Decision_tree_post_pruning.ipynb

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      "name": "python3",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Harshika-123/harshika/blob/master/Decision_tree_post_pruning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "CbeY14GJoOil"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris"
      ],
      "metadata": {
        "id": "anQ6M5Ytonht"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "iris=load_iris()"
      ],
      "metadata": {
        "id": "iHW1XFXpo0Xm"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "iris"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mvW7zh03o8d2",
        "outputId": "3ebedc25-06ab-4086-d6a4-1e8dd4800fa7"
      },
      "execution_count": 4,
      "outputs": [
        {
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              " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),\n",
              " 'frame': None,\n",
              " 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'),\n",
              " 'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\n:Number of Instances: 150 (50 in each of three classes)\\n:Number of Attributes: 4 numeric, predictive attributes and the class\\n:Attribute Information:\\n    - sepal length in cm\\n    - sepal width in cm\\n    - petal length in cm\\n    - petal width in cm\\n    - class:\\n            - Iris-Setosa\\n            - Iris-Versicolour\\n            - Iris-Virginica\\n\\n:Summary Statistics:\\n\\n============== ==== ==== ======= ===== ====================\\n                Min  Max   Mean    SD   Class Correlation\\n============== ==== ==== ======= ===== ====================\\nsepal length:   4.3  7.9   5.84   0.83    0.7826\\nsepal width:    2.0  4.4   3.05   0.43   -0.4194\\npetal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\npetal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\\n============== ==== ==== ======= ===== ====================\\n\\n:Missing Attribute Values: None\\n:Class Distribution: 33.3% for each of 3 classes.\\n:Creator: R.A. Fisher\\n:Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n:Date: July, 1988\\n\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n.. dropdown:: References\\n\\n  - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\\n    Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n    Mathematical Statistics\" (John Wiley, NY, 1950).\\n  - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n    (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n  - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n    Structure and Classification Rule for Recognition in Partially Exposed\\n    Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n    Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n  - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n    on Information Theory, May 1972, 431-433.\\n  - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n    conceptual clustering system finds 3 classes in the data.\\n  - Many, many more ...\\n',\n",
              " 'feature_names': ['sepal length (cm)',\n",
              "  'sepal width (cm)',\n",
              "  'petal length (cm)',\n",
              "  'petal width (cm)'],\n",
              " 'filename': 'iris.csv',\n",
              " 'data_module': 'sklearn.datasets.data'}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "OUiNiIZmo9vP"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=sns.load_dataset('iris')"
      ],
      "metadata": {
        "id": "NTiFUWKZpS5e"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#independent and dependent feature\n",
        "X=df.iloc[:,:-1]\n",
        "y=iris.target"
      ],
      "metadata": {
        "id": "PJYfc0AApW-V"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X,y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AyB1hycJp3mL",
        "outputId": "f444e8f4-38c8-4bf6-8713-462367b8b6ab"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(     sepal_length  sepal_width  petal_length  petal_width\n",
              " 0             5.1          3.5           1.4          0.2\n",
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              " 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",
              " 145           6.7          3.0           5.2          2.3\n",
              " 146           6.3          2.5           5.0          1.9\n",
              " 147           6.5          3.0           5.2          2.0\n",
              " 148           6.2          3.4           5.4          2.3\n",
              " 149           5.9          3.0           5.1          1.8\n",
              " \n",
              " [150 rows x 4 columns],\n",
              " array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]))"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#train test split\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test= train_test_split(X, y, test_size= 0.33, random_state=42)"
      ],
      "metadata": {
        "id": "FoXR9CHKqK_d"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "zxdbB3nbqeoM",
        "outputId": "d1fab676-9bed-4f02-9187-e48b8fc61910"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width\n",
              "96            5.7          2.9           4.2          1.3\n",
              "105           7.6          3.0           6.6          2.1\n",
              "66            5.6          3.0           4.5          1.5\n",
              "0             5.1          3.5           1.4          0.2\n",
              "122           7.7          2.8           6.7          2.0\n",
              "..            ...          ...           ...          ...\n",
              "71            6.1          2.8           4.0          1.3\n",
              "106           4.9          2.5           4.5          1.7\n",
              "14            5.8          4.0           1.2          0.2\n",
              "92            5.8          2.6           4.0          1.2\n",
              "102           7.1          3.0           5.9          2.1\n",
              "\n",
              "[100 rows x 4 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>5.7</td>\n",
              "      <td>2.9</td>\n",
              "      <td>4.2</td>\n",
              "      <td>1.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>7.6</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.6</td>\n",
              "      <td>2.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>5.6</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>7.7</td>\n",
              "      <td>2.8</td>\n",
              "      <td>6.7</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>6.1</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>4.9</td>\n",
              "      <td>2.5</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>5.8</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>5.8</td>\n",
              "      <td>2.6</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>7.1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.9</td>\n",
              "      <td>2.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6af4bf03-d596-4f20-a2cf-3581e99e2daa')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6af4bf03-d596-4f20-a2cf-3581e99e2daa button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6af4bf03-d596-4f20-a2cf-3581e99e2daa');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d0885f10-5e60-4bc1-89ef-c62776ed7bcf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d0885f10-5e60-4bc1-89ef-c62776ed7bcf')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d0885f10-5e60-4bc1-89ef-c62776ed7bcf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_606bc39b-9416-4a96-86e1-5332db60cedd\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X_train')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_606bc39b-9416-4a96-86e1-5332db60cedd button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('X_train');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_train",
              "summary": "{\n  \"name\": \"X_train\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8353762836910894,\n        \"min\": 4.3,\n        \"max\": 7.7,\n        \"num_unique_values\": 34,\n        \"samples\": [\n          6.4,\n          6.9,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4201719224846473,\n        \"min\": 2.0,\n        \"max\": 4.2,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          2.9,\n          2.6,\n          2.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7361701727327505,\n        \"min\": 1.1,\n        \"max\": 6.7,\n        \"num_unique_values\": 38,\n        \"samples\": [\n          3.9,\n          5.8,\n          6.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7472096240160604,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          1.3,\n          2.3,\n          1.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "WeTUz0v2qhBf"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#postPruning\n",
        "treemodel=DecisionTreeClassifier(max_depth=2)"
      ],
      "metadata": {
        "id": "u-A4LHLnqpao"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "treemodel.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "o6J-CRtVrWDF",
        "outputId": "38a2902c-86e2-4540-aa2f-b976844ec769"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=2)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=2)</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=2)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import tree\n",
        "plt.figure(figsize=(15,10))\n",
        "tree.plot_tree(treemodel,filled=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "id": "KLNVr_JprdmU",
        "outputId": "4de568f9-7017-4af8-ee1e-772a20c32f69"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.4, 0.8333333333333334, 'x[3] <= 0.8\\ngini = 0.666\\nsamples = 100\\nvalue = [31, 35, 34]'),\n",
              " Text(0.2, 0.5, 'gini = 0.0\\nsamples = 31\\nvalue = [31, 0, 0]'),\n",
              " Text(0.30000000000000004, 0.6666666666666667, 'True  '),\n",
              " Text(0.6, 0.5, 'x[3] <= 1.75\\ngini = 0.5\\nsamples = 69\\nvalue = [0, 35, 34]'),\n",
              " Text(0.5, 0.6666666666666667, '  False'),\n",
              " Text(0.4, 0.16666666666666666, 'gini = 0.188\\nsamples = 38\\nvalue = [0, 34, 4]'),\n",
              " Text(0.8, 0.16666666666666666, 'gini = 0.062\\nsamples = 31\\nvalue = [0, 1, 30]')]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=treemodel.predict(X_test)"
      ],
      "metadata": {
        "id": "HcdsVJ8Tr8oV"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tVB63MRutQ6V",
        "outputId": "75a9699a-e0f7-4876-c415-7601459572ef"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 2, 1, 1, 0, 1, 2, 1, 1, 2, 0, 0, 0, 0, 1, 2, 1, 1, 2, 0, 2,\n",
              "       0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 1, 0, 0, 2, 1, 0, 0, 0, 2, 1, 1, 0,\n",
              "       0, 1, 1, 2, 1, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score,classification_report"
      ],
      "metadata": {
        "id": "OrwDV3AOtT0O"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "score=accuracy_score(y_test,y_pred)"
      ],
      "metadata": {
        "id": "OhENKSOatcj_"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DQ4c4gQ4tfAY",
        "outputId": "14ae361b-0b3e-4df3-e5c9-5fa84cf3783a"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.98\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OTQsKyIpthgZ",
        "outputId": "30ac7fb0-2479-4fe3-d38c-b0b43b73e960"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        19\n",
            "           1       0.94      1.00      0.97        15\n",
            "           2       1.00      0.94      0.97        16\n",
            "\n",
            "    accuracy                           0.98        50\n",
            "   macro avg       0.98      0.98      0.98        50\n",
            "weighted avg       0.98      0.98      0.98        50\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3HMYmhnQtlbM"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}