
       "      <th>origin_Europe</th>\n",
       "      <th>origin_US</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>88</td>\n",
       "      <td>3139</td>\n",
       "      <td>14.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>193</td>\n",
       "      <td>4732</td>\n",
       "      <td>18.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.1</td>\n",
       "      <td>91.0</td>\n",
       "      <td>60</td>\n",
       "      <td>1800</td>\n",
       "      <td>16.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.5</td>\n",
       "      <td>250.0</td>\n",
       "      <td>98</td>\n",
       "      <td>3525</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34.3</td>\n",
       "      <td>97.0</td>\n",
       "      <td>78</td>\n",
       "      <td>2188</td>\n",
       "      <td>15.8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  displ   hp  weight  accel  size  origin_Asia  origin_Europe  \\\n",
       "0  18.0  250.0   88    3139   14.5  15.0            0              0   \n",
       "1   9.0  304.0  193    4732   18.5  20.0            0              0   \n",
       "2  36.1   91.0   60    1800   16.4  10.0            1              0   \n",
       "3  18.5  250.0   98    3525   19.0  15.0            0              0   \n",
       "4  34.3   97.0   78    2188   15.8  10.0            0              1   \n",
       "\n",
       "   origin_US  \n",
       "0          1  \n",
       "1          1  \n",
       "2          0  \n",
       "3          1  \n",
       "4          0  "
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg = pd.get_dummies(mpg)\n",
    "mpg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = mpg.drop('mpg', axis='columns')\n",
    "y = mpg['mpg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "# Set SEED for reproducibility\n",
    "SEED = 1\n",
    "\n",
    "# Split the data into 70% train and 30% test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=SEED)\n",
    "\n",
    "# Instantiate a DecisionTreeRegressor dt\n",
    "dt = DecisionTreeRegressor(max_depth=4, min_samples_leaf=0.26, random_state=SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the 10-fold CV error\n",
    "In this exercise, you'll evaluate the 10-fold CV Root Mean Squared Error (RMSE) achieved by the regression tree ```dt``` that you instantiated in the previous exercise.\n",
    "\n",
    "Note that since ```cross_val_score``` has only the option of evaluating the negative MSEs, its output should be multiplied by negative one to obtain the MSEs. The CV RMSE can then be obtained by computing the square root of the average MSE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV RMSE: 5.14\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# Compute the array containing the 10-folds CV MSEs\n",
    "MSE_CV_scores = - cross_val_score(dt, X_train, y_train, cv=10, \n",
    "                                 scoring='neg_mean_squared_error', n_jobs=-1)\n",
    "\n",
    "# Compute the 10-folds CV RMSE\n",
    "RMSE_CV = (MSE_CV_scores.mean()) ** 0.5\n",
    "\n",
    "# Print RMSE_CV\n",
    "print('CV RMSE: {:.2f}'.format(RMSE_CV))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A very good practice is to keep the test set untouched until you are confident about your model's performance. CV is a great technique to get an estimate of a model's performance without affecting the test set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the training error\n",
    "You'll now evaluate the training set RMSE achieved by the regression tree dt that you instantiated in a previous exercise.\n",
    "\n",
    "Note that in scikit-learn, the MSE of a model can be computed as follows:\n",
    "```python\n",
    "MSE_model = mean_squared_error(y_true, y_predicted)\n",
    "```\n",
    "where we use the function mean_squared_error from the ```metrics``` module and pass it the true labels ```y_true``` as a first argument, and the predicted labels from the model ```y_predicted``` as a second argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train RMSE: 5.15\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error as MSE\n",
    "\n",
    "# Fit dt to the training set\n",
    "dt.fit(X_train, y_train)\n",
    "\n",
    "# Predict the labels of the training set\n",
    "y_pred_train = dt.predict(X_train)\n",
    "\n",
    "# Evaluate the training set RMSE of dt\n",
    "RMSE_train = (MSE(y_train, y_pred_train)) ** 0.5\n",
    "\n",
    "# Print RMSE_train\n",
    "print(\"Train RMSE: {:.2f}\".format(RMSE_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the training error is roughly equal to the 10-folds CV error you obtained in the previous exercise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dt suffers from high bias because RMSE_CV  RMSE_train and both scores are greater than baseline_RMSE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ensemble Learning\n",
    "- Advantages of CARTs\n",
    "    - Simple to understand\n",
    "    - Simple to interpret\n",
    "    - Easy to use\n",
    "    - Flexibility: ability to describe non-linear dependencies.\n",
    "    - Preprocessing: no need to standardize or normalize features.\n",
    "- Limitations of CARTs\n",
    "    - Classification: can only produce orthogonal decision boundaries\n",
    "    - Sensitive to small variations in the training set\n",
    "    - High variance: unconstrained CARTs may overfit the training set\n",
    "    - Solution: **ensemble learning**\n",
    "- Ensemble Learning\n",
    "    - Train different models on the same dataset.\n",
    "    - Let each model make its predictions\n",
    "    - Meta-Model: aggregates predictionsof individual models\n",
    "    - Final prediction: more robust and less prone to errors\n",
    "    - Best results: models are skillfull in different ways.\n",
    "![ensemble](image/ensemble.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the ensemble\n",
    "In the following set of exercises, you'll work with the [Indian Liver Patient Dataset](https://www.kaggle.com/jeevannagaraj/indian-liver-patient-dataset) from the UCI Machine learning repository.\n",
    "\n",
    "In this exercise, you'll instantiate three classifiers to predict whether a patient suffers from a liver disease using all the features present in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age_std</th>\n",
       "      <th>Total_Bilirubin_std</th>\n",
       "      <th>Direct_Bilirubin_std</th>\n",
       "      <th>Alkaline_Phosphotase_std</th>\n",
       "      <th>Alamine_Aminotransferase_std</th>\n",
       "      <th>Aspartate_Aminotransferase_std</th>\n",
       "      <th>Total_Protiens_std</th>\n",
       "      <th>Albumin_std</th>\n",
       "      <th>Albumin_and_Globulin_Ratio_std</th>\n",
       "      <th>Is_male_std</th>\n",
       "      <th>Liver_disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.247403</td>\n",
       "      <td>-0.420320</td>\n",
       "      <td>-0.495414</td>\n",
       "      <td>-0.428870</td>\n",
       "      <td>-0.355832</td>\n",
       "      <td>-0.319111</td>\n",
       "      <td>0.293722</td>\n",
       "      <td>0.203446</td>\n",
       "      <td>-0.147390</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.062306</td>\n",
       "      <td>1.218936</td>\n",
       "      <td>1.423518</td>\n",
       "      <td>1.675083</td>\n",
       "      <td>-0.093573</td>\n",
       "      <td>-0.035962</td>\n",
       "      <td>0.939655</td>\n",
       "      <td>0.077462</td>\n",
       "      <td>-0.648461</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.062306</td>\n",
       "      <td>0.640375</td>\n",
       "      <td>0.926017</td>\n",
       "      <td>0.816243</td>\n",
       "      <td>-0.115428</td>\n",
       "      <td>-0.146459</td>\n",
       "      <td>0.478274</td>\n",
       "      <td>0.203446</td>\n",
       "      <td>-0.178707</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.815511</td>\n",
       "      <td>-0.372106</td>\n",
       "      <td>-0.388807</td>\n",
       "      <td>-0.449416</td>\n",
       "      <td>-0.366760</td>\n",
       "      <td>-0.312205</td>\n",
       "      <td>0.293722</td>\n",
       "      <td>0.329431</td>\n",
       "      <td>0.165780</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.679294</td>\n",
       "      <td>0.093956</td>\n",
       "      <td>0.179766</td>\n",
       "      <td>-0.395996</td>\n",
       "      <td>-0.295731</td>\n",
       "      <td>-0.177537</td>\n",
       "      <td>0.755102</td>\n",
       "      <td>-0.930414</td>\n",
       "      <td>-1.713237</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age_std  Total_Bilirubin_std  Direct_Bilirubin_std  \\\n",
       "0  1.247403            -0.420320             -0.495414   \n",
       "1  1.062306             1.218936              1.423518   \n",
       "2  1.062306             0.640375              0.926017   \n",
       "3  0.815511            -0.372106             -0.388807   \n",
       "4  1.679294             0.093956              0.179766   \n",
       "\n",
       "   Alkaline_Phosphotase_std  Alamine_Aminotransferase_std  \\\n",
       "0                 -0.428870                     -0.355832   \n",
       "1                  1.675083                     -0.093573   \n",
       "2                  0.816243                     -0.115428   \n",
       "3                 -0.449416                     -0.366760   \n",
       "4                 -0.395996                     -0.295731   \n",
       "\n",
       "   Aspartate_Aminotransferase_std  Total_Protiens_std  Albumin_std  \\\n",
       "0                       -0.319111            0.293722     0.203446   \n",
       "1                       -0.035962            0.939655     0.077462   \n",
       "2                       -0.146459            0.478274     0.203446   \n",
       "3                       -0.312205            0.293722     0.329431   \n",
       "4                       -0.177537            0.755102    -0.930414   \n",
       "\n",
       "   Albumin_and_Globulin_Ratio_std  Is_male_std  Liver_disease  \n",
       "0                       -0.147390            0              1  \n",
       "1                       -0.648461            1              1  \n",
       "2                       -0.178707            1              1  \n",
       "3                        0.165780            1              1  \n",
       "4                       -1.713237            1              1  "
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indian = pd.read_csv('indian_liver_patient_preprocessed.csv', index_col=0)\n",
    "indian.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = indian.drop('Liver_disease', axis='columns')\n",
    "y = indian['Liver_disease']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('Logistic Regression', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
      "          n_jobs=None, penalty='l2', random_state=1, solver='warn',\n",
      "          tol=0.0001, verbose=0, warm_start=False)), ('K Nearest Neighbors', KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "           metric_params=None, n_jobs=None, n_neighbors=27, p=2,\n",
      "           weights='uniform')), ('Classification Tree', DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
      "            max_features=None, max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=0.13, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, presort=False, random_state=1,\n",
      "            splitter='best'))]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier as KNN\n",
    "\n",
    "# Set seed for reproducibility\n",
    "SEED = 1\n",
    "\n",
    "# Instantiate lr\n",
    "lr = LogisticRegression(random_state=SEED)\n",
    "\n",
    "# Instantiate knn\n",
    "knn = KNN(n_neighbors=27)\n",
    "\n",
    "# Instantiate dt\n",
    "dt = DecisionTreeClassifier(min_samples_leaf=0.13, random_state=SEED)\n",
    "\n",
    "# Define the list classifiers\n",
    "classifiers = [\n",
    "    ('Logistic Regression', lr),\n",
    "    ('K Nearest Neighbors', knn),\n",
    "    ('Classification Tree', dt)\n",
    "]\n",
    "print(classifiers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression : 0.764\n",
      "K Nearest Neighbors : 0.701\n",
      "Classification Tree : 0.730\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Iterate over the pre-defined list of classifiers\n",
    "for clf_name, clf in classifiers:\n",
    "    # Fit clf to the training set\n",
    "    clf.fit(X_train, y_train)\n",
    "    \n",
    "    # Predict y_pred\n",
    "    y_pred = clf.predict(X_test)\n",
    "    \n",
    "    # Calculate accuracy\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    \n",
    "    # Evaluate clf's accuracy on the test set\n",
    "    print('{:s} : {:.3f}'.format(clf_name, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how Logistic Regression achieved the highest accuracy of 76.4%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Better performance with a Voting Classifier\n",
    "Finally, you'll evaluate the performance of a voting classifier that takes the outputs of the models defined in the list classifiers and assigns labels by majority voting.\n",
    "\n",
    "REMEMBER : voting classifier means using different algorithms on the same training dataset; it is not the same as bagging."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Voting Classifier: 0.770\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# Import VotingClassifier from sklearn.ensemble\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "# Instantiate a VotingClassifier vc\n",
    "vc = VotingClassifier(estimators=classifiers)     \n",
    "\n",
    "# Fit vc to the training set\n",
    "vc.fit(X_train, y_train)   \n",
    "\n",
    "# Evaluate the test set predictions\n",
    "y_pred = vc.predict(X_test)\n",
    "\n",
    "# Calculate accuracy score\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print('Voting Classifier: {:.3f}'.format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the voting classifier achieves a test set accuracy of 77%. This value is greater than that achieved by LogisticRegression."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bagging and Random Forests\n",
    "> A Summary of lecture \"Machine Learning with Tree-Based Models in Python\n",
    "\", via datacamp\n",
    "\n",
    "- toc: true \n",
    "- badges: true\n",
    "- comments: true\n",
    "- author: Chanseok Kang\n",
    "- categories: [Python, Datacamp, Machine_Learning]\n",
    "- image: images/feature_importances.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bagging\n",
    "- Ensemble Methods\n",
    "    - Voting Classifier\n",
    "        - same training set,\n",
    "        - $\\neq$ algortihms\n",
    "    - Bagging\n",
    "        - One algorithm\n",
    "        - $\\neq$ subsets of the training set\n",
    "- Bagging\n",
    "    - Bootstrap Aggregation\n",
    "    - Uses a technique known as the bootstrap\n",
    "    - Reduces variance of individual models in the ensemble\n",
    "_ Bootstrap\n",
    "![bootstrap](image/bootstrap.png)\n",
    "- Bootstrap-training\n",
    "![training](image/bs_training.png)\n",
    "- Bootstrap-predict\n",
    "![predict](image/bs_predict.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"C:/Users/T0230575/Desktop/Datalab LAS/Documentation/Method/bagging1.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the bagging classifier\n",
    "In the following exercises you'll work with the [Indian Liver Patient dataset](https://www.kaggle.com/uciml/indian-liver-patient-records) from the UCI machine learning repository. Your task is to predict whether a patient suffers from a liver disease using 10 features including Albumin, age and gender. You'll do so using a Bagging Classifier.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "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_std</th>\n",
       "      <th>Total_Bilirubin_std</th>\n",
       "      <th>Direct_Bilirubin_std</th>\n",
       "      <th>Alkaline_Phosphotase_std</th>\n",
       "      <th>Alamine_Aminotransferase_std</th>\n",
       "      <th>Aspartate_Aminotransferase_std</th>\n",
       "      <th>Total_Protiens_std</th>\n",
       "      <th>Albumin_std</th>\n",
       "      <th>Albumin_and_Globulin_Ratio_std</th>\n",
       "      <th>Is_male_std</th>\n",
       "      <th>Liver_disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.247403</td>\n",
       "      <td>-0.420320</td>\n",
       "      <td>-0.495414</td>\n",
       "      <td>-0.428870</td>\n",
       "      <td>-0.355832</td>\n",
       "      <td>-0.319111</td>\n",
       "      <td>0.293722</td>\n",
       "      <td>0.203446</td>\n",
       "      <td>-0.147390</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.062306</td>\n",
       "      <td>1.218936</td>\n",
       "      <td>1.423518</td>\n",
       "      <td>1.675083</td>\n",
       "      <td>-0.093573</td>\n",
       "      <td>-0.035962</td>\n",
       "      <td>0.939655</td>\n",
       "      <td>0.077462</td>\n",
       "      <td>-0.648461</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.062306</td>\n",
       "      <td>0.640375</td>\n",
       "      <td>0.926017</td>\n",
       "      <td>0.816243</td>\n",
       "      <td>-0.115428</td>\n",
       "      <td>-0.146459</td>\n",
       "      <td>0.478274</td>\n",
       "      <td>0.203446</td>\n",
       "      <td>-0.178707</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.815511</td>\n",
       "      <td>-0.372106</td>\n",
       "      <td>-0.388807</td>\n",
       "      <td>-0.449416</td>\n",
       "      <td>-0.366760</td>\n",
       "      <td>-0.312205</td>\n",
       "      <td>0.293722</td>\n",
       "      <td>0.329431</td>\n",
       "      <td>0.165780</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.679294</td>\n",
       "      <td>0.093956</td>\n",
       "      <td>0.179766</td>\n",
       "      <td>-0.395996</td>\n",
       "      <td>-0.295731</td>\n",
       "      <td>-0.177537</td>\n",
       "      <td>0.755102</td>\n",
       "      <td>-0.930414</td>\n",
       "      <td>-1.713237</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age_std  Total_Bilirubin_std  Direct_Bilirubin_std  \\\n",
       "0  1.247403            -0.420320             -0.495414   \n",
       "1  1.062306             1.218936              1.423518   \n",
       "2  1.062306             0.640375              0.926017   \n",
       "3  0.815511            -0.372106             -0.388807   \n",
       "4  1.679294             0.093956              0.179766   \n",
       "\n",
       "   Alkaline_Phosphotase_std  Alamine_Aminotransferase_std  \\\n",
       "0                 -0.428870                     -0.355832   \n",
       "1                  1.675083                     -0.093573   \n",
       "2                  0.816243                     -0.115428   \n",
       "3                 -0.449416                     -0.366760   \n",
       "4                 -0.395996                     -0.295731   \n",
       "\n",
       "   Aspartate_Aminotransferase_std  Total_Protiens_std  Albumin_std  \\\n",
       "0                       -0.319111            0.293722     0.203446   \n",
       "1                       -0.035962            0.939655     0.077462   \n",
       "2                       -0.146459            0.478274     0.203446   \n",
       "3                       -0.312205            0.293722     0.329431   \n",
       "4                       -0.177537            0.755102    -0.930414   \n",
       "\n",
       "   Albumin_and_Globulin_Ratio_std  Is_male_std  Liver_disease  \n",
       "0                       -0.147390            0              1  \n",
       "1                       -0.648461            1              1  \n",
       "2                       -0.178707            1              1  \n",
       "3                        0.165780            1              1  \n",
       "4                       -1.713237            1              1  "
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indian = pd.read_csv('indian_liver_patient_preprocessed.csv', index_col=0)\n",
    "indian.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = indian.drop('Liver_disease', axis=1)\n",
    "y = indian['Liver_disease']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "\n",
    "# Instantiate dt\n",
    "dt = DecisionTreeClassifier(random_state=1)\n",
    "\n",
    "# Instantiate bc (estimator is the decision tree, and we do 50 trees)\n",
    "bc = BaggingClassifier(base_estimator=dt, n_estimators=50, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate Bagging performance\n",
    "Now that you instantiated the bagging classifier, it's time to train it and evaluate its test set accuracy.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set accuracy of bc: 0.71\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1, stratify=y)\n",
    "\n",
    "# Fit bc to the training set\n",
    "bc.fit(X_train, y_train)\n",
    "\n",
    "# Predict test set labels\n",
    "y_pred = bc.predict(X_test)\n",
    "\n",
    "# Evaluate acc_test\n",
    "acc_test = accuracy_score(y_test, y_pred)\n",
    "print('Test set accuracy of bc: {:.2f}'.format(acc_test)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A single tree dt would have achieved an accuracy of 63% which is 8% lower than bc's accuracy!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Out of Bag Evaluation (OOB are the obs left after a bootstrap selects its own observations using training data)\n",
    "- Bagging\n",
    "    - Some instances may be sampled several times for one model, other instances may not be sampled at all.\n",
    "- Out Of Bag (OOB) instances\n",
    "    - On average, for each model, 63% of the training instances are sampled\n",
    "    - The remaining 37% constitute the OOB instances\n",
    "- OOB Evaluation\n",
    "![oob](image/oob.png)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare the ground\n",
    "In the following exercises, you'll compare the OOB accuracy to the test set accuracy of a bagging classifier trained on the Indian Liver Patient dataset.\n",
    "\n",
    "In sklearn, you can evaluate the OOB accuracy of an ensemble classifier by setting the parameter ```oob_score``` to ```True``` during instantiation. After training the classifier, the OOB accuracy can be obtained by accessing the ```.oob_score_``` attribute from the corresponding instance.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "\n",
    "# Instantiate dt\n",
    "dt = DecisionTreeClassifier(min_samples_leaf=8, random_state=1)\n",
    "\n",
    "# Instantiate bc\n",
    "bc = BaggingClassifier(base_estimator=dt, n_estimators=50, oob_score=True, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OOB Score vs Test Set Score\n",
    "Now that you instantiated bc, you will fit it to the training set and evaluate its test set and OOB accuracies.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set accuracy: 0.698, OOB accuracy: 0.700\n"
     ]
    }
   ],
   "source": [
    "# Fit bc to the training set \n",
    "bc.fit(X_train, y_train)\n",
    "\n",
    "# Predict test set labels\n",
    "y_pred = bc.predict(X_test)\n",
    "\n",
    "# Evaluate test set accuracy\n",
    "acc_test = accuracy_score(y_test, y_pred)\n",
    "\n",
    "# Evaluate OOB accuracy\n",
    "acc_oob = bc.oob_score_\n",
    "\n",
    "# Print acc_test and acc_oob\n",
    "print('Test set accuracy: {:.3f}, OOB accuracy: {:.3f}'.format(acc_test, acc_oob))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The test set accuracy and the OOB accuracy of bc are both roughly equal to 70%!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forests (RF)\n",
    "- Bagging\n",
    "    - Base estimator: Decision Tree, Logistic Regression, Neural Network, ...\n",
    "    - Each estimator is trained on a distinct bootstrap sample of the training set\n",
    "    - Estimators use all features for training and prediction\n",
    "- Further Diversity with Random Forest\n",
    "    - Base estimator: Decision Tree\n",
    "    - Each estimator is trained on a different bootstrap sample having the same size as the training set\n",
    "    - RF introduces further randomization in the training of individual trees\n",
    "    - $d$ features are sampled at each node without replacement\n",
    "    $$ d < \\text{total number of features} $$\n",
    "- Random Forest: Training\n",
    "![rf_training](image/rf_training.png)\n",
    "- Random Forest: Prediction\n",
    "![rf_predict](image/rf_prediction.png)\n",
    "- Feature importance\n",
    "    - Tree based methods: enable measuring the importance of each feature in prediction\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"C:/Users/T0230575/Desktop/Datalab LAS/Documentation/Method/RF1.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train an RF regressor\n",
    "In the following exercises you'll predict bike rental demand in the Capital Bikeshare program in Washington, D.C using historical weather data from the [Bike Sharing Demand](https://www.kaggle.com/c/bike-sharing-demand) dataset available through Kaggle. For this purpose, you will be using the random forests algorithm. As a first step, you'll define a random forests regressor and fit it to the training set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "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>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "      <th>instant</th>\n",
       "      <th>mnth</th>\n",
       "      <th>yr</th>\n",
       "      <th>Clear to partly cloudy</th>\n",
       "      <th>Light Precipitation</th>\n",
       "      <th>Misty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>149</td>\n",
       "      <td>13004</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>93</td>\n",
       "      <td>13005</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.0896</td>\n",
       "      <td>90</td>\n",
       "      <td>13006</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>33</td>\n",
       "      <td>13007</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>4</td>\n",
       "      <td>13008</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hr  holiday  workingday  temp   hum  windspeed  cnt  instant  mnth  yr  \\\n",
       "0   0        0           0  0.76  0.66     0.0000  149    13004     7   1   \n",
       "1   1        0           0  0.74  0.70     0.1343   93    13005     7   1   \n",
       "2   2        0           0  0.72  0.74     0.0896   90    13006     7   1   \n",
       "3   3        0           0  0.72  0.84     0.1343   33    13007     7   1   \n",
       "4   4        0           0  0.70  0.79     0.1940    4    13008     7   1   \n",
       "\n",
       "   Clear to partly cloudy  Light Precipitation  Misty  \n",
       "0                       1                    0      0  \n",
       "1                       1                    0      0  \n",
       "2                       1                    0      0  \n",
       "3                       1                    0      0  \n",
       "4                       1                    0      0  "
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike = pd.read_csv('bikes.csv')\n",
    "bike.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = bike.drop('cnt', axis='columns')\n",
    "y = bike['cnt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=25, n_jobs=None,\n",
       "           oob_score=False, random_state=2, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import RandomForestRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# Instantiate rf (25 trees)\n",
    "rf = RandomForestRegressor(n_estimators=25,\n",
    "            random_state=2)\n",
    "            \n",
    "# Fit rf to the training set    \n",
    "rf.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the RF regressor\n",
    "You'll now evaluate the test set RMSE of the random forests regressor ```rf``` that you trained in the previous exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set RMSE of rf: 54.49\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error as MSE\n",
    "\n",
    "# Predict the test set labels\n",
    "y_pred = rf.predict(X_test)\n",
    "\n",
    "# Evaluate the test set RMSE\n",
    "rmse_test = MSE(y_test, y_pred) ** 0.5\n",
    "\n",
    "# Print rmse_test\n",
    "print('Test set RMSE of rf: {:.2f}'.format(rmse_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can try training a single CART on the same dataset. The test set RMSE achieved by rf is significantly smaller than that achieved by a single CART!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing features importances\n",
    "In this exercise, you'll determine which features were the most predictive according to the random forests regressor ```rf``` that you trained in a previous exercise.\n",
    "\n",
    "For this purpose, you'll draw a horizontal barplot of the feature importance as assessed by ```rf```. Fortunately, this can be done easily thanks to plotting capabilities of ```pandas```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "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": [
    "# Create a pd.Series of features importances\n",
    "importances = pd.Series(data=rf.feature_importances_, index=X_train.columns)\n",
    "\n",
    "# Sort importances\n",
    "importances_sorted = importances.sort_values()\n",
    "\n",
    "# Draw a horizontal barplot of importances_sorted\n",
    "importances_sorted.plot(kind='barh', color='lightgreen')\n",
    "plt.title('Features Importances')\n",
    "plt.savefig('feature_importances.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apparently, ```hr``` and ```workingday``` are the most important features according to ```rf```. The importances of these two features add up to more than 90%!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boosting\n",
    "> A Summary of lecture \"Machine Learning with Tree-Based Models in Python\n",
    "\", via datacamp\n",
    "\n",
    "- toc: true \n",
    "- badges: true\n",
    "- comments: true\n",
    "- author: Chanseok Kang\n",
    "- categories: [Python, Datacamp, Machine_Learning]\n",
    "- image: images/sgb_train.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adaboost\n",
    "- Boosting: Ensemble method combining several weak learners to form a strong learner.\n",
    "    - Weak learner: Model doing slightly better than random guessing\n",
    "        - E.g., Dicision stump (CART whose maximum depth is 1)\n",
    "    - Train an ensemble of predictors sequentially.\n",
    "    - Each predictor tries to correct its predecessor\n",
    "    - Most popular boosting methods:\n",
    "        - AdaBoost\n",
    "        - Gradient Boosting\n",
    "- AdaBoost\n",
    "    - Stands for **Ada**ptive **Boost**ing\n",
    "    - Each predictor pays more attention to the instances wrongly predicted by its predecessor.\n",
    "    - Achieved by changing the weights of training instances.\n",
    "    - Each predictor is assigned a coefficient $\\alpha$ that depends on the predictor's training error\n",
    "- AdaBoost: Training\n",
    "![adaboost_train](image/adaboost_train.png)\n",
    "    - Learning rate: $0 < \\eta < 1$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"C:/Users/T0230575/Desktop/Datalab LAS/Documentation/Method/Ada1.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the AdaBoost classifier\n",
    "In the following exercises you'll revisit the [Indian Liver Patient](https://www.kaggle.com/uciml/indian-liver-patient-records) dataset which was introduced in a previous chapter. Your task is to predict whether a patient suffers from a liver disease using 10 features including Albumin, age and gender. However, this time, you'll be training an AdaBoost ensemble to perform the classification task. In addition, given that this dataset is imbalanced, you'll be using the ROC AUC score as a metric instead of accuracy.\n",
    "\n",
    "As a first step, you'll start by instantiating an AdaBoost classifier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "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_std</th>\n",
       "      <th>Total_Bilirubin_std</th>\n",
       "      <th>Direct_Bilirubin_std</th>\n",
       "      <th>Alkaline_Phosphotase_std</th>\n",
       "      <th>Alamine_Aminotransferase_std</th>\n",
       "      <th>Aspartate_Aminotransferase_std</th>\n",
       "      <th>Total_Protiens_std</th>\n",
       "      <th>Albumin_std</th>\n",
       "      <th>Albumin_and_Globulin_Ratio_std</th>\n",
       "      <th>Is_male_std</th>\n",
       "      <th>Liver_disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.247403</td>\n",
       "      <td>-0.420320</td>\n",
       "      <td>-0.495414</td>\n",
       "      <td>-0.428870</td>\n",
       "      <td>-0.355832</td>\n",
       "      <td>-0.319111</td>\n",
       "      <td>0.293722</td>\n",
       "      <td>0.203446</td>\n",
       "      <td>-0.147390</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.062306</td>\n",
       "      <td>1.218936</td>\n",
       "      <td>1.423518</td>\n",
       "      <td>1.675083</td>\n",
       "      <td>-0.093573</td>\n",
       "      <td>-0.035962</td>\n",
       "      <td>0.939655</td>\n",
       "      <td>0.077462</td>\n",
       "      <td>-0.648461</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.062306</td>\n",
       "      <td>0.640375</td>\n",
       "      <td>0.926017</td>\n",
       "      <td>0.816243</td>\n",
       "      <td>-0.115428</td>\n",
       "      <td>-0.146459</td>\n",
       "      <td>0.478274</td>\n",
       "      <td>0.203446</td>\n",
       "      <td>-0.178707</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.815511</td>\n",
       "      <td>-0.372106</td>\n",
       "      <td>-0.388807</td>\n",
       "      <td>-0.449416</td>\n",
       "      <td>-0.366760</td>\n",
       "      <td>-0.312205</td>\n",
       "      <td>0.293722</td>\n",
       "      <td>0.329431</td>\n",
       "      <td>0.165780</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.679294</td>\n",
       "      <td>0.093956</td>\n",
       "      <td>0.179766</td>\n",
       "      <td>-0.395996</td>\n",
       "      <td>-0.295731</td>\n",
       "      <td>-0.177537</td>\n",
       "      <td>0.755102</td>\n",
       "      <td>-0.930414</td>\n",
       "      <td>-1.713237</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age_std  Total_Bilirubin_std  Direct_Bilirubin_std  \\\n",
       "0  1.247403            -0.420320             -0.495414   \n",
       "1  1.062306             1.218936              1.423518   \n",
       "2  1.062306             0.640375              0.926017   \n",
       "3  0.815511            -0.372106             -0.388807   \n",
       "4  1.679294             0.093956              0.179766   \n",
       "\n",
       "   Alkaline_Phosphotase_std  Alamine_Aminotransferase_std  \\\n",
       "0                 -0.428870                     -0.355832   \n",
       "1                  1.675083                     -0.093573   \n",
       "2                  0.816243                     -0.115428   \n",
       "3                 -0.449416                     -0.366760   \n",
       "4                 -0.395996                     -0.295731   \n",
       "\n",
       "   Aspartate_Aminotransferase_std  Total_Protiens_std  Albumin_std  \\\n",
       "0                       -0.319111            0.293722     0.203446   \n",
       "1                       -0.035962            0.939655     0.077462   \n",
       "2                       -0.146459            0.478274     0.203446   \n",
       "3                       -0.312205            0.293722     0.329431   \n",
       "4                       -0.177537            0.755102    -0.930414   \n",
       "\n",
       "   Albumin_and_Globulin_Ratio_std  Is_male_std  Liver_disease  \n",
       "0                       -0.147390            0              1  \n",
       "1                       -0.648461            1              1  \n",
       "2                       -0.178707            1              1  \n",
       "3                        0.165780            1              1  \n",
       "4                       -1.713237            1              1  "
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indian = pd.read_csv('indian_liver_patient_preprocessed.csv', index_col=0)\n",
    "indian.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = indian.drop('Liver_disease', axis='columns')\n",
    "y = indian['Liver_disease']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "# Instantiate dt\n",
    "dt = DecisionTreeClassifier(max_depth=2, random_state=1)\n",
    "\n",
    "# Instantiate ada\n",
    "ada = AdaBoostClassifier(base_estimator=dt, n_estimators=180, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the AdaBoost classifier\n",
    "Now that you've instantiated the AdaBoost classifier ada, it's time train it. You will also predict the probabilities of obtaining the positive class in the test set. This can be done as follows:\n",
    "\n",
    "Once the classifier ```ada``` is trained, call the ```.predict_proba()``` method by passing ```X_test``` as a parameter and extract these probabilities by slicing all the values in the second column as follows:\n",
    "```python\n",
    "ada.predict_proba(X_test)[:,1]\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit ada to the training set\n",
    "ada.fit(X_train, y_train)\n",
    "\n",
    "# Compute the probabilities of obtaining the positive class\n",
    "y_pred_proba = ada.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the AdaBoost classifier\n",
    "Now that you're done training ```ada``` and predicting the probabilities of obtaining the positive class in the test set, it's time to evaluate ```ada```'s ROC AUC score. Recall that the ROC AUC score of a binary classifier can be determined using the ```roc_auc_score()``` function from ```sklearn.metrics```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC score: 0.63\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# Evaluate test-set roc_auc_score\n",
    "ada_roc_auc = roc_auc_score(y_test, y_pred_proba)\n",
    "\n",
    "# Print roc_auc_score\n",
    "print('ROC AUC score: {:.2f}'.format(ada_roc_auc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This untuned AdaBoost classifier achieved a ROC AUC score of 0.71!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Boosting (GB)\n",
    "- Gradient Boosted Trees\n",
    "    - Sequential correction of predecessor's errors\n",
    "    - Does not tweak the weights of training instances\n",
    "    - Fit each predictor is trained using its predecessor's residual errors as labels\n",
    "    - Gradient Boosted Trees: a CART is used as a base learner.\n",
    "- Gradient Boosted Trees for Regression: Training\n",
    "![gb_train](image/gb_train.png)\n",
    "    - $\\eta$ (shrinkage)\n",
    "        - Ensemble is shrinked after it is multiplied by a learning rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the GB regressor\n",
    "You'll now revisit the [Bike Sharing Demand](https://www.kaggle.com/c/bike-sharing-demand) dataset that was introduced in the previous chapter. Recall that your task is to predict the bike rental demand using historical weather data from the Capital Bikeshare program in Washington, D.C.. For this purpose, you'll be using a gradient boosting regressor.\n",
    "\n",
    "As a first step, you'll start by instantiating a gradient boosting regressor which you will train in the next exercise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "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",
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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></th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "      <th>instant</th>\n",
       "      <th>mnth</th>\n",
       "      <th>yr</th>\n",
       "      <th>Clear to partly cloudy</th>\n",
       "      <th>Light Precipitation</th>\n",
       "      <th>Misty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>149</td>\n",
       "      <td>13004</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>93</td>\n",
       "      <td>13005</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.0896</td>\n",
       "      <td>90</td>\n",
       "      <td>13006</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>33</td>\n",
       "      <td>13007</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>4</td>\n",
       "      <td>13008</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hr  holiday  workingday  temp   hum  windspeed  cnt  instant  mnth  yr  \\\n",
       "0   0        0           0  0.76  0.66     0.0000  149    13004     7   1   \n",
       "1   1        0           0  0.74  0.70     0.1343   93    13005     7   1   \n",
       "2   2        0           0  0.72  0.74     0.0896   90    13006     7   1   \n",
       "3   3        0           0  0.72  0.84     0.1343   33    13007     7   1   \n",
       "4   4        0           0  0.70  0.79     0.1940    4    13008     7   1   \n",
       "\n",
       "   Clear to partly cloudy  Light Precipitation  Misty  \n",
       "0                       1                    0      0  \n",
       "1                       1                    0      0  \n",
       "2                       1                    0      0  \n",
       "3                       1                    0      0  \n",
       "4                       1                    0      0  "
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike = pd.read_csv('bikes.csv')\n",
    "bike.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = bike.drop('cnt', axis='columns')\n",
    "y = bike['cnt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "# Instantiate gb\n",
    "gb = GradientBoostingRegressor(max_depth=4, n_estimators=200, random_state=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the GB regressor\n",
    "You'll now train the gradient boosting regressor ```gb``` that you instantiated in the previous exercise and predict test set labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit gb to the training set\n",
    "gb.fit(X_train, y_train)\n",
    "\n",
    "# Predict test set labels\n",
    "y_pred = gb.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the GB regressor\n",
    "Now that the test set predictions are available, you can use them to evaluate the test set Root Mean Squared Error (RMSE) of ```gb```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set RMSE of gb: 49.796\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error as MSE\n",
    "\n",
    "# Compute MSE\n",
    "mse_test = MSE(y_test, y_pred)\n",
    "\n",
    "# Compute RMSE\n",
    "rmse_test = mse_test ** 0.5\n",
    "\n",
    "# Print RMSE\n",
    "print(\"Test set RMSE of gb: {:.3f}\".format(rmse_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stochastic Gradient Boosting (SGB)\n",
    "- Gradient Boosting: Cons & Pros\n",
    "    - GB involves an exhaustive search procedure\n",
    "    - Each CART is trained to find the best split points and features.\n",
    "    - May lead to CARTs using the same split points and maybe the same features.\n",
    "- Stochastic Gradient Boosting\n",
    "    - Each tree is trained on a random subset of rows of the training data.\n",
    "    - The sampled instances (40%-80% of the training set) are sampled without replacement.\n",
    "    - Features are sampled (without replacement) when choosing split points\n",
    "    - Result: further ensemble diversity.\n",
    "    - Effect: adding further variance to the ensemble of trees.\n",
    "- Stochastic Gradient Boosting: Training\n",
    "![sgb_train](image/sgb_train.png)\n",
    "    - Residual errors are multiplied by the learning rate $\\eta$ and are fed to the next tree in ensemble.\n",
    "    - Process is repeated sequentially until all the trees in the ensemble are trained."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression with SGB\n",
    "As in the exercises from the previous lesson, you'll be working with the [Bike Sharing Demand](https://www.kaggle.com/c/bike-sharing-demand) dataset. In the following set of exercises, you'll solve this bike count regression problem using stochastic gradient boosting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "# Instantiate sgbr\n",
    "sgbr = GradientBoostingRegressor(max_depth=4, n_estimators=200, subsample=0.9, \n",
    "                                 max_features=0.75, random_state=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the SGB regressor\n",
    "In this exercise, you'll train the SGBR ```sgbr``` instantiated in the previous exercise and predict the test set labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit sgbr to the training set\n",
    "sgbr.fit(X_train, y_train)\n",
    "\n",
    "# Predict test set labels\n",
    "y_pred = sgbr.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the SGB regressor\n",
    "You have prepared the ground to determine the test set RMSE of ```sgbr``` which you shall evaluate in this exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set RMSE of sgbr: 47.944\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error as MSE\n",
    "\n",
    "# Compute test set MSE\n",
    "mse_test = MSE(y_test, y_pred)\n",
    "\n",
    "# Compute test set RMSE\n",
    "rmse_test = mse_test ** 0.5\n",
    "\n",
    "# Print rmse_test\n",
    "print('Test set RMSE of sgbr: {:.3f}'.format(rmse_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The stochastic gradient boosting regressor achieves a lower test set RMSE than the gradient boosting regressor (which was 52.065)!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Tuning\n",
    "> A Summary of lecture \"Machine Learning with Tree-Based Models in Python\", via datacamp\n",
    "\n",
    "- toc: true \n",
    "- badges: true\n",
    "- comments: true\n",
    "- author: Chanseok Kang\n",
    "- categories: [Python, Datacamp, Machine_Learning]\n",
    "- image: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuning a CART's Hyperparameters\n",
    "- Hyperparameters\n",
    "    - Machine learning model:\n",
    "        - parameters: learned from data\n",
    "            - CART example: split-point of a node, split-feature of a node, ...\n",
    "        - hyperparameters: not learned from data, set prior to training\n",
    "            - CART example: ```max_depth```, ```min_samples_leaf```, splitting criterion, ...\n",
    "- What is hyperparameter tuning?\n",
    "    - Problem: search for a set of optimal hyperparameters for a learning algorithm.\n",
    "    - Solution: find a set of optimal hyperparameters that results in an optimal model.\n",
    "    - Optimal model: yields an optimal score\n",
    "    - Score : defaults to accuracy (classification) and $R^2$ (regression)\n",
    "    - Cross-validation is used to estimate the generalization performance.\n",
    "- Approaches to hyperparameter tuning\n",
    "    - Grid Search\n",
    "    - Random Search\n",
    "    - Bayesian Optimization\n",
    "    - Genetic Algorithm\n",
    "    - ...\n",
    "- Grid search cross validation\n",
    "    - Manually set a grid of discrete hyperparameter values.\n",
    "    - Set a metric for scoring model performance.\n",
    "    - Search exhaustively through the grid.\n",
    "    - For each set of hyperparameters, evaluate each model's CV score\n",
    "    - The optimal hyperparameters are those of the model achieving the best CV score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tree hyperparameters\n",
    "In the following exercises you'll revisit the [Indian Liver Patient](https://www.kaggle.com/uciml/indian-liver-patient-records) dataset which was introduced in a previous chapter.\n",
    "\n",
    "Your task is to tune the hyperparameters of a classification tree. Given that this dataset is imbalanced, you'll be using the ROC AUC score as a metric instead of accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Age_std</th>\n",
       "      <th>Total_Bilirubin_std</th>\n",
       "      <th>Direct_Bilirubin_std</th>\n",
       "      <th>Alkaline_Phosphotase_std</th>\n",
       "      <th>Alamine_Aminotransferase_std</th>\n",
       "      <th>Aspartate_Aminotransferase_std</th>\n",
       "      <th>Total_Protiens_std</th>\n",
       "      <th>Albumin_std</th>\n",
       "      <th>Albumin_and_Globulin_Ratio_std</th>\n",
       "      <th>Is_male_std</th>\n",
       "      <th>Liver_disease</th>\n",
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       "      <th>0</th>\n",
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       "      <td>-0.428870</td>\n",
       "      <td>-0.355832</td>\n",
       "      <td>-0.319111</td>\n",
       "      <td>0.293722</td>\n",
       "      <td>0.203446</td>\n",
       "      <td>-0.147390</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.062306</td>\n",
       "      <td>1.218936</td>\n",
       "      <td>1.423518</td>\n",
       "      <td>1.675083</td>\n",
       "      <td>-0.093573</td>\n",
       "      <td>-0.035962</td>\n",
       "      <td>0.939655</td>\n",
       "      <td>0.077462</td>\n",
       "      <td>-0.648461</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.062306</td>\n",
       "      <td>0.640375</td>\n",
       "      <td>0.926017</td>\n",
       "      <td>0.816243</td>\n",
       "      <td>-0.115428</td>\n",
       "      <td>-0.146459</td>\n",
       "      <td>0.478274</td>\n",
       "      <td>0.203446</td>\n",
       "      <td>-0.178707</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.815511</td>\n",
       "      <td>-0.372106</td>\n",
       "      <td>-0.388807</td>\n",
       "      <td>-0.449416</td>\n",
       "      <td>-0.366760</td>\n",
       "      <td>-0.312205</td>\n",
       "      <td>0.293722</td>\n",
       "      <td>0.329431</td>\n",
       "      <td>0.165780</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.679294</td>\n",
       "      <td>0.093956</td>\n",
       "      <td>0.179766</td>\n",
       "      <td>-0.395996</td>\n",
       "      <td>-0.295731</td>\n",
       "      <td>-0.177537</td>\n",
       "      <td>0.755102</td>\n",
       "      <td>-0.930414</td>\n",
       "      <td>-1.713237</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age_std  Total_Bilirubin_std  Direct_Bilirubin_std  \\\n",
       "0  1.247403            -0.420320             -0.495414   \n",
       "1  1.062306             1.218936              1.423518   \n",
       "2  1.062306             0.640375              0.926017   \n",
       "3  0.815511            -0.372106             -0.388807   \n",
       "4  1.679294             0.093956              0.179766   \n",
       "\n",
       "   Alkaline_Phosphotase_std  Alamine_Aminotransferase_std  \\\n",
       "0                 -0.428870                     -0.355832   \n",
       "1                  1.675083                     -0.093573   \n",
       "2                  0.816243                     -0.115428   \n",
       "3                 -0.449416                     -0.366760   \n",
       "4                 -0.395996                     -0.295731   \n",
       "\n",
       "   Aspartate_Aminotransferase_std  Total_Protiens_std  Albumin_std  \\\n",
       "0                       -0.319111            0.293722     0.203446   \n",
       "1                       -0.035962            0.939655     0.077462   \n",
       "2                       -0.146459            0.478274     0.203446   \n",
       "3                       -0.312205            0.293722     0.329431   \n",
       "4                       -0.177537            0.755102    -0.930414   \n",
       "\n",
       "   Albumin_and_Globulin_Ratio_std  Is_male_std  Liver_disease  \n",
       "0                       -0.147390            0              1  \n",
       "1                       -0.648461            1              1  \n",
       "2                       -0.178707            1              1  \n",
       "3                        0.165780            1              1  \n",
       "4                       -1.713237            1              1  "
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indian = pd.read_csv('indian_liver_patient_preprocessed.csv', index_col=0)\n",
    "indian.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = indian.drop('Liver_disease', axis='columns')\n",
    "y = indian['Liver_disease']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': None,\n",
       " 'max_leaf_nodes': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_impurity_split': None,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'presort': False,\n",
       " 'random_state': None,\n",
       " 'splitter': 'best'}"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Instantiate dt\n",
    "dt = DecisionTreeClassifier()\n",
    "\n",
    "# Check default hyperparameter\n",
    "dt.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set the tree's hyperparameter grid\n",
    "In this exercise, you'll manually set the grid of hyperparameters that will be used to tune the classification tree ```dt``` and find the optimal classifier in the next exercise.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define params_dt\n",
    "params_dt = {\n",
    "    'max_depth': [2, 3, 4],\n",
    "    'min_samples_leaf': [0.12, 0.14, 0.16, 0.18],\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Search for the optimal tree\n",
    "In this exercise, you'll perform grid search using 5-fold cross validation to find ```dt```'s optimal hyperparameters. Note that because grid search is an exhaustive process, it may take a lot time to train the model. Here you'll only be instantiating the ```GridSearchCV``` object without fitting it to the training set. As discussed in the video, you can train such an object similar to any scikit-learn estimator by using the ```.fit()``` method:\n",
    "```python\n",
    "grid_object.fit(X_train, y_train)\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Instantiate grid_dt\n",
    "grid_dt = GridSearchCV(estimator=dt, param_grid=params_dt, scoring='roc_auc', cv=5, n_jobs=-1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the optimal tree\n",
    "In this exercise, you'll evaluate the test set ROC AUC score of grid_dt's optimal model.\n",
    "\n",
    "In order to do so, you will first determine the probability of obtaining the positive label for each test set observation. You can use the method ```predict_proba()``` of an sklearn classifier to compute a 2D array containing the probabilities of the negative and positive class-labels respectively along columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# Extract the best estimator\n",
    "best_model = grid_dt.best_estimator_\n",
    "\n",
    "# Predict the test set probabilities of the positive class\n",
    "y_pred_proba = best_model.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Compute test_roc_auc\n",
    "test_roc_auc = roc_auc_score(y_test, y_pred_proba)\n",
    "\n",
    "# Print test_roc_auc\n",
    "print(\"Test set ROC AUC score: {:.3f}\".format(test_roc_auc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An untuned classification-tree would achieve a ROC AUC score of 0.54!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuning a RF's Hyperparameters\n",
    "- Random Forest Hyperparameters\n",
    "    - CART hyperparameters\n",
    "    - number of estimators\n",
    "    - Whether it uses bootstrapping or not\n",
    "    - ...\n",
    "- Tuning is expensive\n",
    "    - Hyperparameter tuning:\n",
    "        - Computationally expensive,\n",
    "        - sometimes leads to very slight improvement\n",
    "    - Weight the impact of tuning on the whole project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random forests hyperparameters\n",
    "In the following exercises, you'll be revisiting the [Bike Sharing Demand](https://www.kaggle.com/c/bike-sharing-demand) dataset that was introduced in a previous chapter. Recall that your task is to predict the bike rental demand using historical weather data from the Capital Bikeshare program in Washington, D.C.. For this purpose, you'll be tuning the hyperparameters of a Random Forests regressor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bike = pd.read_csv('bikes.csv')\n",
    "bike.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = bike.drop('cnt', axis='columns')\n",
    "y = bike['cnt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# Instantiate rf\n",
    "rf = RandomForestRegressor()\n",
    "\n",
    "# Get hyperparameters\n",
    "rf.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set the hyperparameter grid of RF\n",
    "In this exercise, you'll manually set the grid of hyperparameters that will be used to tune ```rf```'s hyperparameters and find the optimal regressor. For this purpose, you will be constructing a grid of hyperparameters and tune the number of estimators, the maximum number of features used when splitting each node and the minimum number of samples (or fraction) per leaf."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the dicrionary 'params_rf'\n",
    "params_rf = {\n",
    "    'n_estimators': [100, 350, 500],\n",
    "    'max_features': ['log2', 'auto', 'sqrt'],\n",
    "    'min_samples_leaf': [2, 10, 30],\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Search for the optimal forest\n",
    "In this exercise, you'll perform grid search using 3-fold cross validation to find ```rf```'s optimal hyperparameters. To evaluate each model in the grid, you'll be using the negative mean squared error metric.\n",
    "\n",
    "Note that because grid search is an exhaustive search process, it may take a lot time to train the model. Here you'll only be instantiating the ```GridSearchCV``` object without fitting it to the training set. As discussed in the video, you can train such an object similar to any scikit-learn estimator by using the ```.fit()``` method:\n",
    "```python\n",
    "grid_object.fit(X_train, y_train)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Instantiate grid_rf\n",
    "grid_rf = GridSearchCV(estimator=rf, param_grid=params_rf, scoring='neg_mean_squared_error', cv=3,\n",
    "                      verbose=1, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the optimal forest\n",
    "In this last exercise of the course, you'll evaluate the test set RMSE of ```grid_rf```'s optimal model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'GridSearchCV' object has no attribute 'best_estimator_'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-294-b5962580b52f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m# Extract the best estimator\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mbest_model\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrid_rf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;31m# Predict test set labels\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'GridSearchCV' object has no attribute 'best_estimator_'"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error as MSE\n",
    "\n",
    "# Extract the best estimator\n",
    "best_model = grid_rf.best_estimator_\n",
    "\n",
    "# Predict test set labels\n",
    "y_pred = best_model.predict(X_test)\n",
    "\n",
    "# Compute rmse_test\n",
    "rmse_test = MSE(y_test, y_pred) ** 0.5\n",
    "\n",
    "# Print rmse_test\n",
    "print('Test RMSE of best model: {:.3f}'.format(rmse_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  }
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