{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "45278a2a",
   "metadata": {},
   "source": [
    "(chapter8_part2)=\n",
    "\n",
    "\n",
    "## Decision Trees in Classification\n",
    "\n",
    "- This is a supplement material for the [Machine Learning Simplified](https://themlsbook.com) book. It sheds light on Python implementations of the topics discussed while all detailed explanations can be found in the book. \n",
    "- I also assume you know Python syntax and how it works. If you don't, I highly recommend you to take a break and get introduced to the language before going forward with my code. \n",
    "- This material can be downloaded as a Jupyter notebook (Download button in the upper-right corner -> `.ipynb`) to reproduce the code and play around with it. \n",
    "\n",
    "\n",
    "This notebook is a supplement for *Chapter 8. Decision Trees* of **Machine Learning For Everyone** book.\n",
    "\n",
    "## 1. Required Libraries, Data & Variables\n",
    "\n",
    "Let's import the data and have a look at it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e3bf2e2d",
   "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>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.25</td>\n",
       "      <td>1.41</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.60</td>\n",
       "      <td>0.39</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.71</td>\n",
       "      <td>1.29</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.20</td>\n",
       "      <td>2.30</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.75</td>\n",
       "      <td>0.59</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.26</td>\n",
       "      <td>1.70</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.50</td>\n",
       "      <td>1.35</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.50</td>\n",
       "      <td>2.90</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.88</td>\n",
       "      <td>0.61</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.91</td>\n",
       "      <td>2.00</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     x1    x2  Color\n",
       "0  0.25  1.41   blue\n",
       "1  0.60  0.39   blue\n",
       "2  0.71  1.29   blue\n",
       "3  1.20  2.30   blue\n",
       "4  1.75  0.59   blue\n",
       "5  2.26  1.70  green\n",
       "6  2.50  1.35  green\n",
       "7  2.50  2.90  green\n",
       "8  2.88  0.61  green\n",
       "9  2.91  2.00  green"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%config InlineBackend.figure_format = 'retina' #to make sharper and prettier plots\n",
    "\n",
    "# Creating the DataFrame based on the provided data\n",
    "data = {\n",
    "    'x1': [0.25, 0.60, 0.71, 1.20, 1.75, 2.26, 2.50, 2.50, 2.88, 2.91],\n",
    "    'x2': [1.41, 0.39, 1.29, 2.30, 0.59, 1.70, 1.35, 2.90, 0.61, 2.00],\n",
    "    'Color': ['blue', 'blue', 'blue', 'blue', 'blue', 'green', 'green', 'green', 'green', 'green']\n",
    "}\n",
    "\n",
    "# Convert the dictionary to a pandas DataFrame\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Display the DataFrame\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d0d5d26",
   "metadata": {},
   "source": [
    "## 2. Visualizing Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "018e4001",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 454,
       "width": 571
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting\n",
    "fig, ax = plt.subplots()\n",
    "colors = {'blue': 'blue', 'green': 'green'}\n",
    "\n",
    "# Group by color and then plot each group\n",
    "for key, group in df.groupby('Color'):\n",
    "    group.plot(ax=ax, kind='scatter', x='x1', y='x2', label=key, color=colors[key])\n",
    "\n",
    "# Setting plot labels and title\n",
    "ax.set_xlabel('x1')\n",
    "ax.set_ylabel('x2')\n",
    "ax.set_title('Scatter Plot of Colors')\n",
    "\n",
    "# Display the legend\n",
    "ax.legend(title='Point Color')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b107274b",
   "metadata": {},
   "source": [
    "## 3. Preprocessing Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a013cdab",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Color'] = df['Color'].map({'blue': 0, 'green': 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2863845a",
   "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>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.25</td>\n",
       "      <td>1.41</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.60</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.71</td>\n",
       "      <td>1.29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.20</td>\n",
       "      <td>2.30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.75</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.26</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.50</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.50</td>\n",
       "      <td>2.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.88</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.91</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     x1    x2  Color\n",
       "0  0.25  1.41      0\n",
       "1  0.60  0.39      0\n",
       "2  0.71  1.29      0\n",
       "3  1.20  2.30      0\n",
       "4  1.75  0.59      0\n",
       "5  2.26  1.70      1\n",
       "6  2.50  1.35      1\n",
       "7  2.50  2.90      1\n",
       "8  2.88  0.61      1\n",
       "9  2.91  2.00      1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dd74f94",
   "metadata": {},
   "source": [
    "## 4. Training a Decision Tree with Gini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4111c236",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bdfa1c7",
   "metadata": {},
   "source": [
    "#### 4.1. Splitting into X and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0964d844",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['x1', 'x2']]\n",
    "y = df['Color']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8f48f53",
   "metadata": {},
   "source": [
    "#### 4.2. Building the Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a36a8169",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" 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>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(random_state=42)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_classifier = DecisionTreeClassifier(criterion='gini', random_state=42)\n",
    "tree_classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fe81c9b",
   "metadata": {},
   "source": [
    "#### 4.3. Predict and Evaluate the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "32549979",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the decision tree model: 1.0\n"
     ]
    }
   ],
   "source": [
    "y_pred = tree_classifier.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy of the decision tree model:\", accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46c97eed",
   "metadata": {},
   "source": [
    "#### 4.4. Visualize the Tree (optional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e8a7ce47",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "import graphviz\n",
    "\n",
    "dot_data = tree.export_graphviz(tree_classifier, out_file=None, \n",
    "                                feature_names=['x1', 'x2'],  \n",
    "                                class_names=['blue', 'green'],\n",
    "                                filled=True, rounded=True,  \n",
    "                                special_characters=True)  \n",
    "graph = graphviz.Source(dot_data)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "928cbc1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 12.0.0 (20240704.0754)\n",
       " -->\n",
       "<!-- Title: Tree Pages: 1 -->\n",
       "<svg width=\"218pt\" height=\"195pt\"\n",
       " viewBox=\"0.00 0.00 217.50 195.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 191)\">\n",
       "<title>Tree</title>\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-191 213.5,-191 213.5,4 -4,4\"/>\n",
       "<!-- 0 -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>0</title>\n",
       "<path fill=\"#ffffff\" stroke=\"black\" d=\"M139,-187C139,-187 69,-187 69,-187 63,-187 57,-181 57,-175 57,-175 57,-116 57,-116 57,-110 63,-104 69,-104 69,-104 139,-104 139,-104 145,-104 151,-110 151,-116 151,-116 151,-175 151,-175 151,-181 145,-187 139,-187\"/>\n",
       "<text text-anchor=\"start\" x=\"72.5\" y=\"-169.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x1 ≤ 2.005</text>\n",
       "<text text-anchor=\"start\" x=\"76.25\" y=\"-154.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"66.88\" y=\"-139.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 8</text>\n",
       "<text text-anchor=\"start\" x=\"65\" y=\"-124.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [4, 4]</text>\n",
       "<text text-anchor=\"start\" x=\"68\" y=\"-109.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = blue</text>\n",
       "</g>\n",
       "<!-- 1 -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>1</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M82,-68C82,-68 12,-68 12,-68 6,-68 0,-62 0,-56 0,-56 0,-12 0,-12 0,-6 6,0 12,0 12,0 82,0 82,0 88,0 94,-6 94,-12 94,-12 94,-56 94,-56 94,-62 88,-68 82,-68\"/>\n",
       "<text text-anchor=\"start\" x=\"19.25\" y=\"-50.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"9.88\" y=\"-35.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4</text>\n",
       "<text text-anchor=\"start\" x=\"8\" y=\"-20.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [4, 0]</text>\n",
       "<text text-anchor=\"start\" x=\"11\" y=\"-5.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = blue</text>\n",
       "</g>\n",
       "<!-- 0&#45;&gt;1 -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>0&#45;&gt;1</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M82.78,-103.73C78.45,-95.43 73.9,-86.67 69.53,-78.28\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"72.75,-76.9 65.03,-69.64 66.55,-80.13 72.75,-76.9\"/>\n",
       "<text text-anchor=\"middle\" x=\"56.82\" y=\"-86.34\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">True</text>\n",
       "</g>\n",
       "<!-- 2 -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>2</title>\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M197.5,-68C197.5,-68 124.5,-68 124.5,-68 118.5,-68 112.5,-62 112.5,-56 112.5,-56 112.5,-12 112.5,-12 112.5,-6 118.5,0 124.5,0 124.5,0 197.5,0 197.5,0 203.5,0 209.5,-6 209.5,-12 209.5,-12 209.5,-56 209.5,-56 209.5,-62 203.5,-68 197.5,-68\"/>\n",
       "<text text-anchor=\"start\" x=\"133.25\" y=\"-50.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"123.88\" y=\"-35.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4</text>\n",
       "<text text-anchor=\"start\" x=\"122\" y=\"-20.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 4]</text>\n",
       "<text text-anchor=\"start\" x=\"120.5\" y=\"-5.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = green</text>\n",
       "</g>\n",
       "<!-- 0&#45;&gt;2 -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>0&#45;&gt;2</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M125.22,-103.73C129.55,-95.43 134.1,-86.67 138.47,-78.28\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"141.45,-80.13 142.97,-69.64 135.25,-76.9 141.45,-80.13\"/>\n",
       "<text text-anchor=\"middle\" x=\"151.18\" y=\"-86.34\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">False</text>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.sources.Source at 0x179c72ce0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize the decision tree\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5246d56a",
   "metadata": {},
   "source": [
    "## 5. Training a Decision Tree with Entropy\n",
    "\n",
    "#### 4.1. Splitting into X and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c33aa131",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['x1', 'x2']]\n",
    "y = df['Color']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f28e878",
   "metadata": {},
   "source": [
    "#### 4.2. Building the Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7e70ffdf",
   "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>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, 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\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', random_state=42)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_classifier = DecisionTreeClassifier(criterion='entropy', random_state=42)\n",
    "tree_classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffbb9ce4",
   "metadata": {},
   "source": [
    "#### 4.3. Predict and Evaluate the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e4b8c2f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the decision tree model: 1.0\n"
     ]
    }
   ],
   "source": [
    "y_pred = tree_classifier.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy of the decision tree model:\", accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dfbb74c",
   "metadata": {},
   "source": [
    "#### 4.4. Visualize the Tree (optional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b6781767",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "import graphviz\n",
    "\n",
    "dot_data = tree.export_graphviz(tree_classifier, out_file=None, \n",
    "                                feature_names=['x1', 'x2'],  \n",
    "                                class_names=['blue', 'green'],\n",
    "                                filled=True, rounded=True,  \n",
    "                                special_characters=True)  \n",
    "graph = graphviz.Source(dot_data)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cf58ec4e",
   "metadata": {},
   "outputs": [
    {
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       "</g>\n",
       "<!-- 0&#45;&gt;1 -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>0&#45;&gt;1</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M83.53,-103.73C79.2,-95.43 74.65,-86.67 70.28,-78.28\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"73.5,-76.9 65.78,-69.64 67.3,-80.13 73.5,-76.9\"/>\n",
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize the decision tree\n",
    "graph"
   ]
  }
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