Decision Tree.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dbda2831",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import warnings as wr\n",
    "wr.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7060770b",
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the Iris dataset from scikit-learn\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "# Load the dataset\n",
    "iris = load_iris()\n",
    "\n",
    "# Create a DataFrame from the iris dataset\n",
    "iris_df = pd.DataFrame(data=iris.data, columns=iris.feature_names)\n",
    "\n",
    "# Add the target variable to the DataFrame\n",
    "iris_df['target'] = iris.target\n",
    "\n",
    "# Print the first few rows of the DataFrame\n",
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "86afc5df",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris_df.drop('target', axis =1)\n",
    "y = iris_df[['target']]\n",
    "feature_names = iris.feature_names\n",
    "class_names = iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "63fbe886",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=2, random_state=4)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "tree_clf = DecisionTreeClassifier(max_depth = 2, random_state=4)\n",
    "tree_clf.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "59f128b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import export_graphviz\n",
    "from sklearn import tree\n",
    "import graphviz\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7ada492b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export the decision tree as a dot file\n",
    "dot_data = export_graphviz(tree_clf, out_file=None,\n",
    "                           feature_names=feature_names,\n",
    "                           class_names=class_names,\n",
    "                           filled=True, rounded=True,\n",
    "                           special_characters=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5e5dd9c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a graph from the dot data\n",
    "graph = graphviz.Source(dot_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "31b6df61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save the decision tree as an image file\n",
    "graph.format = 'png'\n",
    "graph.render('decision_tree')\n",
    "\n",
    "# Display the decision tree image\n",
    "Image('decision_tree.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f4f59b20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.90740741, 0.09259259]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf.predict_proba(X.iloc[[55]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b8d2d40c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf.predict(X.iloc[[55]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deeecf42",
   "metadata": {},
   "source": [
    "## Regression Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d57a191e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Quadratic training set + noise\n",
    "np.random.seed(42)\n",
    "m = 200\n",
    "X = np.random.rand(m, 1)\n",
    "y = 4 * (X - 0.5) ** 2\n",
    "y = y + np.random.randn(m, 1) / 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b4b3e845",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=2, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=2, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor(max_depth=2, random_state=42)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "tree_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "50af73d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'max_depth=3')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg1 = DecisionTreeRegressor(random_state=42, max_depth=2)\n",
    "tree_reg2 = DecisionTreeRegressor(random_state=42, max_depth=3)\n",
    "tree_reg1.fit(X, y)\n",
    "tree_reg2.fit(X, y)\n",
    "\n",
    "def plot_regression_predictions(tree_reg, X, y, axes=[0, 1, -0.2, 1], ylabel=\"$y$\"):\n",
    "    x1 = np.linspace(axes[0], axes[1], 500).reshape(-1, 1)\n",
    "    y_pred = tree_reg.predict(x1)\n",
    "    plt.axis(axes)\n",
    "    plt.xlabel(\"$x_1$\", fontsize=18)\n",
    "    if ylabel:\n",
    "        plt.ylabel(ylabel, fontsize=18, rotation=0)\n",
    "    plt.plot(X, y, \"b.\")\n",
    "    plt.plot(x1, y_pred, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "plt.subplot(121)\n",
    "plot_regression_predictions(tree_reg1, X, y)\n",
    "for split, style in ((0.1973, \"k-\"), (0.0917, \"k--\"), (0.7718, \"k--\")):\n",
    "    plt.plot([split, split], [-0.2, 1], style, linewidth=2)\n",
    "plt.text(0.21, 0.65, \"Depth=0\", fontsize=15)\n",
    "plt.text(0.01, 0.2, \"Depth=1\", fontsize=13)\n",
    "plt.text(0.65, 0.8, \"Depth=1\", fontsize=13)\n",
    "plt.legend(loc=\"upper center\", fontsize=18)\n",
    "plt.title(\"max_depth=2\", fontsize=14)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_regression_predictions(tree_reg2, X, y, ylabel=None)\n",
    "for split, style in ((0.1973, \"k-\"), (0.0917, \"k--\"), (0.7718, \"k--\")):\n",
    "    plt.plot([split, split], [-0.2, 1], style, linewidth=2)\n",
    "for split in (0.0458, 0.1298, 0.2873, 0.9040):\n",
    "    plt.plot([split, split], [-0.2, 1], \"k:\", linewidth=1)\n",
    "plt.text(0.3, 0.5, \"Depth=2\", fontsize=13)\n",
    "plt.title(\"max_depth=3\", fontsize=14)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "11a69660",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'digraph Tree {\\nnode [shape=box, style=\"filled, rounded\", color=\"black\", fontname=\"helvetica\"] ;\\nedge [fontname=\"helvetica\"] ;\\n0 [label=\"x1 <= 0.197\\\\nsquared_error = 0.098\\\\nsamples = 200\\\\nvalue = 0.354\", fillcolor=\"#f6d6be\"] ;\\n1 [label=\"x1 <= 0.092\\\\nsquared_error = 0.038\\\\nsamples = 44\\\\nvalue = 0.689\", fillcolor=\"#eb9d65\"] ;\\n0 -> 1 [labeldistance=2.5, labelangle=45, headlabel=\"True\"] ;\\n2 [label=\"squared_error = 0.018\\\\nsamples = 20\\\\nvalue = 0.854\", fillcolor=\"#e58139\"] ;\\n1 -> 2 ;\\n3 [label=\"squared_error = 0.013\\\\nsamples = 24\\\\nvalue = 0.552\", fillcolor=\"#f0b489\"] ;\\n1 -> 3 ;\\n4 [label=\"x1 <= 0.772\\\\nsquared_error = 0.074\\\\nsamples = 156\\\\nvalue = 0.259\", fillcolor=\"#fae6d7\"] ;\\n0 -> 4 [labeldistance=2.5, labelangle=-45, headlabel=\"False\"] ;\\n5 [label=\"squared_error = 0.015\\\\nsamples = 110\\\\nvalue = 0.111\", fillcolor=\"#ffffff\"] ;\\n4 -> 5 ;\\n6 [label=\"squared_error = 0.036\\\\nsamples = 46\\\\nvalue = 0.615\", fillcolor=\"#edaa79\"] ;\\n4 -> 6 ;\\n}'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "export_graphviz(\n",
    "        tree_reg1,\n",
    "        out_file=None,\n",
    "        feature_names=[\"x1\"],\n",
    "        rounded=True,\n",
    "        filled=True\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "75b2c003",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'min_samples_leaf=10')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree_reg1 = DecisionTreeRegressor(random_state=42)\n",
    "tree_reg2 = DecisionTreeRegressor(random_state=42, min_samples_leaf=10)\n",
    "tree_reg1.fit(X, y)\n",
    "tree_reg2.fit(X, y)\n",
    "\n",
    "x1 = np.linspace(0, 1, 500).reshape(-1, 1)\n",
    "y_pred1 = tree_reg1.predict(x1)\n",
    "y_pred2 = tree_reg2.predict(x1)\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(X, y, \"b.\")\n",
    "plt.plot(x1, y_pred1, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "plt.axis([0, 1, -0.2, 1.1])\n",
    "plt.xlabel(\"$x_1$\", fontsize=18)\n",
    "plt.ylabel(\"$y$\", fontsize=18, rotation=0)\n",
    "plt.legend(loc=\"upper center\", fontsize=18)\n",
    "plt.title(\"No restrictions\", fontsize=14)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X, y, \"b.\")\n",
    "plt.plot(x1, y_pred2, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "plt.axis([0, 1, -0.2, 1.1])\n",
    "plt.xlabel(\"$x_1$\", fontsize=18)\n",
    "plt.title(\"min_samples_leaf={}\".format(tree_reg2.min_samples_leaf), fontsize=14)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6da24d4",
   "metadata": {},
   "source": [
    "### Train and fine-tune a Decision Tree for the moons dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4dfa9cb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "\n",
    "X, y = make_moons(n_samples=10000, noise=0.4, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9d2f6b5e",
   "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=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "670eeb8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 294 candidates, totalling 882 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "             param_grid={&#x27;max_leaf_nodes&#x27;: [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
       "                                            13, 14, 15, 16, 17, 18, 19, 20, 21,\n",
       "                                            22, 23, 24, 25, 26, 27, 28, 29, 30,\n",
       "                                            31, ...],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 3, 4]},\n",
       "             verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, estimator=DecisionTreeClassifier(random_state=42), n_jobs=-1,\n",
       "             param_grid={&#x27;max_leaf_nodes&#x27;: [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
       "                                            13, 14, 15, 16, 17, 18, 19, 20, 21,\n",
       "                                            22, 23, 24, 25, 26, 27, 28, 29, 30,\n",
       "                                            31, ...],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 3, 4]},\n",
       "             verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=3, estimator=DecisionTreeClassifier(random_state=42), n_jobs=-1,\n",
       "             param_grid={'max_leaf_nodes': [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
       "                                            13, 14, 15, 16, 17, 18, 19, 20, 21,\n",
       "                                            22, 23, 24, 25, 26, 27, 28, 29, 30,\n",
       "                                            31, ...],\n",
       "                         'min_samples_split': [2, 3, 4]},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "params = {'max_leaf_nodes': list(range(2, 100)), 'min_samples_split': [2, 3, 4]}\n",
    "grid_search_cv = GridSearchCV(DecisionTreeClassifier(random_state=42), params, n_jobs=-1, verbose=1, cv=3)\n",
    "\n",
    "grid_search_cv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d2557f2",
   "metadata": {},
   "source": [
    "- **n_jobs:** This parameter controls the number of jobs to run in parallel for fitting the models and performing cross-validation. By setting n_jobs to an integer value greater than 1 (e.g., n_jobs=-1), it allows the grid search to utilize multiple CPU cores, enabling faster execution when dealing with a large number of models or a large dataset. A value of -1 typically indicates using all available CPU cores, but you can also specify a specific number.\n",
    "\n",
    "- **verbose:** This parameter controls the verbosity of the output during the grid search process. By setting verbose to a non-zero value, it provides additional information and progress updates about the grid search. The higher the value, the more detailed the output. For example, verbose=1 displays a progress bar, while verbose=2 provides detailed logging for each model fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "eb184d01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_leaf_nodes=17, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_leaf_nodes=17, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_leaf_nodes=17, random_state=42)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "33fd9929",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8695"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "y_pred = grid_search_cv.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc382e57",
   "metadata": {},
   "source": [
    "### Growing a Forest\n",
    "\n",
    "- Continuing the previous exercise, generate 1,000 subsets of the training set, each containing 100 instances selected randomly. Hint: you can use Scikit-Learn's ShuffleSplit class for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dc8fdffd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5e744bf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import ShuffleSplit\n",
    "\n",
    "n_trees = 1000\n",
    "n_instances = 100\n",
    "\n",
    "mini_sets = []\n",
    "\n",
    "rs = ShuffleSplit(n_splits=n_trees, test_size=len(X_train) - n_instances, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "634a25b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "for mini_train_index, mini_test_index in rs.split(X_train):\n",
    "    X_mini_train = X_train[mini_train_index]\n",
    "    y_mini_train = y_train[mini_train_index]\n",
    "    mini_sets.append((X_mini_train, y_mini_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "dfa2842b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 2)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_mini_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0473eb2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mini_sets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f72361bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.805471"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.base import clone\n",
    "\n",
    "forest = [clone(grid_search_cv.best_estimator_) for _ in range(n_trees)]\n",
    "\n",
    "accuracy_scores = []\n",
    "\n",
    "for tree, (X_mini_train, y_mini_train) in zip(forest, mini_sets):\n",
    "    tree.fit(X_mini_train, y_mini_train)\n",
    "    \n",
    "    y_pred = tree.predict(X_test)\n",
    "    accuracy_scores.append(accuracy_score(y_test, y_pred))\n",
    "\n",
    "np.mean(accuracy_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cc780c4",
   "metadata": {},
   "source": [
    "- Now comes the magic. For each test set instance, generate the predictions of the 1,000 Decision Trees, and keep only the most frequent prediction (you can use SciPy's mode() function for this). This gives you majority-vote predictions over the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "92046285",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = np.empty([n_trees, len(X_test)], dtype=np.uint8)\n",
    "\n",
    "for tree_index, tree in enumerate(forest):\n",
    "    Y_pred[tree_index] = tree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "86421cdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import mode\n",
    "\n",
    "y_pred_majority_votes, n_votes = mode(Y_pred, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3c447e8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 0, ..., 0, 0, 0]], dtype=uint8)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_majority_votes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "84849c45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[951, 912, 963, ..., 919, 994, 603]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_votes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7563b3ec",
   "metadata": {},
   "source": [
    "- Evaluate these predictions on the test set: you should obtain a slightly higher accuracy than your first model (about 0.5 to 1.5% higher). Congratulations, you have trained a Random Forest classifier!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3ef8263f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.872"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred_majority_votes.reshape([-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6654c4fa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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