{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_6IjlSbqz21Z"
      },
      "source": [
        "# Ансамбли моделей"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FOqDrFJdJYEX"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set(context=\"notebook\", style=\"white\", rc={\"axes.axisbelow\": False, 'figure.figsize':(11.7,8.27)})\n",
        "\n",
        "from sklearn import tree\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.linear_model import LogisticRegression"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yMrxYJt7rDpj"
      },
      "source": [
        "## Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DW-U4oVVuMsV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "8ef74b63-88f6-48f9-9d71-eed8a0e8027c"
      },
      "source": [
        "df = sns.load_dataset('iris')\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width species\n",
              "0           5.1          3.5           1.4          0.2  setosa\n",
              "1           4.9          3.0           1.4          0.2  setosa\n",
              "2           4.7          3.2           1.3          0.2  setosa\n",
              "3           4.6          3.1           1.5          0.2  setosa\n",
              "4           5.0          3.6           1.4          0.2  setosa"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</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>setosa</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>setosa</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>setosa</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>setosa</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>setosa</td>\n",
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              "\n",
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              "        document.querySelector('#df-5323fd81-73bd-471d-84f4-6cfc62fb4539 button.colab-df-convert');\n",
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              "        const element = document.querySelector('#df-5323fd81-73bd-471d-84f4-6cfc62fb4539');\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",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
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              "      --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",
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              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "    }\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",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.435866284936698,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7652982332594667,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7622376689603465,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"setosa\",\n          \"versicolor\",\n          \"virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lYnI4VhGvyBN"
      },
      "source": [
        "df = df[['sepal_length', 'sepal_width', 'species']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u0Y9FJx6xJbP"
      },
      "source": [
        "df['species'] = df['species'].map({'setosa': 0, 'versicolor': 1, 'virginica': 2})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "SwqZWkXLyBBf",
        "outputId": "7b1d7807-383c-4481-9b3a-d49880d3dc78"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     sepal_length  sepal_width  species\n",
              "0             5.1          3.5        0\n",
              "1             4.9          3.0        0\n",
              "2             4.7          3.2        0\n",
              "3             4.6          3.1        0\n",
              "4             5.0          3.6        0\n",
              "..            ...          ...      ...\n",
              "145           6.7          3.0        2\n",
              "146           6.3          2.5        2\n",
              "147           6.5          3.0        2\n",
              "148           6.2          3.4        2\n",
              "149           5.9          3.0        2\n",
              "\n",
              "[150 rows x 3 columns]"
            ],
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              "\n",
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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>species</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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 3 columns</p>\n",
              "</div>\n",
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              "\n",
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              "            style=\"display:none;\">\n",
              "\n",
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              "    </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-802bda37-ba6d-42bb-a4d0-728b53c2a63c 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-802bda37-ba6d-42bb-a4d0-728b53c2a63c');\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-d4466948-1649-4931-8eba-9bd8d1a55923\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d4466948-1649-4931-8eba-9bd8d1a55923')\"\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-d4466948-1649-4931-8eba-9bd8d1a55923 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.435866284936698,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RbcGO4dRr4Le",
        "outputId": "5decea20-b427-41d1-b228-bbb47786ec65"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5oZgB12qrSr5"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "train, test = train_test_split(df, test_size=0.15, random_state=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ExkIPWOsFb7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1917936d-3aad-43f7-c408-2bf03cad586c"
      },
      "source": [
        "X_train = train.drop('species',axis=1)\n",
        "y_train = train['species']\n",
        "\n",
        "X_test = test.drop('species',axis=1)\n",
        "y_test = test['species']\n",
        "\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(127, 2)\n",
            "(23, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        },
        "id": "2uHIdo-DsTLB",
        "outputId": "c251ff26-21a1-4f25-be60-6da1c9683cc7"
      },
      "source": [
        "sns.scatterplot(data=df, x='sepal_length', y='sepal_width', hue='species')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='sepal_length', ylabel='sepal_width'>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1170x827 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WXYOGdBzv-aF"
      },
      "source": [
        "# Decision tree"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b_SYT6ywrIBm"
      },
      "source": [
        "## Simple ensembles"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PmTHrXDOwD93"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "98Ylo-yOwHix"
      },
      "source": [
        "clf = DecisionTreeClassifier()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "GtxFpZR1zlsG",
        "outputId": "9fd9a3f3-6ad9-4290-83d4-2e5bb53db7ed"
      },
      "source": [
        "clf.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\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-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1 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-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 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-1 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-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 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-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 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-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1 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-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 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-1 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-1 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-1 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-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 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-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 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: 0.5em;\n",
              "  text-align: center;\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-1 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-1 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-1 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-1 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-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf.score(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LqFoe-tVo8X3",
        "outputId": "64aea043-f52f-40a7-a1eb-9faf83f53e68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.937007874015748"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EZrFd8zDpFpq",
        "outputId": "ff43b710-46cf-4629-cb10-bb27e783abce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5652173913043478"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "niHIEREYvM6L"
      },
      "source": [
        "def decision_boundary(df_train, X_test, feature_1, feature_2, y_train, y_test, clf):\n",
        "    min1, max1 = X_train[feature_1].min() - 1, X_train[feature_1].max() + 1\n",
        "    min2, max2 = X_train[feature_2].min() - 1, X_train[feature_2].max() + 1\n",
        "\n",
        "    x1grid = np.arange(min1, max1, 0.1)\n",
        "    x2grid = np.arange(min2, max2, 0.1)\n",
        "\n",
        "    xx, yy = np.meshgrid(x1grid, x2grid)\n",
        "\n",
        "    r1, r2 = xx.flatten(), yy.flatten()\n",
        "    r1, r2 = r1.reshape((len(r1), 1)), r2.reshape((len(r2), 1))\n",
        "\n",
        "    grid = np.hstack((r1,r2))\n",
        "\n",
        "    y_pred = clf.predict(grid)\n",
        "    # reshape the predictions back into a grid\n",
        "    zz = y_pred.reshape(xx.shape)\n",
        "    # plot the grid of x, y and z values as a surface\n",
        "    plt.contourf(xx, yy, zz)\n",
        "    # create scatter plot for samples from each class\n",
        "    sns.scatterplot(data=X_train, x=feature_1, y=feature_2, hue=y_train, markers='X')\n",
        "    # for class_value in range(2):\n",
        "    #     # get row indexes for samples with this class\n",
        "    #     row_ix = np.where(y_train == class_value)\n",
        "    #     # create scatter of these samples\n",
        "    #     plt.scatter(X_train.iloc[row_ix], X_train.iloc[row_ix, 1], cmap='Paired')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 760
        },
        "id": "9TSpP9USwd3n",
        "outputId": "7ba2e6ee-e859-4a13-aa99-373cc3e1a360"
      },
      "source": [
        "decision_boundary(X_train, X_test, 'sepal_length', 'sepal_width', y_train, y_test, clf)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1170x827 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import tree\n",
        "tree.plot_tree(clf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3VveiPNDscie",
        "outputId": "f9ceaa74-f687-44c3-91b4-c4c33ed3e62b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.31875, 0.9615384615384616, 'x[0] <= 5.45\\ngini = 0.666\\nsamples = 127\\nvalue = [43, 39, 45]'),\n",
              " Text(0.1, 0.8846153846153846, 'x[1] <= 2.8\\ngini = 0.264\\nsamples = 46\\nvalue = [39, 6, 1]'),\n",
              " Text(0.20937499999999998, 0.9230769230769231, 'True  '),\n",
              " Text(0.05, 0.8076923076923077, 'x[0] <= 4.7\\ngini = 0.449\\nsamples = 7\\nvalue = [1, 5, 1]'),\n",
              " Text(0.025, 0.7307692307692307, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0, 0]'),\n",
              " Text(0.075, 0.7307692307692307, 'x[0] <= 4.95\\ngini = 0.278\\nsamples = 6\\nvalue = [0, 5, 1]'),\n",
              " Text(0.05, 0.6538461538461539, 'x[1] <= 2.45\\ngini = 0.5\\nsamples = 2\\nvalue = [0, 1, 1]'),\n",
              " Text(0.025, 0.5769230769230769, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
              " Text(0.075, 0.5769230769230769, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
              " Text(0.1, 0.6538461538461539, 'gini = 0.0\\nsamples = 4\\nvalue = [0, 4, 0]'),\n",
              " Text(0.15, 0.8076923076923077, 'x[0] <= 5.35\\ngini = 0.05\\nsamples = 39\\nvalue = [38, 1, 0]'),\n",
              " Text(0.125, 0.7307692307692307, 'gini = 0.0\\nsamples = 34\\nvalue = [34, 0, 0]'),\n",
              " Text(0.175, 0.7307692307692307, 'x[1] <= 3.2\\ngini = 0.32\\nsamples = 5\\nvalue = [4, 1, 0]'),\n",
              " Text(0.15, 0.6538461538461539, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
              " Text(0.2, 0.6538461538461539, 'gini = 0.0\\nsamples = 4\\nvalue = [4, 0, 0]'),\n",
              " Text(0.5375, 0.8846153846153846, 'x[0] <= 5.95\\ngini = 0.537\\nsamples = 81\\nvalue = [4, 33, 44]'),\n",
              " Text(0.428125, 0.9230769230769231, '  False'),\n",
              " Text(0.325, 0.8076923076923077, 'x[1] <= 3.35\\ngini = 0.473\\nsamples = 26\\nvalue = [4, 18, 4]'),\n",
              " Text(0.3, 0.7307692307692307, 'x[0] <= 5.75\\ngini = 0.298\\nsamples = 22\\nvalue = [0, 18, 4]'),\n",
              " Text(0.25, 0.6538461538461539, 'x[0] <= 5.65\\ngini = 0.133\\nsamples = 14\\nvalue = [0, 13, 1]'),\n",
              " Text(0.225, 0.5769230769230769, 'gini = 0.0\\nsamples = 8\\nvalue = [0, 8, 0]'),\n",
              " Text(0.275, 0.5769230769230769, 'x[1] <= 2.55\\ngini = 0.278\\nsamples = 6\\nvalue = [0, 5, 1]'),\n",
              " Text(0.25, 0.5, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
              " Text(0.3, 0.5, 'gini = 0.0\\nsamples = 5\\nvalue = [0, 5, 0]'),\n",
              " Text(0.35, 0.6538461538461539, 'x[1] <= 2.65\\ngini = 0.469\\nsamples = 8\\nvalue = [0, 5, 3]'),\n",
              " Text(0.325, 0.5769230769230769, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
              " Text(0.375, 0.5769230769230769, 'x[1] <= 3.1\\ngini = 0.49\\nsamples = 7\\nvalue = [0, 4, 3]'),\n",
              " Text(0.35, 0.5, 'x[0] <= 5.85\\ngini = 0.5\\nsamples = 6\\nvalue = [0, 3, 3]'),\n",
              " Text(0.325, 0.4230769230769231, 'gini = 0.5\\nsamples = 4\\nvalue = [0, 2, 2]'),\n",
              " Text(0.375, 0.4230769230769231, 'gini = 0.5\\nsamples = 2\\nvalue = [0, 1, 1]'),\n",
              " Text(0.4, 0.5, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
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              " Text(0.725, 0.7307692307692307, 'x[0] <= 6.95\\ngini = 0.449\\nsamples = 44\\nvalue = [0.0, 15.0, 29.0]'),\n",
              " Text(0.7, 0.6538461538461539, 'x[0] <= 6.35\\ngini = 0.439\\nsamples = 43\\nvalue = [0.0, 14.0, 29.0]'),\n",
              " Text(0.575, 0.5769230769230769, 'x[1] <= 2.75\\ngini = 0.494\\nsamples = 18\\nvalue = [0, 8, 10]'),\n",
              " Text(0.475, 0.5, 'x[0] <= 6.25\\ngini = 0.49\\nsamples = 7\\nvalue = [0, 4, 3]'),\n",
              " Text(0.425, 0.4230769230769231, 'x[1] <= 2.45\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 2, 1]'),\n",
              " Text(0.4, 0.34615384615384615, 'x[0] <= 6.1\\ngini = 0.5\\nsamples = 2\\nvalue = [0, 1, 1]'),\n",
              " Text(0.375, 0.2692307692307692, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
              " Text(0.425, 0.2692307692307692, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
              " Text(0.45, 0.34615384615384615, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
              " Text(0.525, 0.4230769230769231, 'x[1] <= 2.4\\ngini = 0.5\\nsamples = 4\\nvalue = [0, 2, 2]'),\n",
              " Text(0.5, 0.34615384615384615, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
              " Text(0.55, 0.34615384615384615, 'x[1] <= 2.6\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 1, 2]'),\n",
              " Text(0.525, 0.2692307692307692, 'gini = 0.5\\nsamples = 2\\nvalue = [0, 1, 1]'),\n",
              " Text(0.575, 0.2692307692307692, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
              " Text(0.675, 0.5, 'x[1] <= 2.85\\ngini = 0.463\\nsamples = 11\\nvalue = [0, 4, 7]'),\n",
              " Text(0.65, 0.4230769230769231, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 0, 2]'),\n",
              " Text(0.7, 0.4230769230769231, 'x[0] <= 6.25\\ngini = 0.494\\nsamples = 9\\nvalue = [0, 4, 5]'),\n",
              " Text(0.65, 0.34615384615384615, 'x[1] <= 2.95\\ngini = 0.5\\nsamples = 6\\nvalue = [0, 3, 3]'),\n",
              " Text(0.625, 0.2692307692307692, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
              " Text(0.675, 0.2692307692307692, 'x[0] <= 6.15\\ngini = 0.48\\nsamples = 5\\nvalue = [0, 2, 3]'),\n",
              " Text(0.65, 0.19230769230769232, 'x[1] <= 3.2\\ngini = 0.5\\nsamples = 4\\nvalue = [0, 2, 2]'),\n",
              " Text(0.625, 0.11538461538461539, 'x[0] <= 6.05\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 1, 2]'),\n",
              " Text(0.6, 0.038461538461538464, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
              " Text(0.65, 0.038461538461538464, 'gini = 0.5\\nsamples = 2\\nvalue = [0, 1, 1]'),\n",
              " Text(0.675, 0.11538461538461539, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
              " Text(0.7, 0.19230769230769232, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
              " Text(0.75, 0.34615384615384615, 'x[1] <= 3.1\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 1, 2]'),\n",
              " Text(0.725, 0.2692307692307692, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
              " Text(0.775, 0.2692307692307692, 'x[1] <= 3.35\\ngini = 0.5\\nsamples = 2\\nvalue = [0, 1, 1]'),\n",
              " Text(0.75, 0.19230769230769232, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
              " Text(0.8, 0.19230769230769232, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
              " Text(0.825, 0.5769230769230769, 'x[1] <= 3.15\\ngini = 0.365\\nsamples = 25\\nvalue = [0, 6, 19]'),\n",
              " Text(0.8, 0.5, 'x[1] <= 2.85\\ngini = 0.432\\nsamples = 19\\nvalue = [0, 6, 13]'),\n",
              " Text(0.775, 0.4230769230769231, 'gini = 0.0\\nsamples = 4\\nvalue = [0, 0, 4]'),\n",
              " Text(0.825, 0.4230769230769231, 'x[1] <= 2.95\\ngini = 0.48\\nsamples = 15\\nvalue = [0, 6, 9]'),\n",
              " Text(0.8, 0.34615384615384615, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2, 0]'),\n",
              " Text(0.85, 0.34615384615384615, 'x[0] <= 6.55\\ngini = 0.426\\nsamples = 13\\nvalue = [0, 4, 9]'),\n",
              " Text(0.825, 0.2692307692307692, 'gini = 0.0\\nsamples = 4\\nvalue = [0, 0, 4]'),\n",
              " Text(0.875, 0.2692307692307692, 'x[0] <= 6.65\\ngini = 0.494\\nsamples = 9\\nvalue = [0, 4, 5]'),\n",
              " Text(0.85, 0.19230769230769232, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
              " Text(0.9, 0.19230769230769232, 'x[0] <= 6.75\\ngini = 0.469\\nsamples = 8\\nvalue = [0, 3, 5]'),\n",
              " Text(0.85, 0.11538461538461539, 'x[1] <= 3.05\\ngini = 0.5\\nsamples = 4\\nvalue = [0, 2, 2]'),\n",
              " Text(0.825, 0.038461538461538464, 'gini = 0.5\\nsamples = 2\\nvalue = [0, 1, 1]'),\n",
              " Text(0.875, 0.038461538461538464, 'gini = 0.5\\nsamples = 2\\nvalue = [0, 1, 1]'),\n",
              " Text(0.95, 0.11538461538461539, 'x[0] <= 6.85\\ngini = 0.375\\nsamples = 4\\nvalue = [0, 1, 3]'),\n",
              " Text(0.925, 0.038461538461538464, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
              " Text(0.975, 0.038461538461538464, 'gini = 0.444\\nsamples = 3\\nvalue = [0, 1, 2]'),\n",
              " Text(0.85, 0.5, 'gini = 0.0\\nsamples = 6\\nvalue = [0, 0, 6]'),\n",
              " Text(0.75, 0.6538461538461539, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
              " Text(0.775, 0.7307692307692307, 'gini = 0.0\\nsamples = 11\\nvalue = [0, 0, 11]')]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1170x827 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf = DecisionTreeClassifier(max_leaf_nodes = 2)\n",
        "clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "1r6KVHrsp0Vd",
        "outputId": "ee9ca03b-ec22-4532-af5a-2131e4471dca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_leaf_nodes=2)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\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: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\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: 0.5em;\n",
              "  text-align: center;\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_leaf_nodes=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\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_leaf_nodes=2)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf.score(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eBvplXQVp75G",
        "outputId": "f5af9233-3fe1-479c-fae3-991331d6d5e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6535433070866141"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CZpII1nOp_3Q",
        "outputId": "3ff7c73a-0c58-4cb7-bdcb-3ebaf55b7154"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4782608695652174"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "decision_boundary(X_train, X_test, 'sepal_length', 'sepal_width', y_train, y_test, clf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 760
        },
        "id": "_CndG-a8qQtC",
        "outputId": "eb088532-f6fd-4170-e8d2-c7048620fd49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1170x827 with 1 Axes>"
            ],
            "image/png": 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JRJo6kgpuERERERERaRzBAPjLAwS9AexuB84kB3ZHYp774AOHM/LgAwH4zzXX88tvSxPzxDGo4BYREREREZEG85ZH+Gb+B+Qtza081qZ3DnuPH4E7Kf7Lh1kszW+JsuaXSERERERERFqUYIBqxTZA3tJcvpn/AcFAEwVrYiq4RUREREREpEH85YFqxfZ2eUtz8ZfvmhW3Cm4RERERERFpkKA3dkFdW/vOSgW3iIiIiIiINIjdHXtltNrad1YquEVERERERKRBnEkO2vTOqbGtTe8cnEkquEVERERERER2mN0Be48fUa3obtM7h8HjRyRsa7DmRtuCiYiIiIiISIO5kywMPXNUk+3D7fX6+OSzzwHYsHETZWVlvPPeIgAGD9qLzMyMxAT5GxXcIiIiIiIi0ijsDrA7HJCe+Fva+fn5TLv0iirHtv/9iUceZp/MQQnPpIJbREREREREWrz27dux9IclTR2jCs3hFhEREREREYkDFdwiIiIiIiIicaCCW0RERERERCQOVHCLiIiIiIiIxIEKbhEREREREZE4UMEtIiIiIiIiEgcquEVERERERETiQAW3iIiIiIiISByo4BYRERERERGJAxXcIiIiIiIiInFga+oAIiIiIiIiIg319rvv88bCt/j1t98pLi6mU05Hxp9yEseM+xeGYTRJJhXcIiIiIiIi0uI9Pv8Z2rfbjcsuvpDMjAy++Gox1954M5s25XHBuf9ukkwquEVERERERKRRlJQFyC8opaSkDE9qMpnpKXiSHQl57ofuu5uMjPTKvw/dZzCFhUU8Pv9pzjtnIhZL4mdUq+AWERERERGRBtucX8b1V9zJF598U3lsvwMGc/2Mi2mdmRz35/97sb1d79178eIrC/B6vSQnxz/DP2nRNBEREREREWmQkrJAtWIb4ItPlnD9FXdRUhZoklzfff8DbVq3bpJiG1Rwi4iIiIiISAPlF5RWK7a3++KTJeQXlCY4EXz7/Q8sfOc9zjr91IQ/93YquEVERERERKRBSkrKGtTe2Dbl5fF/l13JPoMHcdopJyX0uf9OBbeIiIiIiIg0iMcTe8h2be2Nqbi4hHMuuJD09DTuv/P2JlksbTsV3CIiIiIiItIgmRkp7HfA4Brb9jtgMJkZKQnJ4fP5OG/qRZSWljJn5n14PIl53mhUcIuIiIiIiEiDeJIdXD/j4mpFd8Uq5ZckZGuwUCjERZf9hxWrVjNn1v20adM67s9ZG20LJiIiIiIiIg3WOjOZ2+654n/7cHuSycxI3D7cN95yGx998hmXXzyN0rIyfvjp58q2PXbvhcORmBx/p4JbREREREREGoUn2YEnORPITPhzf/7lYgBuu+veam3vv/ka7du3S3AiFdwiIiIiIiKyE1j01utNHaEazeEWERERERERiQMV3CIiIiIiIiJxoIJbREREREREJA5UcIuIiIiIiIjEgQpuERERERGRXYVpYppmU6do1kzThEZ6j1Rwi4iIiIiI7AJsRgRMk3Kfv6mjNGvlPj+YZsX71UDaFkxERERERGQXYDXAY3jZsmUrAEkuJ4ZhNHGq5sP862LEli1b8RherI3w1qjgFhERERER2UVkW/0Qhi15YTCMij9S4a+h5B7DW/E+NQIV3CIiIiIiIrsIw4DWNj9Zpp+QqRnGVRgVw+4b4872diq4RUREREREdjFWA6yNMEdZYtMlDREREREREZE4UMEtIiIiIiIiEgcquEVERERERETiQAW3iIiIiIiISBw060XTysrKGDNmDHl5ebz00kv069cv6mNHjBjB+vXrqx3/6aefcDqd8YwpIiIiIiIiUk2zLrgffPBBwuFwnR8/evRoJkyYUOWYw+Fo7FgiIiIiIiIitWq2BfeKFSt45plnuPzyy7nuuuvqdE6rVq0YMGBAfIOJiIiIiIiI1EGzncM9ffp0TjrpJLp06dLUUURERERERER2WLMsuN9++23+/PNPLrjggh0674033qBv374MHDiQf//73/zxxx9xSigiIiIiIiISW7MbUu71epkxYwYXXXQRKSkpdT5vxIgR9O/fn3bt2rF27VoefvhhTjnlFBYsWEDHjh3jmFhERERERESkumZ3h/uhhx4iKyuLY489dofOu/rqqxk7dix77703Rx99NE899RQAc+fOjUdMERERERERkZia1R3u9evXM2/ePGbNmkVJSQkA5eXllf8tKysjOTm5Tn21bt2aQYMG8euvv8Ytr4iIiIiIiEg0zargXrduHcFgkHPOOada2+mnn86ee+7JCy+80ATJRERERERERHZMsyq4e/fuzZNPPlnl2NKlS7n11lu54YYb6NevX537ysvL49tvv2XcuHGNHVNERERERESkVs2q4E5NTWXIkCE1tvXp04c+ffoAcMYZZ7Bhwwbee+89AP773//y4YcfcuCBB9K6dWvWrl3LnDlzsFqtnHXWWQnLLyIiIiIiIrJdsyq46yoSiRAOhyv/3qFDBzZv3swtt9xCSUkJHo+HoUOHMnXqVK1QLiIiIiIiIk2i2RfcQ4YMqbaf9vYVyLcbMGBAtWMiIiIiIiIiTanZbQsmIiIiIiIisjNQwS0iIiIiIiISByq4RUREREREROKg2c/hFhERaY6M5CwsGR0wK/6GYYYJb8sFX1ETJxMREZHmQgW3iIjIDjIyOhCxeyjNzQUzUnHMaiV5t05QsgGzdGsTJxQREZHmQEPKRUREdoTVDklZlG/cUFlsA5jhMKXr1mBkdgTDaMKAIiIi0lyo4BYREdkBRtpu+PK3RW33FxZhJLdKYCIRERFprlRwi4iI7ADD7iLi90dtjwQCGHZnAhOJiIhIc6WCW0REZEcEvFid0Qtqq9OBGfQlMJCIiIg0Vyq4RUREdkCkeCOurChDxg0DZ1o6Zmn0IeciIiKy61DBLSIisiPCISjJI7ldRwyrtfKwYbOT0qETkW2r4a/NwkRERGTXpm3BREREdlCkaCMEvHjad8Q0/iq6I0Ei21aAv7Rpw4mIiEizoYJbRESkPryFhNcXNnUKERERacY0pFxEREREREQkDlRwi4iIiIiIiMSBCm4RERERERGROFDBLSIiIiIiIhIHKrhFRERERERE4kAFt4iIiIiIiEgcqOAWERERERERiQMV3CIiIiIiIiJxoIJbREREREREJA5UcIuIiIiIiIjEgQpuERERERERkThQwS0iIiIiIiISByq4RUREREREROJABbeIiIiIiIhIHKjgFhEREREREYkDFdwiIiIiIiIicaCCW0RERERERCQOVHCLiIiIiIiIxIEKbhEREREREZE4UMEtIiIiIiIiEgcquEVERERERETiQAW3iIiIiIiISByo4BYRERERERGJAxXcIiIiIiIiInGggltEREREREQkDlRwi4iIiIiIiMSBCm4RERERERGROFDBLSIiIiIiIhIHKrhFRERERERE4kAFt4iIiIiIiEgcqOAWERERERERiQMV3CIiIiIiIiJxoIJbREREREREJA5UcIuIiIiIiIjEga2pA4iISGK1aZvNhLNPpnv3TqxYkcu8R59l08bNTR1LREREZKejgltEZBcy9aKzOe3UcaSYfsxAgP36dOLocaN45rk3uOfOOU0dT0RERGSnoiHlIiK7iFGjD+Ss8WNJKi8g4i3HDIeIeMtJKi/g9JOO4LDDRzR1RBEREZGdigpuEZFdxLSLzsbhLa6xzeErYeq0CQlOJCIiIrJzU8EtIrKLyMpIhUik5sZIhIx0T2IDiYiIiOzkVHCLiOwizFofYCQihoiIiMguQwW3iMguIjd3A4bNXmObYbORu3ZjghOJiIiI7NxUcIuI7CKm33g/5a5UMP5xJ9swKHemcvNN9zVNMBEREZGdlApuEZFdxC8/L+XSy26l0J5KyJ2K3ZNKKCmVQnsql//ndn768bemjigiIiKyU9E+3CIiu5AP3/+M4R98wQEH7Uunzh1Ys2Y9n3z4BZFoi6mJiIiISL2p4BYR2cVEIhE++uDzpo4hIiIistPTkHIRERERERGROFDBLSIiIiIiIhIHKrhFRERERERE4kAFt4iIiIiIiEgcNOuCu6ysjAMOOIBevXrx888/x3ysaZrMmTOHgw46iP79+3PiiSfyww8/JCaoiIhIPRgprbC274u1Q38s7fqAO72pI4mIiEgjatYF94MPPkg4HK7TYx955BHuv/9+zjzzTGbPnk12djYTJkxg7dq1cU4pIiKyowys7fsStKVTvG49xWvWULpxE5GUtlhad2/qcCIiItJImm3BvWLFCp555hmmTJlS62P9fj+zZ89mwoQJnHnmmey7777cfffdpKenM3fu3ASkFRERqTtLVg7ewhL827bAX3ugm6EQ3k0bCYUsGCmtmjihiIiINIZmW3BPnz6dk046iS5dutT62O+++47S0lLGjBlTeczhcHDIIYfwySefxDOmiIjIDjOSMwkWF9bY5t22BUt6u8QGEhERkbholgX322+/zZ9//skFF1xQp8evXLkSgK5du1Y53q1bNzZs2IDP52v0jCIiIvVisRIJhqK3RyKYGInLIyIiInHT7Apur9fLjBkzuOiii0hJSanTOcXFxTgcDpxOZ5XjqampmKZJUVFRPKKKiIjsODOCYbXGfIhhqOAWERHZGTS7gvuhhx4iKyuLY489tqmjiIiIND7ThEgQw2avsdmWlIzpLU5wKBEREYmHZlVwr1+/nnnz5jF16lRKSkooLi6mvLwcgPLycsrKymo8LzU1lUAggN/vr3K8uLgYwzBIS0uLe3YREZG6imxZRUqHjtXudFvsDpLatCVSoB02REREdga2pg7wd+vWrSMYDHLOOedUazv99NPZc889eeGFF6q1bZ+7vWrVKnbffffK4ytXrqRdu3a4XK74hRYREdlRwXIiecvwdOhKOBQhEghgdTqxWEzCG36FcLCpE4qIiEgjaFYFd+/evXnyySerHFu6dCm33norN9xwA/369avxvL322ouUlBTeeuutyoI7GAzy7rvvcsABB8Q9t4iIyA4LlBJe9xPYnFhtDswiH2EV2iIiIjuVZlVwp6amMmTIkBrb+vTpQ58+fQA444wz2LBhA++99x4ATqeTSZMm8cADD5CZmUnPnj159tlnKSwsZOLEiQnLLyIissNCfsyQv/bHiYiISIvTrAruuopEIoTD4SrH/v3vf2OaJvPmzSM/P5/evXszd+5cOnbs2EQpRUREREREZFfW7AvuIUOG8Mcff1Q59tRTT1V7nGEYTJo0iUmTJiUqmoiIiIiIiEhUzWqVchEREREREZGdhQpuERERERERkTho9kPKRURk55KUnMRFF5/D6NEHYLNaCIYj/PeNRXw8910CvkBTxxMRERFpNCq4RUQkYZKSk1jwxjw6eOyY3lL4a/3Ls44dyeGjDuLyk68E7YwlIiIiOwkNKRcRkYS54qrJdEixY3rLqhw3vaW0Sbdy4vnHN1EyERERkcangltERBJmxIj9MH1lNbaZ5aXsP3poghOJiIiIxI8KbhERSQjDMLBbY3/s2Gz6WBIREZGdh77ZiIhIQpimSTAcifmYcNhMUBoRERGR+FPBLSIiCfPpJ19juJJqbLO4U/hq0ZIEJxIRERGJHxXcIiKSMLdMv59NPrNa0W24k9lSBs8+8HwTJRMRERFpfCq4RUQkYUqKSxl35Fm8/vH3FDhSKXWlU2BP5aV3F3PZif/BV+5r6ogiIiIijUb7cIuISEKVFJdyxaU3AxULqZlmxbztAZ5OTRlLREREpNHpDreIiDSZ7cW2iIiIyM5IBbeIiIiIiIhIHKjgFhEREREREYkDFdwiIiIiIiIicaBF00REWhir1cqR4w7FYrHw+qtvEw6HmzrSrs3hBtOEoFZYFxGpxrBg2F2Y4RCEA02TwWLFsDkxw0EIB5smg+yyVHCLiLQg73zwHL17dydcXgrA/Q/cwIqVuRy0/zFNnGzXY2R2xJLSipDXB4aBzeUkUrgRs3hTU0cTEWl6FitFjrbk5Xv54/eVtG7bim5dcsg0t2EJlCUog40iRxs2bitn2R+raLtbNl06dyTLzMdIVAbZ5angFhFpIb754W0yXFZKlv1W5XiH7Db8tPQD+vce0UTJdj2W1t0J+CP4V62sctzdZjesGTbMgnVNlExEpBkwLOS7crhk2m38/MPvlYc9qSk8NO9meqQT/6LbsLDN2YGLptzK0l+WVR5OTfMw+4lb6ZaCim5JCM3hFhFpAfoP2IPsrHR8mzdWa/NvySMt2cWwA4c2QbJdkN1FxOLCn7+tWpM3byNGciuwWJsgmIhI8xBwZvLA/c9UKbYBSopLOW/CVeQbWXHP4HdlcfcdT1QptgGKi0o476yryDcy455BBFRwi4i0CHfeez3+bXlR231b87jtjisTmGjXZUlri78gP2q7v6gQI6VVAhOJiDQvhWEXb7/+QY1tJcWlrFy1Eaz2+GYIOnn/7U9rbisoYu36rWDRYF+JPxXcIiItQGZGGpFg9IVezGCQ1FRPAhPtwqyOmD+LSDCEEecvkiIizVkgECIUir6g55bN2zCs8S12/f4gkUgkavvWLQUQ5wwioIJbRKRF+OGH37AlpURttyalsPT35QlMtAvzlWBNSorabEtyY/o1L1BEdl1JLhuZWelR23v26oIZ9Mc1Q7LbQWpa9AvRXbvnQCi+GURABbeISIsw5dwrcWa2AksNv7YNA1er1pw74ZLEB9sFRYrzcGdmgWFUazMsVhwpKZjlBU2QTESkeci0lDDl/86ssW3PvfagTYYLzOh3nxslg7WE8y88rca2vYfuSbbHVrGlo0icqeAWEWkBvF4vsx58Ek+XnljdyZXHre4kPF178tRTL5OfX9SECXchZoTI1tV4cjpjdbkrD9uSkknp1Ilw3rIYJ4uI7AK8RRw0tAfX3TyNzFYZANjsNsYecyi3330pqYH4b59oeAs59MC+XHXDFDIy0wCwO+wcc+IYbpkxDY9fWzhKYmjigohICzHjpvtZ8tX33DvzJtLa5wBQWuZl0qSreOu/7zdxul2LWZ6PGSwnKbMj2NtgAKavhMiGXyEUaOp4IiJNLsWfx5H7dmS/obdR7gvhcNhJt3lxeNfE/e72dh7/JsYN78zwYXfg9YVwOu2kW73Yvat1d1sSRgW3iEgLsui9T+nX66CmjiEAQR8R3c0WEYnOV0QmRWQaQPCvPwlm+IrIoggMQNdDpQloSLmIiIiIiIhIHKjgFhEREREREYkDFdwiIiIiIiIicaCCW0RERERERCQOtGiaiEgLk5mZzr7DBgMGi7/8lq1btiU8Q1arTIbutzcAX32+hG3bdmzfacMwGLLvXuzWri3r1m5gyeLv4xFTREREpEmp4BYRaSFsNhv3PHADgwf1Jd0OYFAYMPnh5z+48IKr8fvjv/yqw2Hn3pk3MWhAb9LsFccKg/DNt79y0dTrCAZrX4J2+IFDufnWy0m1GyTZLZQFIxQHIsy++hF+++a3OL8CERERkcTRkHIRkRbioTkzOGivXnh8RYRLigiXFOLxFzGsXxceeeyuhGSYM+9ODtizGymVGYrw+Io4cK8ePPzobbWe37dfb+655xqywqXYvcUEiwtxeItpFSnnsrsupGP3jgl4FSIiIiKJoYJbRKQF6NipPXv27YHFX16tzfB76d29Iz16do1rhm7dO9OnZycMv7dam8Xvpd/uXenUOXbBfM1100jyFYFpVm0wIzj9hUy4/PTGjCwiIiLSpFRwi4i0AEcfM4Y0ayRqu8cS5vgT/xXXDMedcASplugZ0mwRjjnu8Jh9dMrZDTMUqrEtEgzQoUu7BmUUERERaU5UcIuItAButwvTjF7sEongdrvimsHlcmFGwlHbzUiEpBgZDMOo9Tnq8BARERGRFkMFt4hIC/DBos8pj0T/le3DxqJFn8U9gzfGWpvlppUPPvgiartpmpSUeaNX1RYLxYVlDY0pIiIi0myo4BYRaQGWLP6eraV+LHZHtTbDZiffH+bjGMVuY/j046/I94YwbPbqGex2tpUF+PLzJTH7eGjWkwTdqTW2RdzpPP/Qy42SVURERKQ5UMEtItJCnDH+QvLCdsJuD4bVhmG1EnZ72IqLM8ZfiPnPhcjikeG0C9liOv/KYMWw2gi7PWwOOzjztGm1nv/KS2/y6sJP8CalY3W5wWLB6k6iPCmDd177nMXvL477axARERFJFO3DLSLSQmzckMeog07kiLGjOObYwzEMg9cWPMvrr75Tp/2vG8O63A2MPPAExh41mqOOHo0JvPryW/z39fcIRVkM7Z9uuPYunnjsRc497zQ6de7AihU/M/vh+WTl6yNJREREdi6GmYhbIi3QyJEjyV29jtJt1qaOIiKySxjg6QTAIGsGAEN8FR9P/dpuBSBrWMVQdtvQgQBYBo5IdEQRERERxpx3DYbdzaJFi2p9rIaUi4iIiIiIiMSBCm4RERERERGROFDBLSIiIiIiIhIHKrhFRERERERE4kBLwopIQqR4kjnl1KPZo09P1q7dyFNPvMjmvK1NHSvh0tJSOfX0Y+nZswurVq1l/hMvsW1bQVPHEhGRvzEcSZQYHsKmQYo1iNWXD1pnWETqQQW3iMTd0ccezhX/OZ8UM4AlHMDYtw8nHj+GV157jxnTH2jqeAlz2hnHMWXKmaSYAYxwAHNoH049eSxPzn+VmffNa+p4IiKCQbG7Pd/+uIon5j1OcVEJ+w3fi/FnjKO1sQ1LoKypA4pIC6OCW0Tiqm//3lx95fm4yvIBiAAEAiQDJx41ilUrcnn+2deaMmJCDNl3EBdNPQNnWT4mUHGfJEAyZUw47SiWL1vN2ws/aNqQIiK7uBJnG+68+1neeuPDymPPr1nPG6++z/wX76Wj1Q/hUBMmFJGWRnO4RSSuLv/PBbgDJTW2ObzFTDpvfIITNY3Lrjgfl6+4xjant5gLLzo7wYlERKQKi5W84lCVYnu78jIvd98+D58jqwmCiUhLpoJbROIqp+NumMFgzY2mSbLTjic1JbGhmkDb1pmY4XDNjWaEjLQULBb9ShYRaSqGy8PHH3wdtf3zj7+mNOJIYCIR2Rno252INC3DIBKJNHWKJmcYYGpBHhGRpmOaWG3WqM26KCoi9aHfHCISV3/8sRKLw1lzo2GhsLicstLyxIZqAqtzN2LY7DW2GRYrmzbnq+AWEWlCpq+Yg0cMidp+yJjheCy+BCYSkZ2BCm4RiasZt8yk1J5ccQv3H/zuVO6+a04TpEq8W6bfT7krtcb3wetO5bZbZjVBKhERqWSatEqKcPrEY6s1ZbbK4IILx+P05zdBMBFpyVRwi0hcrVyxhmnTbqTQkUbY7cGWlAJJqZS4M7h31nzeeav64jQ7o19//p3//Od2ipxpRJJSsSWlYCalUuzO4OZbH+KLz5c0dUQRkV1esn8zZ50yknnP3MHI0cMZtE9/LrlyEk+/cCdt2QKRKGtxiIhEoW3BRCTuPv/0aw7Y72hGHnoAvXt3Z926TSx84z283l1raN5773zMh4s+Z/SYg+neozOrV6/j7TcX4fcHmjqaiIj8JcW/iX5Zdqb/50RCEXAZPvCuaepYItJCqeAWkYQIh8O8+9aHvLuL3NGOJhQK8eYb7zV1DBERiSUcxFa+WV+URaTBNKRcREREREREJA5UcIuIiIiIiIjEgQpuERERERERkThQwS0iIiIiIiISB81qLYiPP/6YRx55hOXLl1NaWkqbNm0YNWoUkydPxuPxRD3vtNNO4+uvv652fOHChXTr1i2ekUVEdsjAQf258KKJtG6dRX5+EbMeeJwvd2BLsN3at+Hue69n0KB+YBj89ONSLr7wBtasWVvnPvbeZwBTLpxAdnYmW7cWMPP+x/j6q+/q83KajsONJTMHIykdALOsgEjRBm3ZIyIiIs1Ksyq4CwsL6d+/P6eddhrp6eksW7aMBx54gGXLljFv3ryY5+61115cfvnlVY516NAhnnFFRHbInfdcx4jhe+EOlhMJBumck8GD917Fkp+WMensyzBNM+b5ow49gEfn3UFgyyaC61YC0D+nFR999hIXTr2O1199u9YM982azvAh/XAFyjFDQTp3bsXsmdfx+ZJfmXzelY3yOuMusx22IcfgLSomtKViqx67JxV3hz2JbPodM1DexAFFREREKjSrgnvcuHFV/j5kyBAcDgfXXHMNeXl5tGnTJuq5qampDBgwIM4JRUTq55jjj+CQ4QOxlRcR+euYGQrhCBUztH93zp50Ko88PD/q+RaLhTlz76B8zXLM8P/u4gZLiwmVl3Hf/Tfw9psfEAhE39P7lNOO4aAhfbGVF7G9tDdDQRyhIobv3ZvTzzyeJx9/sRFebRxZrNiHHE3xxo0QiVQeDpYUEyovx5PTi3Du900YUEREROR/mv0c7vT0dACCwWDTBhERaYBzJp2KzVtSY5vVW8Kppx4d8/zJ0yYSKSmsUmxvZ0bChIryueyqyTH7OGvCiVEz2HylnHHW8THPbw6sA/bDX1pepdjezgyHCJSWYSRlNEEyERERkeqaZcEdDofx+/38+uuvzJo1ixEjRtQ6PPzrr79mwIAB9OvXj/Hjx7NkSd3nRIqIxJsnJQliDBl3OW1Yrdao7aNGDSNUVhq1PVxWwkEHDo2ZISXJFT2DaZLscsY8vzmwdutL0OeL2h4qK8VwRV/zQ0RERCSRmtWQ8u0OPvhg8vLyABg+fDh33XVXzMcPHjyYcePG0blzZzZv3szcuXM566yzeOqppxg4cGAiIouIxBSpZX42GIRruHu9XUFBEZa2aUR7hGGzUVRU0KAM1e8ZNz9meSkWqzVqVsNqhUgooZlEREREommWd7jnzJnDc889x/Tp01m5ciXnnntuzC+iU6dO5bjjjmPvvffm8MMP56mnnqJ169Y8+OCDCUwtIhLdDz/8htXpqrHNYrezbPmamOffdussbBmtorbbM1px680PxOzj19+WYXHUfBfb4nDy29LlMc9vDkKL38OZkhy13ZmeQaRkSwITiYiIiETXLAvu3XffnYEDB3L88cfz4IMPsnjxYt577706n5+UlMSBBx7Ir7/+GseUIiJ1d+v0Byi0uDGsVQcWGRYrJXYP118beyTPb7/8Qe76zTgysqq12dMzycsv5puvf4jZx0033EuxLbniLvDfM1itFNuSufG6e+r2YpqQuXUTltItODyp1dqcmVkYwTIIa80PERERaR6aZcH9d7169cJut5Obm9vUUURE6m3D+k2cctJkVpSEKHOmEnJ5KHemkuszOOusS1ixfHWtfRw07BiWrt1KUueeOLPb4mzVlqTOPVi5uZj99xlb6/lr16zntFMvZHWZSZmrIkOZM5U1ZXDa+GnkrlnXCK80/kKLX8VRnoenUxdcrVrjym6Dp3NXHLYwkS0rmjqeiIiISKVmOYf773788UeCweAO7aldXl7ORx99RL9+/eKYTERkx6xcsYZ/HXY6HXLa0aHDbmzauIXVq3bsYuLYw88gJSWJccccjmExeGPBOxQVFtf5/D//WMHhh44np1MH2rVvw4b1eS2m0K5kmoS/fxuLEcLm8gAmkYKSmIvSiYiIiDSFZlVwT548mb59+9KrVy9cLhe///47c+fOpVevXowaNQqAK6+8kgULFvDbb78B8M033/Doo49yyCGH0L59ezZv3sxjjz3Gli1buO+++5ry5YiI1Ghd7gbW5W6o9/mlpeU8/eRLDcqQu2Zdyyu0/8mMYHqLmjqFiIiISFTNquDu378/CxcuZM6cOZimSfv27Tn++OOZOHEiDocDgEgkUmUBtezsbILBIPfccw+FhYW43W4GDhzIDTfcQP/+/ZvqpYiIiIiIiMguzjDN+o3BM02T559/npdeeom1a9dSXFx9SKNhGJV3oluakSNHkrt6HaXbou+LKyIijWeApxMAg6wZAAzxVXw89Wu7FYCsYXYAbEMrtnu0DByR6IgiIiIijDnvGgy7m0WLFtX62Hrf4b799tt5/PHH6d27N2PHjiUtLa2+XYmIiIiIiIjsdOpdcC9YsIBDDz1U86RFREREREREalDvgtvn87Hffvs1ZhYR2UlZLBZOP+sETh1/NC6nnXA4wsKFHzLz/scoLytPSAar1cqZE0/k5JPH4XTaCYUivPHG+zw083G8Xl9CMthsNiaccwonnngkDruNYCjMawveZfaDT+Lz+evUx+579OA/V02ma+eOYEDu2o3MuGUmP/+4tE7nOxx2zp40nmOPOxyn3UYgGOKVlxcy5+H5BAKJ2b86NTWF2XPvYMiQAWCamBh8+tkSHv6/WfjKE/OzkL9YrESS2hK0eAj5Q9hdNuyhAoyyvIRmKHe0ojBox1vuw+Nxk2Etx+7dVucuIq40CsxUikp82GxW0twW0iL5ENT/TyIi0rTqPYf7/PPPJzMzk+nTpzd2pmZBc7hFGofFYmH+czPp13U3LN7Syq2bjKQU1pcEOXrsBEpLyuKawWq18uyLD7FHTjZGeSnwvwxri/wcPXZi3At/u93O8y/Ppme7DIzyksrjRlIKq/O9HDtuYq2F/yGjD+TWWy4lOVBKJBioON9mp9zp4cabHuD1Be/EPN/hsPPiq4/QvU0qlJf+ryHJw4otJRx/1Nn4/YH6v8g6SEtPZcn3b0PRVoLFhZXH7WkZkJLJ6ftNoI/fDWgOd9xZbATTevLdS1+y6beK7eksVgvdhveh5/Ae2AqXxT+D1cZWewduuHYWX332LQB2h51TTh/H6aeOJtW7ttYuvM5WfPr9Wm6fPrtyi7wu3Tsx465L6ZJcjhEoraUHERGRHbMjc7gt9X2S6667jh9//JGHH36YgoKC+nYjIju5U047pqLYLq+6T7JZXkr7JCs33nxZ3DNMOPvkv4rtErYX29szdPTYufb6i+KeYdL5p9HrH8X29gyd011cfuXkmOc7HHZuvOkS3GUFlcU2gBkK4i7L56qrJ+NOcsfsY+pF/6Z7638U2wDlJXRvlcJFl0zasRdVD888/yAUbq5SbAMEiwqgNJ9r5lwZ9wxSIZTSkS+f+Kiy2AaIhCMs++hn/vx8BWZydtwzFNnbMG3yzZXFNkAwEOSJR1/i2Rc/IOTOjN2B3ckf68u56pI7quxHv2r5GiacehlbjKx4RRcREamTOhfcAwcOZK+99qr8M2bMGHJzc7nvvvvYb7/9GDBgQJX2vfbai0GDBsUzu4i0AKedfmzFne0amP5yhg4ZgNUa35EkJ578r7/ubNeQwVfOAQfsE9fnBzj2uMPhH8X2/zKUMWrUsJjnH/GvQ0gxQvz9gsHfpRDkuBOOjNnHv8aOgmg/C28phx9+cMzzG8Mee3QnWFJ9VwuoKLr3GNgr7hkEMAz8QRsFa2oeOr7ik18IWmspdhucwcLmAh9//LaixuanH3uVgnBSzC5KLencd9fjNbaVlZbzyUffYLhTG5pURESk3uo8h3v06NEYhhHPLCKyE3I57RCJPjfYYkbweFIoLCyKWwan3Q7h6HOkLZi4k9x4y71xy+CwWSHGFGmb1cBqtRIOh2ts771HD+yEqbkVrOEQu/fuXnuGaB0ADnt8L3xYLBaIRGI/yKylXRqHxU75tpovAAFEQmFCwTCOeGaw2cldsyFqs9frw+sNQoz/LQPYWfHn6qjt3327lOMP7QPUfJFHREQk3upccM+YMSOeOURkJxUKRWKOpTENC2Vxnj8dqqXIMw0LvjgvnBaKxF4uI2IStdgGyF2znrARvfKIWG0xixeAcC0ZQuF6LelRZ5FIBIxaBlbV1i6NIxLCnZ4ctdmwGNjsNojfNSgIh2jbLvqwdZvdhstlj32hijDtOrRlxbLVNbb37NUJwvFdl0BERCSWen+zmTlzJn/++WfU9mXLljFz5sz6di8iO4kFC97BSPLU2GZxOvlt6QqCwfiujr3wzQ8xklJqbLO63Hz/w2/Uc/3IOnvv3U8x3DUXOFZ3El9//WPM8199ZSElZvRrpCWmjeefWRCzj48++gqLu+YhuhZXEp99tiTm+Y1h7bpNWKNksCV7WLNiXdwzCGBGcLkhJTutxuYOe3XHHonfqBMAImHaZafStl3rGpv/dfQoMmyxL4SlUcy5U0+tsc1mtzF6zDDM8sKGJhUREam3BhXcf/zxR9T2ZcuWMWvWrPp2LyI7iUcenk9uoRdcVYssi9NFPm6u+k/8R8/MvH8e64qDGM6qGaxOF1sjdq696o64Z7j37jlsKA9Xz+BykxewcuN1d8c8v6y0nNmzn8afnF71LrBh4E9K46n5C2odln/7jFls8hlYnFUXVzOcLvICBjNujv9F0tNPnYKjbUesrqoZrO5kHK3bMX3SrXHPIBVsZesYNmk0njYZVY636Z1D/yMGJmRrsIzIVh5+7GY6dmpf5fgBI4Zy3vknYPfF3hrM9JcxqE8Hzpl8apW1IDypKcx69CayLRpKLiIiTave+3DXprCwELvdHq/uRaSF8Pkqtt265vqLOPDAIdgwiRgWfv75D6656nbyNm2JewZvuZejx57FdTdczLDhg7GaESKGhR9+XMq1V93B1i113++3vkpLyjj6XxO47oaL2W+/QVipyPDdd79y7VW3k59fWGsfjz36HBvWbWLaxeeQmZ4KpklRqZf775jJf19/r9bzi4tKOPpfE7j+povZZ58BFRmwsOSbn7ju6jviOo9+u9Wr1jLqoBN4+vlZtO3SETMYxLDbWbc+jxsPuYCtG7fSyZpRe0fScCE/zrKVHHD2AfiDVvwlPpLSk7Eb5VgL/kjMfPqgj/b2Lcyddy35JQEKC4pps1srMhxhkr25VXY2iMbj28jpx+zD2KMOZsOGLTidTtq08pBBIRZ//P+fFhERiWWH9uFesmQJixcvBirucB966KH07Nmz2uNKSkpYuHAhbdq04aWXXmq8tAmkfbhFGp9hGCQlu/GW+yrm8ypDvTO4XE4Mw6h17+54Zmgom81GZlY6W7fkE4lEGODpBMCgvwpu7cOdSAZYLBCJsapeS8hgWACzToW6iIhIfe3IPtw7dId78eLFlfOyDcPg3Xff5d13363xsd27d+eaa67Zke5FZCdnmiZlpfFdIG1XyeDzRV91PVEZGioUCrE5b2uTZpDtzCYuthspg1a5FxGRZmaHCu6zzz6bU089FdM02W+//bjhhhs49NBDqzzGMAzcbjdOp7NRg4qIiIiIiIi0JDtUcLtcLlwuFwCLFi0iMzMTt9tdy1kiIiIiIiIiu556L5rWvn372h8kIiIiIiIisouqc8E9YsQIDMPYoc4Nw+D999/f4VAiIjszl8tJZlYGBQVFeMu99eqjVXYWhmGwZbPmQItUsjkq5nGHQ02dpGm5PBAOQrB+iyqKiEjjqXPBvc8++1QruH/55ReWLVtG9+7d6dKlCwCrVq1i+fLl9OjRg759+zZuWhGRFiwrK4Pb7ryaPnt0x2KGMQ0rf67I5fJLprNxQ932PD7+pLGcd/5pJDttYII3FOHROc8y/8mWuSOESGMIJ7cjZE2laGMBVocdT6sU7P5NGL7Cpo6WUIXJXSgqj/D798tJTfPQuWs3WllKsJdvbupoIiK7rDoX3DNmzKjy9/fff5/333+fxx57jH333bdK2+eff860adO48MILGyeliEgLl56exiuvz6WNLUT4b3sDD+7ampdencPRYyfWumL3lGkTmXDqWBzeYvBXbHuUAlw8ZTwdcnZjxvQH4vkSRJqlcFpXln6yghWfvFN5zGq3MuTMUWRlWLB485swXeLke3py352PsfC1/21R405yc/vMaxnYfTecpRubMJ2IyK7LUt8T77vvPsaPH1+t2AbYf//9OfXUU7nvvvsaFE5EZGdx8eXn0toRIeyvOsQzEvCThZ/rb7w45vmpaR7GnzoOh7cIqLrHsMNbzNHjDiGrVWZjxxZp1gxnCptXF7Pik1+qHA8Hw3z56LsEbK2bKFmCpe7G229+WqXYBvCWe7lo0jVsDWiBWxGRplLvgnvNmjWkp6dHbU9PTyc3N7e+3YuI7FQOPHAoEW/N+16H/T4GDNgj5vlHH3s4qZboexSnGmFOPmVcgzKKtDRBRyuWvvdDjW2maZL73UqM5IzEhmoCm4NJPDXvxRrbQsEQH73/BZa0tglOJSIi0ICCOycnh1deeYWysrJqbaWlpbz88st07NixQeFERHYWVkvsRSctBlit1qjt7du3wYhEL7iNcJDd2usLtexaTIud8oKSqO0lm4vBYk9goqZhYmHr5uhD59eu2YBhdyUwkYiIbFfvbcGmTZvG1KlTGTNmDEcffTSdOnUCKu58v/rqq2zbtk1DykVE/uL3B8Ew+Odw8O2CYZNwOHpB/dNPvxMce3DUq6Qhq42ff1ra8KAiLYgl7COjY2u2rthQY3vrHrtBsOaRJTsTmxGmx+5dWfb7yhrb9xrcn7C3qMY2ERGJr3rf4R41ahRz5swhMzOT2bNnc+WVV3LllVcye/ZssrKyePjhhxk1alRjZhURabGeffY1zKSUmhvdybz530U1t/3l7Tc/oMS0/lW0/4NhUGraefWlhY2QVKTlsPry6PevwTW2OZJdtO3ZFtNXmuBUiZdlFDDlkrNrbMtuncWeA3eHsoIEpxIREWjAHW6AYcOGMWzYMLZs2cKGDRVXl9u1a0d2dnajhBMR2VnMnfMMB4/Yj35d22HxloBpgmFgJqWwbEMhd93+cMzzQ6EQl148nXvvvRZPqLxy8TWL00mpLZkrr7gNvz+QiJci0nyEAqQklbL/OYfx3Yuf4S2oKK4zO7dl75OHYy9f3bT5EqWsgD265zD97v9w74zZlcPL9xrcn6tvvog2bGnigCIiu64GFdzbZWdnq8gWEYkhEokw/qTJHHv8EZw54USS3C78/iBPz32GZ+e/SigUqrWPzz/9muOOPY9LLj+Xvn16AvD7Tyu587aHWLF8dZxfgUjzZCnfTKvUFA4+/xBCIQPDasGOF6t3JYR2nYtQaeW5jN6nIwOev4eSEi8OpwOPy0JGcBNEWbBRRETir84F94IFCwAYN24chmFU/r02Rx11VD1iiYjsfCKRCC8+/wYvPv9GvftYs3otU867qhFTiewE/KXY/cvZ+ZdHq0XxJtoAbWxAGKi+rq2IiCRYnQvuK664AsMwOPzww3E4HFxxxRW1nmMYhgpuERERERER2SXVueBetKhiQR+Hw1Hl7yIiIiIiIiJSXZ0L7vbt28f8u4iIiIiIiIj8T723BXvuuedYsWJFY2YRERERERER2WnUe5Xy66+/HsMwSEtLY9CgQey9994MGjSIvn37YrHUu44XkTjp3qMLnbvmkL+tgO+++alJMvTs1Y1OXTqyZfM2fvju5x0+PzMrg1NPOxab3carL73J6lVr45Ay/nr36UmHju3YtDGPn39c2tRxpKWz2rG4UzFNE7O8EMxIUydqsQx3GobVjhkowwx4d7wDmwOLy4NpRv76WZiNnjERjKR0DIuViL8Mgr4d78DmxOJKwYyEMcuLgB19HwyMpLSGZWiwv2XwlULI3wQZRGRnUO+C+/PPP2fJkiV8++23fPvtt9xxxx2Yponb7WbAgAGVRfiQIUMaM6+I7KDOXXJ4aPattEpLJtkOvpBJadDkxhvu5f13P0lIhu49ujDzoZtplZpEkq0iQ0nQ5Nqr7+TjD7+o9XyLxcLCd+fTq0dnQiVFYJpMueA0tuQXcchBJ1BcXJqAV9Fwffrtzn0P3EB6kpMkG3hDJsX+MJddcjNLFn/f1PGkpTEshDydKSuJsPazVVgdNnL26obL5sVauq6p07UoQUc6WyMePlj4FblrNzJ03z3p37cbWZHNdSv2LFYKHbuxYs0WFr33HmlpKRx2+AFku0MkBbbG/wU0Er8jg62hZN5/7XM2bNzC8OGD2KN3JzJDm+q2xZrVRoF9N/5YvoGPPniXVtkZjB4zjFYOH+5Afp0yRJKyCVizWPfDKsqLymm3R0fS26ZgK1kF4WADX2Hd+JyZbPG7ePflz9iypYCDRuxDr+4dK7ZYS1AGEdl5GKbZOJdfS0tL+f777/nmm2945513WLNmDYZh8NtvvzVG9wk3cuRIclevo3SbtamjiNRbq+wsXn/zMdJDpZh/3+fZMPAlZzJlynV88fk3cc3QdrfWLHh9LqmBEsxw1Qze5EzOPfdKvvn6x5h9fPDJS3TMcBMo2FbluDXZQyglnb69Do5H9EaV06kDL73yMCneIsxI+H8NhgVvcgbjx0/j99+WNV3AZmCApxMAg6wZAAzxVXw89WtbUbBkDavY9Mk2dCAAloEjEh2xWQll9OSb5xeT90fV4rrHQf3ptV9HrCW5TZSsZQk5Uvl6RSn/d/5NhMP/+7fZuk0r5s6fQdvIBvj7764aFCR1ZtqUGfz2859Vjl946QSOGT2gRRTdAUc6n/68mf/83238/ath+4678cgTN5MdWAt//931T4bBNldnLjjnelYsW1Ol6aobp3DYft1w1VJ0m0mt2Lgelsz/sMrxlNbpHHDuaOyFv8d9BIfPkcm7X67ipmvuq3K8S/dOPPTIdWT51rTYkQsi0njGnHcNht1dp4XEG2Xsd25uLu+++y5vvfUWb731FqtXr8blcjF06NDG6F5E6unC/zubDAJVi20A08RVXsBV114Y9wwXX3ou6firFtt/ZUgqL+Sa6y6KeX7Xbp3onLNbtWIbIFxWgttiMu7Ywxszclxcde2FeIJlVYttADNCkq+Y6274v6YJJi2S4fKw8Y/N1YptgGUf/URZuRUs9R7EtkspIJ1Lp95SpdgG2Jy3lVtvfBivPSvm+aY7g5deWlSt2Aa47455bPbawGj+U+0KSOWqS+7gn/dh1q/dyP13P4nfmRnz/LArk8fmvVqt2Aa4+doH2BZKqjVDwJ7Nkqc/rHa8dHMhv777I2Zydq19NFR+OKlasQ2wavka5j36CiFX7PdBROSf6v0JMH/+fKZNm8awYcM49NBDue222ygsLOTkk0/mhRde4JtvvmHevHmNmVVEdtD+++9NxFdec2MkQquMVFLTPHHNMHhwf8LemjOYkTBtszNwu11Rz5/6f/8mVFi92N4umL+VadMmNDhnvPXZozuRQM1zAM1QkC6dtPOD1F3Qnsmyj3+J2r78s98hWYVBrWwO/vwzl4C/5uHSX372LUUhe8wuCiPJvPD0f6O2v/n6RxhJ6Q1JGXeGI4nvvvmt2kWH7d5761OKw9F/TwMURpJ47cV3orZ//MFiDHdq9AzOFPL+2BB1uvfaJX8SsqbHzNBQhjuVTz5cErX9tZfepShS+4UDEZG/q/fl7+nTp2O1Wjn00EOZOHEiffv2bcxcItIIjNoeEIngdDqBkibLYJoRHA4HXm/N8yRTU1MwI9GHEJqRCG53cgMSJoZh1PpOYLVao37hFfk7EwshX/Q5tcFyPyaW2n8H7OoMKyUlZVGbTdMkFIr9b9LEoLQ0eh+FhcUYFssOLxuWUBYLxUXR18IIh8OEw7GHckdM8PmiLyxWWFgCRoxpehYLgfLo50fCkWp33xudYaGwKPrnod8fINKsf5Ai0hzV+w73qaeeSo8ePXjnnXc4+eSTOfnkk7nrrrv4+OOPKSmJ35d3Eam7zZu3YdiiX1cLmgbbttZtIZv62ppfhGGN/iUrGIaiouKo7W+89i62lOh3RWwpHj75ZHGDMiZCUVEpRN3BwaDcF1CxLXVmi5TRdo9OUds7DuyCJdAyFhNsUiEfe/TpHrW5XYe2JLtif1VyG372HTYoavsho/cn4m3e34tMfzmD9ol+46R7zy4k2WNXmkmWIAMG9YnaPvygwZj+6O+D6S+jTc92UdvTO2ZjNeO7UrjpL2XY8Og/yz0H9cFt0aJpIrJj6l1wX3PNNSxYsICvv/6amTNnMnjwYL799lsmT57MkCFDGDduHDfddFNjZhWRHXTnHbPxOWseMh52JfPaa+8SiXH3uDHcc9cc/K6aC+awK4UXno8+FBPg1ZcWErE7sdgd1doMixVbRiuuv/qORskaTw888DjBKMMpQ+4UHpv7QoITSUtmlG+l14h+2JzVhzsnZ6WS3TUb06+Cu1amSWZShMOOPKjG5iuuOZdMimJ24Q7kc+HFZ+JwVv8d1bN3V3p0bdv8t5QyI7RJszPswMHVmgzD4IprzyUtUhCzi+RQPpddNQmbrfoF1j332oOObVNjLz4XCZOUBNk9q0+vMQyDgcfuh9WXV/traYhwiI5tU2u8cGC1Wrn0P+eQEor9PoiI/FODV/FISUnhwAMP5P/+7/+47bbbuPrqq+nUqRN//PEHzzzzTGNkFJF6WrL4ex59/GW8yZlYXW4ALHY7gaQ0lixdw523PRT3DJ99spgnn30DX3LG3zI48Cel8cWPy5l5f+1rPRxz1L9xduiCIyOrYvEhw8Cemk5S5+5cOOU6ysvrsV9ugr313/d59b8f40vKwOKsmAtpcTjxJ6Xz3uc/8NQTLzZxQmlRzAhOby4jLzmaDgO7YVgs2Jx2uh3QlwMvGIO9dGVTJ2wxUnybueSS8Vx29blkt87CMAz69N+dec/cwcBu6Zi+Wi5cREJ0cJXy9Mv3MeygIVitVjypKZx1zgk88NA1ZAQ2JuaFNJDHv4nrbjiPCy+dQGarDAzDYMCgPjz5wt30bmuvfV/yUIDOqQHmv3Qf++w3EIvFQlp6KpOmjOeOuy8lPbCp1gzWktXsc/J+9DliMM4UNxjQqkc7RvzfUaQ4ChOyH3eafyO333Ux504dT1p6KhaLhX32Hcj8l+6la3qo+V88EZFmp0Hbgi1fvpxvvvmmcj/uvLyKK4+tW7dm7733ZtCgQZxyyimNFjaRtC2Y7ExyOnXggqlnsfvu3diyeRuzZj7B99/+lNAMnbvkMHnqmfTo2ZVNm7bw4Mwn+PH76Is+/VNSkpvrp1/KEUeOxGKxsPir77ns4hvZnNf8t9v5ux49u3LB1LPo0qUj69dtYuYDj/HbL380daxmQduC1YPFSiSpNWGrBzCxhQoxyrdq26L6cKVSYHqIGFYcZgBPpACCO1BcWW147Vl4cYIZIc1ShtXb8u6Gmq40Ck0PpmHBiZ/kUEHd9uDezmqn3J6BFxcWM0K6pRTDW7hjIZIyCNqywGLFEiqvuLO9IxkagenOoDCSTMSw4MZHUrBAe3CLSKUd2Ras3gX3kCFDKC4uxjRNunbtyt57781ee+3F3nvvTYcOHerTZbOigltEJLFUcIuIiEhLsCMFd71XKT/66KMri+zMTG09IiIiIiIiIvJ39Z7DfcUVVzBq1Kg6F9tFRUWcfvrp/Pbbb/V9ShEREREREZEWo8GLptVVMBjk66+/pqgo9mqfIiIiIiIiIjuDhBXcIiIiIiIiIruSes/hFhHZES6Xk6OOGcMefXqwNncjLz7/BoWFiR3x4na7OPq4w9l9926sWbOeF59/g+Kikh3q48ijRjNp0qlYrBbmP/UKzz71SpzSikhCWe34HRkEIuC2hLD5Clrkau9mekdKQg5sFkgKbgVv4kcWRlr1oNhnwWazkBLeCiVbEp5BRKS5UMEtInE3eszB3HDTxaQQwBYJE7FaOfvsE5n/9AIeuHduQjL8a9yhXH3NVJIJYouEMC02zjnnZObNe4HZDz5V6/mt27Tio89ewUmYYGE+YDL96gu44cZLOGL0eJb9qX2PRVqqUmcbVm4q47FHniJv0xb2GtyXU0/7F60thVgDO3ZRrqkYyZnkma1Y8PTbfPzBl3hSUxh/5jH07duT9JLlQCT+IdJ2Iy/g4ZV5b/LZx1+TmubhtAnH0XuP3mQUL43/84uINEMquEUkrnr17s7NN1+Mu7QAMAn/dTwFmDB+HGtzN7DglbfimqFv/95cf+1UXGX5AFUynDvxeHLXbOCtN2Nv6/DBJy9hKdyMz1teeSzsLcfidLLw3fn06LxfnNKLSDz5nFm8/OYSZt79eOWxZb+vZMELb/PYs3fSwxOEoK/pAtaFxcaGUCanH3cBhQX/u6P97eIfOexfI7j0srNILVkW3wwON+vLkzj9uPOqjBz65qsfGHvMaKb835mkq+gWkV2Q5nCLSFxdceVkkv2lQPWhmQ5vEZOnnhn3DP+5ajJuf813qRzlxVx40cSY548YNRy3taLA/qeI34/FV8bESac2SlYRSazCSDKz7nmi2nG/P8ANV99PyV/7wjdn/tQcHrzn8SrF9nZvv/EBG7cUg80R1wxlyZ25//ZHapym8/or77B1WzFY45tBRKQ5UsEtInHVtUsHIsFAzY2micftJMWTHNcMOR12wwwFo2SIkJGajNMZ/YvgWWefRKi4IGp7oKiA8acd09CYIpJodhe//PQnZpS52n/8tpwSv5HgUDuuJGjn/Xc+idr+9hsfYknbLa4ZyvwRPlr0RdT2RW9/gjW7U1wziIg0RwkruC0WC+3atcPlciXqKUWkJTCax5fZaF+468Ko6KDRsohI4hiWWn4HNZPfUbHF/v1T62tsJEaM9ypRGUREmpuEFdyZmZl88MEHDBw4MFFPKSLNwIqVa7HYo9w9NgxKyn2UlpTFNcOatRsw7PYoGSwUFJcRCES5Aw7MfeQZ7KmZUdvt6Rk8+dTLDY0pIokW9NGnT3cslpq/DvXu2wOPIwGLjTVQqj3EIWMOjNo++oiDiBRtjGuGFJfBgSOjr2UxYvRwwlvWxDWDiEhzVOdF004//fQd7twwDJ54ovq8KBHZdcy4ZRbPPHMf7lB+tbvAAXca90+fFfcMt948kyefuAtXsGJ18b/zJ6Vxz813xTz/o0WfUxYysbuTCXurXhywOF1EHMk8NufZxo4tIgmQbiljysVnct8d86ocd7mcXHvTVDzh6NNJmgtHcS7nTzuTxZ9/S/62wiptR4wdxW7ZHijJi2uGpNI1TL3sbL79+keKCourtB1zwuG0yvRA8fq4ZhARaY7qXHDXZ7hlQ4ZoisjO4c/fl3PFFbdx082X4jFC2CIhIhYrJaadJ554mdcXvBP3DL/+/DvXXHs31143jZR/ZJgz+1neefujWvs4ePixfPzZy7gzMgkW5mOaYE/LIGx3MnrUyXF/DSISH65APkcfthcD9tqDxx99mbxNW9l7n76ccMoRtDHyIdDMVygHiITYzbqV+a/M4o1X3uWjRV/iSU1m/JnHskfvzqSW/Bn/DAEv7dPKeHrBQ7z+0tt8+tFXpKWlMn7CcfTq1VkrlIvILsswVRXXaOTIkeSuXkfpNmtTRxHZKTgcdv511Gj22KMH69Zt4uUX/1vjarbx5HI5GXvUaHrt3o3c3A28/OJ/d3g4+2FHjOCcSeOx2aw89eQrvPjca3FKu+sZ4KlYUGnQX6tCD/FVfDz1a7sVgKxhFdMCbEMrpiZZBo5IdETZmVnt+B3pBMMGbmsIq7diK8OWJpLegbKQE5sV3IEt4C2u/aTGzpDdk2Kfgd1mITmQB39tySgisrMYc941GHY3ixbF3lYWtA+3iCRIIBDk5Rf+S1POdPb5/Lzw3OsN6uPtNz/g7Tc/aKREItJshIM4vVtwNnWOBrIUrsPT1Bm2/El6E2cQEWkuGqXgLi0tpbS0lEik+sIi7dq1a4ynEBEREREREWlRGlRwP/PMMzz++OOsXbs26mOWLtWcHREREREREdn11HtbsGeffZYbb7yRnJwcpk2bhmmanHHGGZxzzjm0atWK3XffnZtvvrkxs4qIiIiIiIi0GPUuuOfPn8+wYcN49NFHOeGEEwA48MADueiii1i4cCFlZWUUFhY2Vk4RERERERGRFqXeQ8pzc3M55ZRTALDbK1aODQaDAHg8Ho477jieeeYZJkyYUOc+P/74Yx555BGWL19OaWkpbdq0YdSoUUyePBmPJ/YSIC+++CKPPvooGzZsoEuXLlx00UUcfPDB9Xx1Is2H3W5n7NGjOfnkcdgddj7+6CvmPfIshYVFde6jS9dOTJ56Fj16diF/WwEPznqSr7/6Lo6pm6eevbox5cIJdOrcgc15W5n5wOP88N3PdT7f5XJy/EljOfrow7BYLbz91kc89cSLlJWW17mP3ffowZSpZ9Expz0bN25m1v2P8dOPv9Xn5bRo/fbszTkTx9O2bTbr129i9qPzIdef0AyGy0PQkY1psWOEA9gDmzH9O7BqvcVKJKk1YVsqmCa2cCFG2VYwq69nEjc2J97kDpSFKnbUSLMFsJfkQg1rqkTlSqXA9BDBip0gaWYhZsC7AxlcBFJyCBsVy405KcVSuCax70Nz4E6lwNYWfwgwINMNjm0rgHCduzDc6QQcrcCwYgl7sfo2Q3AHtiVzJONP6kDEcAAmTrMES+FaoO4/C9OdTmEkhQgWnARICRdAKLH/NhvM5qDMloHPdGBgkmEpw/Du2H7qEXcGhZFkTCy4DD/JoQIIBeIUuLkyCLszKIokYWLBbfhJCuZDOFj3LuwuSizpBHBgIUyGpQy8hXFLLNIc1bvg9ng8hMMVHyIpKSm43W42bdpU2Z6cnMzWrVt3qM/CwkL69+/PaaedRnp6OsuWLeOBBx5g2bJlzJs3L+p5b775Jtdccw3nnnsuQ4cOZeHChUyePJmnn36aAQMG1Ov1iTQHWa0yeeHl2WQn2bD6ysCM0OOEQzjxxCOZOvlaFtehaJ560dmcPn4cKWEfYZ+X7ult2POB6/jxj9VMOP2iGhc73Bldde2FHDNuFEkhLxG/jx4Z7dlrznS++m4p559zRa3nd8hpxzPPzSLTDoavFEyTHmeO44wzj2PCmRez9Nfa97m9YfqlHHnY8IoMAT899ujI4Lkz+OTLH5k25ZrGeJktwm23XU3fjp3Z+PnvbPhiDZ7W6dx97RUsX57L3BmPJSRDOK0Lm1YW89vbH1CeX4KnTQZ9jxxMVutMrKXR1yWp5EzG58zhl/9+w4afV2FYLOQM7knvQ/rjKF0BwfgXKJGkbNb7k3ng+jl8vOhL7HYbRxx1CGeffwrZ4Q3gq33bveKkjrz/0Q/Mfeh5NudtpXvPLlx02QT6dGlFsr/2z/BIcmvKjWx+eflLNi/NxWKz0n5wD3ofujeu0mWwIxcwWjCvJ4d1BSHuu+1uvv7ie9xJLsYdN4bxE4+jdXAd+Etjd2AYhNJ7suaHtSz74G38pV4yclqz51FD8bhLsXi31JohlNyOsnAqvzz3BVuXr8fmdJAzdHd6HLwnrsLfai8WDQtFSTksWPAxTz32KkWFxfQf2Jv/u+Jsume5cQYK6/6GNCG/I51VhRbuuvVBfvj2VzypKZxyxlEcd9xI0svrcCHIYqXAlcOLL77Hc0+9TklxKXvt059LrjibzqlB7IG6X+xu0Sw28l0deWb+m7z47Jt4y30M2W8gF102kZxkL7ZA7dvN+ZyZ/LHBz1233cPSX5aRmZXOGWcfxxFj9iWtPJeWuO2eSH3Uex/uM844g/bt23PLLbcAcOaZZ1JUVMTDDz9MJBJh0qRJWCwWFixY0KCAL7zwAtdccw2ffPIJbdq0qfExo0ePpm/fvtx1112Vx0466SQ8Hg+PPPJIvZ5X+3BLc/DK6/Po3TqZSOAfX94NgxJ3BiMOPIHysuh3V/cZuhcPz7oBZ1n1K/thVzLzX/mA22fMauzYzc6oQw/gzlsuwV5eWK0t7PYwc+4rzHn4qZh9vPfh87R3RjBDVa/sGxYr+bYUDtz/mMqLkDU5cuwhTL92Mvby6l/WQm4Pd816hicff7FuL6gFO/mUozlh2MHkfvJLtbZuhwzk7Xc/w/vOr0D89uGOJLfhz2+28sf7P1RrG3Dc/uR0s2GUx9g32DDwp/Vm0R2vEvRVLWLc6SkcPPVw7AVxXjDUYmODrRMnjT0Xb3nVu9HtOrRl7tN30arsj5hdlLta88Cchbz03MJqbTffeSmH7NUGwxfjS7XFRlnK7nx4+4uEg6EqTSnZaex3/pG483+q+2tqqZwpLC9P5/RjLyAYqPr7oXvPLtw75ybalMe+IBf2dOLbN35h48+rq7UNP+9wspLzY4++cCRRZMnh47texvzHRdS09q0YMvEQ3AXV/839XbGrHddc9yhffPpNleOGYTDniVsZ2M7csZEPTcHu4td8JxNOvrTaxeS9Bvfj9tsmk+ZbH7OLIlcHLr7kXn78rurII6vVyuPP3cXuaWUt745/PRS4OnLB+bey7PeVVY7b7DaeeeV+uji2QjgU5WwwXCl8sdzL1HOuq9Y26rDhXHXpyaT4N9VwpkjLsCP7cNd7DvfYsWNZtmwZgUDFl40pU6awYsUKDjroIEaMGMGqVauYNm1afbuvlJ6eDvxvuPo/rV27ltWrVzNmzJgqxw8//HC+/PLLynwiLU3Xbp3o2DarerENYJp4CDD+9GNj9nHxJZNw+Wu+y2X1lTH2qEOwWOr9a6DFmDptIo4od/us3hJOPe3omOfvvc8AspId1YptADMSJtUa4Yixh8Ts4/zJZ2L31pzB5i3lzAknxDx/Z3Hqycew7vOah9Cv/uhnjjxlTI1tjSlky+TPD36sse2XN74mZM+Oeb6Z1Irln/xardgG8BaWsnHpBnDFdyfkQGpHHnv4uWrFNsCGdZv46vPvsKS2jtlHYdBRY7ENcOctc8g302KeH0rtxNK3llQrtgFKtxSxZcVGjOSsmH3sDAqd7Zh556PVim2A5X+u4s/fV0JKq+gdGBb8IWeNxTbA9y9/QdBZ8w2H7fxJHfllwRfVim2AovVbKdlcAs7k6B1YrOQVBqoV2wCmaTLjpocpNDJiZmgOSqwZ3HbTwzWO3Ppuyc9s2FoGlhiDO602cjcVVSu2AcLhMHfcOodSa/N/HxrM5mTZyk3Vim2AUDDEfXc+jteeGbOLfDONGTc+VGPb+29/yjavAYbRKHFFmrt6f9M+9thjefHFF3E4HAAMGjSIN998k8svv5yrrrqK119/nYMOOqhefYfDYfx+P7/++iuzZs1ixIgRdOjQocbHrlxZ8cugS5cuVY5369aNYDAYc8sykeZs4KB+pDmifxgZfi/Dh+8Ts4/WrTMxQ9GvQNsxyWoV+0NzZ5CR5sGMRL/77LRZcbtdUdv3Hz6YZGv0wUD2cIADDhgSM0NaanKMoYwmSS4HVuvOPaLGMAzsGETCNb8P4WAIe7zfA8PAW1SOGan55xnyBwn4ov+bAQgbSWz8NTdq+7ofVxOxpTQoZm1KA1a++HRJ1Pb33/kUvy1G0W+1s2rluqjNBflFlJbFvosXwMmWP6L3kffzaiLunb848Ydg8RfRp/d8+N7n2NLbRm03HG62rc6L2l66uZBQJPYMwIjhZOvy6HduN/yyCosnetFvONx8tyT6HfAVy1bjDTX/4qg8ZGXpL8uitn/5+fcYMS48GM4UPv/026jtP333G17T3qCMLYHF5WHRe19Gbf/qs28pxxmzjzJfmA3rot/B/vXnPzHs0T93RXYmDdqH+586duzImWee2eB+Dj74YPLyKj58hg8fXmWo+D8VFVUMz0xNTa1yfPvft7eLtDRlZV5CUYoCqBjKXBZjODnUYXaUYeDz7cCCPC1UXd6HYA136bYrKSrFjHF90rBaKS2NPUez9tk7Rswh6TsD0zQxrLGv88Z9xIVpYnXE/sJsqaXoN4hgd0X/sulIcmLZgUWq6sNigWRPMuTVPM86NdWD1Yjx/5wZISUlxh1PwO6wx15ryzSxux0Eymr+HWJzObAY5k4/S9M0TVJSkiksqPn7Rlp6KmaMobdmJIwjKUYRaBhYrLUVuxFsTgehGkZdQMX/k8S46EgkQmp69As0NpsVq9WyI+u/NQmrxcDusNc42gAgPT0VzFjvQ7jiMVG4XE4szf+6Q4OZZpiMjOjvQ4onGUstn2l2uw3DMKJ+9qWmptQ4IkNkZ9TgbzZ//vknjzzyCNdffz3XX389jz76KH/8EXveWG3mzJnDc889x/Tp01m5ciXnnnvuTv9FVOSfPv7wC4piLATqtTp57LEXYvbx2adLsLiSam60WNhSUExJcS2L+ewEfvjhN6wud41thtXG2vV5hGKMBHj9tXcpCkf/llVi2nj6qVdjZvjllz+xOGsu0gybnVWro98p3Jms3bgJd3rNd39TstNYl7sx7hmcbgOnp+Z/F6m7ZWK3xV6B1xbMp+dBfaO29xi+B3hjzAFvBJnWUk45PfpUiBPHj8VSFGOuaiRM+3aZpHhqLvT6D+xNqiP2526SUUin/ftEbe+yfx/Mgp1/lFl2Mhx90hFR2484ahThzdHvuhL0kdExC0uUi1Ht+nXBFo69AJ6TEjoO3T1qe86gnkTyN0RtNwPl7LVX76ijbEaNOYBUa/O/OJtm9fKvo0bV2GYYBvvuPwDTF/0zz/SVcMBBe0dtH3fcoaRb6r4rRUtllhdy+JEHRm0/8dQjSbfEXhAx1RZk/wNrHoXncDro1avTLjEXXgQaUHAHAgGuuOIKxo0bx1133cULL7zACy+8wJ133slRRx3FZZddVu/507vvvjsDBw7k+OOP58EHH2Tx4sW89957NT42La1ijllJSdUPo+Li4irtIi2Nt9zLgtfeI+iu4a6Dw8WavEIWfxF96BvAffc8SgEODNs/BrMYBr6kDKbfcF8jJm6+Ztwyk0KLG+OfXyYNC+XuNG687u6Y52/dso1PPvuWsKt6cRJxJvHbn7ks+7P6XLe/u3n6/RRbk6tnsFgoc6VyfS0Zdha33T6LbuOGYHNWvctsdzvpOXYoz8x8Lu4Z7N4N7H/2oVjtVf9dOJKcDD1jBDZv7KLf9JeR3SWD3fp2qtbW/cC+pHjMHds2px4iRZs4cMQ+7DusenFw2oTj6Nguo9Yvs5lmAfc+dF3Fney/H2+VwY23XkRKsJaLBoUb6TiwG1nd2lVr6jZyAK4kdoltlKxbl3HcyUeyR79e1domXzyRzFQ31HLTwO7bxNCzDsH4x5zWpKxU+h81GEt59CHnAJaCtfQ8eE/S2lcfNt7n6P2wW7zUtjVYhlHErfdcUS1Dh5x2TL3oNJy+bTHPbw7svnzOOe8EOnfLqXLcMAxuuv0SMi21X2DOtJZz/a0XVTvevWdnJpx9DFZffC+mNQumSbY7yCVXTqrW1Ld/L447/pBat1lzB7ZxxdXn0LZd1bUkrFYrd828mgwKGzOxSLNW71XKb775Zp566ilOOeUUxo8fT05ODoZhsGbNGp566imeffZZTjvtNK666qoGBTRNk379+jF16lTOOeecau1r165l1KhRzJo1i1Gj/ndV86mnnuK2227ju+++q5xnviO0Srk0F/936SSOO+5wki0RrIZBWcTgp1+XM/X8q/B6a7/jkNOpAw/NmUHrjBSSrOALm5QGTa6/7m4+fP+zBLyC5qFHz67MfOhmMlNcJNkMfCGT4kCY/1x2K199GfvCBVR8Ybv+pks49NDhJBsRDAPKwgZfff0jl/7fTVEXdvy73ffowf0zbyIz2YXbBt6QSbE/xGWXTOebr2texGtnNGCvftx84+WEt5YSLCjDnpWCke5m3vR5rFq6ikF/LUoUr1XKAXClEnC1J+/PjRRuKCCrUytadcnGUZ4LgTrcwTIMQp7OeMsN1v6wCovVSseBXXDZvFhLEzRawWKjMLkr6zZs4903P8LlcnLYvw4mO82Fp3RFnboIO1PZaqbz2SffsXLFWgYN7sOe/XuQFdkMdVmR2mLDm9aLsgIv679fjtVpJ2dQTxyOEK6iGHd1dzZ2N5udOaxeuY4P3/scT2oyhx05gnSPg8w6/iwi7kyCjjZs+DmXsvxS2vRqR/puqdhLV9XtwoXNgdfTk5LNJWz4ZRWOJCc5g3pit3hxFse+ILhdwJHOtoiHD97/ik0btrDf8L3o1aMDmcFNEG4hF0+sdgrsbVm2chOffrSE7DaZjDxkP7JsZbgCdduL2+fIZFsoifff+ZytWwo44OB96N6lDRnBjTFX5t7ZlDuy2BZw8u7bn1FYWMKIUUPp0iGL9MDG2FMUtrM5ybe14dela/jqix/omLMbB43YhyxLEXa/pnxKy7Yjq5TXu+AeMmQIBx10ELfddluN7ZdeeimffPIJixcvrk/3lX744QdOPPFE7rnnHg4//PAaHzN69Gj69evHnXfeWXns5JNPJiUlRduCyU7B4bCz95CBOOx2fvrhV/LzC3e4jy5dO9Glaw7btubz4w+/Nn7IFqJHz650zGnP5s1b+eWnHd+6yZ3kZtDee2K1Wvj+u58pLqp9r+N/6rl7dzp02I1Nmzbz2y8Nm4LTkvXu05PddmvDunUb+fP35QzwVNwxTkjB/RfDlYJhc2IGfbG3XYrGYsNwpwImZnlR7Xv8xoPdhSW1DRAhUrARIjteEBguD4bNgRkor9/WTw43Fk9rMCNECuuXYafgTseasRtEQoTzVsaeLxyF4U7FsNqJ+MsgWI9h3M7kigXSIuG/hpHv+P+ThjsNw2qrf4bmwObEcCVDOIzprU9xZ2AkpYLFiunbNbYCq5mBkZQGFkvFcPz6jFqxu7A4kzHDQUxv7ft3i7QEO1Jw13vRtFAoxJ577hm1feDAgXz44Yc71OfkyZPp27cvvXr1wuVy8fvvvzN37lx69epVeff6yiuvZMGCBfz22/+2bJgyZQqXXHIJOTk5DBkyhIULF/LTTz8xf/78+r04kWYmEAjyxadfN6iPVSvXsGrlmkZK1HIt+3NlrcO/Y/GWe/nsk68alOHP35fz5+/LG9THzmDpr3+y9NfY+xPHm+krxaQB6xhEQphlTTzENOgjsq1h/7ZNX0nDFjcLeBucYafgLSTsLWxQF6a3uGE/C39ZRaHcoAxFLX+xu5Afs7QhRfJfF9F2eSZmeWHDugj6iLTUCzcijaDeBfewYcP47LPPOOWUU2ps//TTT9l///13qM/+/fuzcOFC5syZg2matG/fnuOPP56JEydWDguPRCLVFlA78sgj8Xq9PPLII8yZM4cuXbowc+ZMBg4cWL8XJyIiIiIiItJA9R5SvnLlSqZNm0ZOTg6nnnoqOTkVC1SsWbOGp59+mnXr1nHPPfeQmVl1j9/09PQGh04EDSkXEUmsphhSLiIiIrKjEjKkfPt86j///LPaE22v4Y84ovo2GUuX7vicSREREREREZGWpt4F9wUXXFBt6wgRERERERERqVDvgnvKlCmNmUNE4mjcMYfx73NOJSXJRSgcYcGrbzP3kWfrtK2YyE7JMDCT2xC0pBEKhrE5rNhD+RhlW+rehTOZoLMtwZAFwzCwWYMVe3hrcaAdY1iIJLclaPEQ/utn4QhuhfId2PfZ5SHfTKOkPIjNZiXVaeIJbav7isoWK5GkNn/LYMER2gZlCdx72mIlktSWoCWFcDCM3WHBHtwK5bvAvs8iIjuxehfc/1RSUkJSUhJWq+Y8izQnc+bdwdD+PbF6S8D0ghXOP30sY48azTHjJlJa0rDVbEVaHMNCKKMXv77zE2u+XoQZiWC1W+k6vA+99u+JtbD2ldMj7kyKyj1889gHlG+r2OYmdbdMBp96ECmuPPBp65s6sVgJpffih9e+YcOPKzFNE5vTTq9RA+gysCvWotp3FPA5M1nyez63Xj+DLZsrCuQ9+vXiptsuIsdRWPu+6lYbwdSefL/gazb+vApMsLkc9D50IJ36dcZavLrhr7M2VjvB1B5898pXbPp1DZhgdznoPWYQOXt0wlqsFeBFRFoqS0NO/vnnn5k4cSJ77rknQ4YM4euvK7Ytys/P57zzzmvwHtwi0jDjjjmsotj+x/7AZnkJ7ZOtTL/1iiZMJ9I0wint+f7lr1n91e+YkYp/F+FgmGUf/MTSj//ATG4buwOLFZ+Rzaez3qwstgGKN+bz0f2vE3B2iGf8nUooJYcvnviI9T+sqFz/JeQP8uubS1j1w0ZIzordgc3J8s0h/u/8GyuLbYDffv6Ds06+lC1Gdu0ZknP4fO4iNv5UUWwDhHwBfn59Mbm/bYWkzNgdNIJQSic+e+Q9Nv2ypjJD0Bfgp1e/ZMOyIoyk9LhnEBGR+Kh3wf3dd99xyimnsGbNGsaOHUsk8r8v85mZmZSWlvL88883SkgRqZ9/n3MKVl9JzY1+L/sM7o/dbk9sKJEmFiSZDb+srrFt5We/EbSmxzzfTMpm6bvfU9MmH+FAiNXfLMdIymiEpDs5w4I/YKNgdV6NzX+8/wNBW+yCu9SWzv13PV5jW3FRCV988SOGyxMzg9drULR+a43NS9/5rtYMDWaxUl4apnhjzUPHf33rGwK22i8ciIhI81Tvgvuee+6hW7duLFy4kIsuuqha+5AhQ/jxxx8bFE5EGiY5yQ1/uxj2T1YzTEZmWgITiTQxw4Kv2Bu12YxECPpDMbsIGy7yc6PP9d66Mo+wxV3viLsMq52SzYVRm0O+AKFg7J1LA6ad339dHrV98Zc/gT3Gz8LupHhT9DnSwXI/oXDMCA1m2F0Uro8+V9xf4iUc0SK1IiItVb0L7p9//pljjjkGh8NR42rlbdq0YevWmq8Yi0hihMLRi20A02KlRHO4ZVdiRnAkOWM+xOqIvbyJQYikjJSo7SlZHiwE6xVvlxIJ4U5LjtpsWAystthfU6yEadOuddT2rt06QCTGzyIcjPmztNistWZoKDMcJDkz+l14q92KRcvjiIi0WPX+FLHZbFWGkf9TXl4eSUlJ9e1eRBrBglffxkiq+cukxW5n1ZoNeMuj3+0T2Rk57CFSd6t5Xm52rw7YiX0RyurdQu9DB0Rt7z58j8Subt1SRcIkpdpxRSm6Ow7qgT1SFLOLdEr493kn1dhmtVo5/IgDMMsKoncQDpGS4cbpqfkueKd9emEPF8bM0GChAKmtU6JeCOqy3x7YQ3HOICIicVPvgnvPPffknXfeqbGtvLycV155hcGDB9c7mIg03LxHnmVtUQCcVb9MWux2iuwerrj0liZKJtJ0bOXr2G/iKFKyq06nSO+QzeAT98datiF2ByE/aenQ98jBVUZ4WWxWBo8/GJdRWGWRQonO4V3HAecfjju9atHdqkc7+h0+AKNsc8zzTX8p+w7qxvizjq7ys3C7Xdzz0LVk20przWD3ruWA84/AlVr1JkHrXh3Y45C+tWZoDPbydRxwwRHVCv+2e3Si10G779B2dSIi0rwYZk2rvtTBjz/+yPjx49l333054ogjuPzyy7niiitITk5m7ty5bNq0ieeee47dd9+9sTMnxMiRI8ldvY7SbRrHJS1bckoS02+5giFD9sRqhjEtVlatXs8Vl93KmtVrmzqeSKUBnk4ADLJWLDg2xFfx8dSvbcX0pKxhFQv82YYOBMAycET9n8zmJJTcAV85lBeWkpyVitMRxlaWC+HYc7i3iyS1JmTPojivCMNqwdMqBbt/M4ZP+ybvCMPhJuDugLc0jK+4jJTsNJy2ANaS3DpfuPA6W1EYdrNi+Vrcbic5HVuTaRRh9ddxeza7m2BSB7wlIXwl5X9l8GMtWZu4iyeOJILuDpSXBPGXePG0TsNh8WEtTWAGERGpkzHnXYNhd7No0aJaH1vvghvgyy+/5Prrr2fNmqr7Q+bk5DB9+nT22Wef+nbd5FRwy87GbreTnpFGaUkpXq+vqeOIVJPQgns7wwJWG4SDUN+PQ6sdMOtcqEsU238WoSCVe2PtKKu94ucYqefPojEyNJTFWvGnKTOIiEhMO1Jwx14Zphb77rsv77zzDkuXLmX16tWYpknHjh3p169fQ7oVkTgIBoNs2ayFDEWqMCMQCjSsj7AWSGsUzeFn0RgZGioSrvgjIiI7hXrP4V66dCn//e9/AejduzdjxozB4/EwY8YMjj/+eJ544olGCykiIiIiIiLS0tS74L7jjjtYuHBh5d/Xrl3L5MmTWbduHQAzZszg+eefb3hCERERERERkRao3gX377//zqBBgyr//tprr2GxWHj11Vd58cUXGT16NM8991yjhBQRERERERFpaepdcJeUlJCenl75948//pj999+fzMyKvU3333//aoupiYiI7HRsDrA0aEkUATCMhr+PFmvFwmf1zmABm6thGUQak2Gp+P+6pbPYKv6Ni+yC6v3Jlp2dzYoVKwDYvHkzv/76K8ccc0xle1lZGRZLAz70REREmrFIeheCljRKtxRiWK2kZHqwBbdgKVnf1NFaFruLAksrthZ6KSgoZrd27chwRUjxb67zdlh+ZysKI0msXbsJl8tJ2zbpZJoFWAK178MNQFI6m81WbNlaREF+IR1yckhLspHhza3/iuciDRBxJFNgZJK3pZiysnI6dGxHus2H29eC9mQ3DMqcrSnw29iwbjPp6RlkZySRYW6DgLep04kkTL0L7pEjRzJ//nwCgQA//vgjDoeDQw45pLL9jz/+oGPHjo0SUkREpDkJZfZmzY/r+O2N/xIJVxSFVoeNQaeMpHXHrlgLVzZxwhbC7mJdqBWTz7mW9Ws3VR7e74C9uf7GC8jwrq51u7YSVzuefvEjHpv9ApFIxc8ixZPMHfdfyYCOadgCRTHPN5IzWFmawtR/X8KmDZsrjw87eAhX3TiVViXLAO2DLYkTcXpYusXChedeTFFhxV7yhmFwwqn/4pyzjyS1fF0TJ6wLg0J3J26Z/igfvv9F5dG27Voz65Eb6eQ0MAPlTZhPJHHqfQt62rRpHHLIIbz22mts27aNW2+9lVatWgFQWlrK22+/zf77799oQUVERJoFZwrF2/z8suCLymIbIBwI8fUT7+ALuTXEvI7yLa04d8JVVYptgC8++YZZDzxHwNUq5vmGM5lvflrD3Ieeqyy2AUpLypjy72vZEkmtNcOmcBbnn3V5lWIb4LMPF/PIzGfwp+XswCsSabitZgaTzvhPZbENYJomz89/nUUf/QSu2v+/bmohdxZz571WpdgG2LRhM+eceSVbjawmSiaSePUuuJOTk7nrrrtYsmQJH3zwAWPGjKlsS0pK4pNPPuHCCy9slJAiIiLNRTilA7+/+23NjSYs/+gnyFCRViuLlY2bi8nbWPMQ2TdfX0RhKPZ86iJSmT3r2RrbQqEwb7/5KUZSevQO7C7W5G5g6+b8Gpv/u+A98n07wfxZaTEMVwpffvEjfn/N+8E/+tBzFJieBKfacQVhN688/1aNbdu25JO7bgtYdWFSdg1xmWRtsVjweDzY7fZ4dC8iItJkwhEbZduKo7aXbSsmbOrzr1ZWG5uiFNsAoWAIn6/moqPyMVjZsG5T1PZly3LB6ojabjjc1e6u/13AH8Dn88fMINKobA6WL8+N2rw5bythmv9FIL8/SCDKRQOA9evyMKz6PSm7Bq1qJiIisgOslgBp7aMPh8zomI0VXwITtVChIDmd20Vtdie5cbujF8sADiNIj927Rm0ftPceEIy+OJPpK6N7z85R2z2pKbhdzpgZRBpVwMuAgb2jNnfuloOdYAID1Y/bacOTmhK1vWv3HMxQ7AtqIjsLFdwiIiI7wFq0hj3G7AM17HBjtVvpsn8fyNe2mLUyI7ROd9G7b48am0+feCyZ1rKYXaSECpl68Zk1tnlSUxh+4CBMX0n0DsIB2rbJpFuPzjU2j59wHNku3eGWxDEDXgbs2YPMrPQa26ddchbpRB9h01xkWsuYeO5JNbZ17dGJdtkeiIQTnEqkaajgFhER2REhP257OfudcwTu9OTKwynZaQyfchSucAvatqeJpQXzuPuB/7D/gYMrjzmdDiacexInHHMAVm/Nc6srhfz0aOtkxj1XkJGZVnm4e88uzJ1/O9nm1lozZIfWc++cmxiy/16Vx1wuJ2edezLjjh6BtaglrAgtO5NWkc3Mffr2KhejPKkpXHPThQzomY3pj30hqjkwvAX8a8xgzp06HtffRokM2X8vZj58HenBvCZMJ5JYhmnWst/GLmrkyJHkrl5H6bbmP09GRGRnMMDTCYBB1gwAhvgqPp76ta0omrKGVcz3sw0dCIBl4IhER6zK05qAoy0BbwjDYuBwWnD41mOW1VIkSlUWK+WOVhQF7Xi9flJSXGRay7F5t9W5C9OZSgFpFJX5sVmtpLkhNVwAwToO7bc5KXK2o9hn4vX68HiSaeUKYS/SSAVpIjYHJbYsinwGwWAQT7KLTEsxFl/sbe6am5Ark4JIMiWlPtxuJ2n2IEmBbdrfXlq8Meddg2F3s2jRolofq+UBRURE6qNkMw42UznLuBR0BbseImGSfHkkATiAekzrNPzFZFJM5vZvNTs6hT7kJy20irTtGfx//RFpKqEAntBGPAB26vXvojmw+fLJJp9sBxD+64/ILkZDykVERERERETiQAW3iIiIiIiISByo4BYRERERERGJAxXcIiIiIiIiInGgRdNERHZBXbp2ol2Htmxcv4mVK3bdlZgNRxLYnZgBb91XtG7sDNldsLpSCJVsg8INTZPBmQw2R5O+Dw1msWG4UsCMYHpLqM8SdoYzBWx2TH85hLRqmoiINJwKbhGRXUiv3t2ZcfOV2H0R/FtLcGalEEqyccVVt/D7b8uaOl7iOFMIuDuwbfVWijYWkJGTTWb7dBzlayqKzgQItepBIJxE3q9rKc1bTUbnNmR2GkCSuRUKErT3szOVgLs9W5bnUbKliKzOrUnfLRV72ZqWU3gbFoqcu7F+Symfvr6ElJQkDjx4H7LsPtyBum0tFnKmsi2Szlef/8j6dXkMGtyXHt06khnKg1ALXR5aRESaBRXcIiK7iJxOHXjovlv447lPCZT/7+6dPcnJrHumM/HcS1m9KrcJEyaII4lSduPj2xcQ8gcrDzs9bg6aciSuyKq4392MZHWhpBA+n/Uc4cD/9qN1pSYxbMpRJHt8ULI1rhlMZwoloVZ8MuNlwsH/7dXjTk/mwMlH4CxdDuFgjB6ah0J3R66+ciaLv/i+8ti9t8/l4v+cw9iRfUkKxH4fww4PP64NMPnf5xIKVvwsHpvzAu07tuWRx28hO7IWItrLSERE6kdzuEVEdhGXX3YBq95YUqXYBgiW+1n1xhIuv+z8JkqWWEF3ez5/5J0qxTaAv8TLV49/QDhpt7hn8JmpLH7krSrFNoCvuJxvn3ofr6N93DOEXO34bM7bVYptAG9hGUue/TQh70ODudJ4+52vqhTb29116xzyyq1gxP6qk29kMO3cGyqL7e3Wr93EjFvm4HVkNWpkERHZtajgFhHZRXTv3InSLUU1tpVtLaZbp5wEJ2oafp+Jr6isxraiDdsIhB3xDWCz4Sssw19a89D1gtzNhPyhGtsajcVKeZGfYHnNd/K3rdhIEHd8MzSCwkgyTz/xWtT2115ZhCU5I3oHNgfLl6/F56v5ffj0g8UUh+L8/4OIiOzUVHCLiOwqIrEXkTLDkQQFaUKGhbA/9jDpSCjO74PNRcAbe8j6P+86NzrDQsAbe25yJNT8h1FHDAtFhSVR27duLcCMdYfbYqWosDRqs2maBINxvvghIiI7NRXcIiK7CH8khMVmrbHNYrUQZBcouM0IrjQ3hmHU2Gxz2nG4an6PGo2vFE/rdKg5AnaXA0eSM74ZwkE8rdOiNjuSXdgdzf8rQpIRYJ/9BkRtH3XIvuCPXlAT9LF7765Rm9u0zSbZpeVuRESk/pr/p6mIiDSKp555mQ779a6xrcN+vXn62VcTnKhp2EOFdDugb41texy+N/ZgfBcrA7DYDDrus3uNbT3H7I3LHv+7qnazhJx9etXY1m/sEOyBzXHP0FCuQD5Tpp2OzV69KO6Q047+/brGXnXeNGmVYnDQyH1rbL7kynPIMGqehiEiIlIXKrhFRHYRLz7/On+WbqbbEYNxZ6QA4E5Podvhe7Pcu41nn9k1Cm5L2SZ2H96NgccPw5WaBEBSVir7nD6SnN6ZUF63raQawr3tJ/Y4Yh96jx2K01ORIblVGnudPor2e3bBsvnXuGewlm6g3+g+9B83FGdKxXztlNbp7DvxUHbr7AZvYdwzNFgkRHtXGfNfupe9h+4JgMPp4NgTD+eRx6eTGdpUaxcp/jyuvuZszr/wdDypFf8uuvboxKxHb2JIn9bgiz5kXUREpDaGaZqxJ/XtokaOHEnu6nWUbovz0EIRkQQbOKg/55x9Kq2zs9iyNZ85j87nu29+aupYDPB0AmCQtWKRqyG+io+nfm0r7jhnDbP/f3t3Hl5Febh9/D5r9p2ELYAsAoGwCCkBlaVBiyCCaKuAbKKIK2htNfZV0P4oVX/WF0FZBUUUXOpSF6AgUFCkVkFQgbqAhJCwmX07Wc6Z9w9eojELoEwmyfl+rsvLnueZGe4T6MH7zMwzkiRn/4skSfaLUn7ZLxgUrnJXnAybQ3ajXK6ykzLqu1y1SFBxqVuGJJtNCnUWyXfi23qNYAuKVLm7mXxyyG6UyVV6QkZpzYvKNVhOt4qcUSr2uWW3SeH2Yrk8OZJx9rdJGEFRyvGFyGvY5LZVKNLIk1FWbGJoAEBjNfy2h2RzBWnTpk1n3JYbkwDAz3y283PdttP6gm25kny5SvIrX1ry7fOx/Qr+0Usr7qI3SnLl/NHZ7Eb5LXxFmUIqjivkFxzCVpKjaOVUvm6UPwcAQIPDJeUAAAAAAJiAwg0AAAAAgAko3AAAAAAAmIDCDQAAAACACVg0DQCAn8kWECqvK1ySZC/NkcrreOZzjQewywhpJkMu2Q2PVJQtlusC0JQYwVEq8bnksvnkKs2VfBVWRwLqFYUbAIBz5XCqPKyjTh78Xof+86VsDrs6XtJV0S1by5l/8KweR+ULjlOJEalvt+xVwYk8RbeNVYdLuiqgLFM2T149vAkAMI/PHaKTaqZ/vLZJ//n3HjVvEatJU0arTbRTwaUnrY4H1BsKNwAA56g8vJM+XLZJ+UezK8dO/DddzbvEK+l3v5Izr+5naRtBUTpxVPr4hdcrx7IOHtWBD/dq0B1XKtJdzjOgATRezgCllYRpytg7VVz0w5U/G977l/7wp+kanZKgwLLsOg4ANB3cww0AwLkIitCRz9OrlO3Tjn91RLnHiyVXYJ2HKHc316drtlYb91V49clL/1J5YMvzFhcA6luBI0p/fnB+lbJ92pOPLlOuEWpBKsAaFG4AAM5BhSNS3+34qtb5A9v/KyMgsvYD2Owqzi2Rt7zm+xiLvs9XeQV/PQNovArLHfpyz39rnPP5fPry86/P+MUk0FTwNzoAAOfCJhlG7QubnZqz1bG/rc79fzgGADROZ/oM83p9qvNzEmhCKNwAAJwDpzdf7ZI61Trfvn9n2cvqWPTM51VIdLDsTkeN00GRoXK5f2lKALBOaIChC7t2qHHOZrOpR6/O5/5UB6CRonADAHAuinPULqmjgmPCq01Fto1VTHzEGRc8c5aeVK8xA6qN22w2JY0bJJfn2HmLCwD1Ldybo9lzZsrldlWbm3rr9YpyeixIBViDVcoBADhHroJvNeSOK5T+WZrSPv1GdodDHS7pqpZdWsiZ980Z97eXZCm+UytFzByl/Rt3q+j7fEW1iVXXy3op0PheKimsh3cBACYp96hTlFuvvv2MXlj+hnZ9+qXiWjTT1Gm/U9cLohTEl4rwIxRuAADOVUWZXDn71TEhUm0TB8gmQ86KXBk5NS8SVBN7YaYiHC71uzpBPsMph1EmW8m3ks9rYnAAqB+O0nzF2wr1x9uHq7jiKrkchoIr8iTKNvwMhRsAgJ/JKMmVU7mn/vfPOYC3XPaCTO7vAtA0GT65SrIUIUnlVocBrMHf8QAAAAAAmIDCDQAAAACACSjcAAAAAACYgMINAAAAAIAJGtSiaevWrdPbb7+tvXv3Kj8/X+3atdPEiRN17bXXymaz1bpfSkqKMjIyqo1//vnnCggIMDMyAD/idDo1bESKLh82RHa7Tdv+tUPvvLVepaVl9ZbB5XJp5FWXa+SVl8lms2n9hi166411Kivzs9Vo7E4ZIXGqcEZIMuSqyJWKsnQuS5fZAkJU7o6VYXfJ7iuVw3NCKj+HZ8Pa7CoPjFaBL1A2SeH2YjlKcs4pA84Tp1vlYW1VoUDJMBSofNly061OVf/sTnkColXsc8smnyLtxbKV5FqdCgD8WoMq3M8//7xat26t1NRURUVF6aOPPtJDDz2kY8eO6c4776xz32HDhmnq1KlVxtxut5lxAfiR5i1iNe+Zudryz+1a8Niz8nq9GjR0gFa9slgP/OHPOvDtIdMztI5vqeXL/qaC/Zk6uf07yWfot0kDdfON4zVt+h+Vdsg/CoYtLEblPUbrv9sO6MiuDyS7Te1+1VmdB3eTq+CAVFF6xmN4w9rp+6Ol2rd+u4q+z1N4yxgljvyVIiMqZC/MPHMIV5COKU7LF76qjes+kNPp1KhrL9f4CVeqWXmmVFF/X8L4O19onErscdr79sc6uT9dDrdLbZK7qOOlvRSY99+z+vPQFHjdYcooDdPTf3lBOz7cqdDwEI2dMEqjRg1SlCedx80BgEVshmE0mK/is7OzFR0dXWXsoYce0tq1a/XJJ5/Ibq/5CviUlBQNGTJEs2bNOm9Zhg4dqsOHjqgwy3Hejgmg8Vq5+hn9bc4iHc04XmU8MipCc/7vAxp37TT5fD5TM7z99kodW7tHJbmFVcYDwoLVbsyvNOLKCWpAH+nnrHdYO0lSX0eUJCnZc+q99GjxvSQp5lKXZLNL9zylzYveV2lBSZX9g6PCNOSu4XLl7K/z1/EFx+nAF3nat/bTanNJ44aodRufVJJTxxFsOhHQVuOvvUd5uflVZlq3aaHlK/+iGE9anRlwnjjcKgrprC3/+5q8ZRVVpsJbxaj/tGEKyv7ConD1yOHSobIY3fDbu1X2kytuevXprieemKlIzxGLwgFA0zP8todkcwVp06ZNZ9y2Qd3D/dOyLUkJCQkqLCxUcXGxBYkAQOrdp4cOfJ1WrWxLUm5Onj7Y9G9dNmywqRmSB/SV73hhtbItSaUFxSo5lKUhKZeYmqEhsCck6dCuw9XKtiQV5xQoc1+GFBhe5zEqXNHav35XjXN73tqhcndcnft7g6L08uq11cq2JGWkH9OOf38hW2BYncfA+VEe3lb73vtPtbItSfmZWco5kiUFRViQrH6VuKL1fx9fUa1sS9KeXXuVlpkjOVwWJAMANKjCXZOdO3eqefPmCg0NrXO7d955R4mJibrooos0bdo0ffXVV/WUEEBTl9Svt/6zfWet8//+8FP9qt9Fpma49JJ+KjhQvfCflv/tUQ26NNnUDA2Br30PHfnycK3z6Z8dlM9ZR9m12VSS75FRy9UI5SWlKvPUfeltodetzRs+qnV+3btb5bEF1XkMnB/lRqBO7K/9z8PR3QdlD2tWj4msUWwEaMeHtX9Gbdq4Q7YgvgQCACs0qHu4f+rTTz/V2rVrdf/999e5XUpKinr27KlWrVopPT1dixcv1vjx4/XWW2+pTZs29ZQWQFNV6ilVUHDtBSo4JEilpebeJ1pSUiJns9rPUDkD3CouyTM1Q4NQUSpnQO3rczgDTi0WVSvDkN1V961Cdkfd30Xbbad+z2sTEhosu63xXtrfqBg+OQNcqiitedFAZ4BLNj9YxM4mQ4FBgSoprn7lhySFh4dKJt/yAgCoWYM9w33s2DHdc889Sk5O1qRJk+rc9sEHH9SoUaOUlJSkMWPGaNWqVZKk5cuX10dUAE3chvVbdNmIQbXOX3FVit57Z6OpGd5+e4MiusfXOh/Zo43eeGOtqRkaAvuebbrw4gtrnb9wUDfZPNl1HiMw2CF3SGCNc6FxkXI5q1+e/GPhtiKNnzSq1vlxE0bKVeYHX340AEG2ArXp37XW+XYDEuTNavr3Lofbi3TNdVfUOv+bYZfI8Icv5ACgAWqQhTs/P1/Tpk1TZGSkFixYUOtiabWJi4tT3759tXfvXpMSAvAnx4+dVHZ2jlKuGFhtLql/b4VHhWn/3q9NzXAkPVMHjmcqtnvbanMxnVsrsyi3XlZKt5rv2GHFxjnUomv1Lx/a9O2kyGbuM65K7fJkasBNv5HdWfVMtzPQrf5ThspZcrTO/Q1PgS4dkKh+A3pXmxt17W/UMT6SVcrriS03XR0u6a6I+OqXjbcf0lOBQZK8Tf/3wlmSo4mTR6l9p3bV5u6690Y1CyqXGvGCigDQmDWoVcolyePx6MYbb9TRo0f1yiuvqHnz5j/rOLfccotycnL02muv/az9WaUcwI85HA498NDdat++rbZv/UQ+r1f9ByYpNy9PD/+fx+TxmP/oIafTqblzH1DPLp1VfOCEDMNQSMc47f/ukO6//38a/bO4z2qVcknOi38lb48RKrLH6NAn38put+uCfp0UHGLImZ+ms3kOthEYobKAVsr4PE25GTmKaR+rlgmt5S5Jl0qrL0xXjc2uvICW+u5Itv7x1hYFuF26+pqhat0sSGGeugs7zjOnWyVhXZSbmaOjuw/IEeBSu/5dFRgkBeaZ+0VYg+JwKdvVQvu/Stc/121XdHSERl8zVM1DfAouPWl1OgBoUs5llfIGVbgrKip055136rPPPtNLL72kTp06/azjHD9+XCNGjNDo0aN/9qPCKNwAahIREa7+lyTJ4bDrP//+TN+fzKr3DJGREep/SZJsNuk/O3YpK6uuR1g1HmdduPufWqDOnjRM9uBISYZ8RbmSr+5LwWtiC46QzRkgo6xEhqfg3EM73bIHRfyiDDhPgsLlCIuVDJ+8Wen++3vhCpQ9KFzyeeUrypEM7t0GgPPtXAp3g1o07ZFHHtGWLVuUmpqqwsJC7d69u3KuW7ducrvdmjx5sjIzM7Vx46n7Jd99911t2bJFgwcPVlxcnNLT07V06VI5HA7deOONFr0TAE1VXl6+/rl2s6UZcnPztP69M3/AN3necvkKftmZO6M475ctqVVR9osz4DwpyZe3pPqj2vxOuUe+co/VKQAA/1+DKtzbt2+XJD366KPV5jZt2qT4+Hj5fD55vT88siU+Pl4nTpzQ3LlzVVBQoLCwMPXv318zZsxghXIAAAAAgGUaVOHevPnMZ41Or0B+Wu/evauNAQAAAABgtQa5SjkAAAAAAI0dhRsAAAAAABM0qEvKAaAha9EyTrfdNlkX9ewuGdLXB77TgqdXKO1QutXR/I497gKVxySqolySbHK5fHIWHZZ+zkrjAAAAJqFwA8BZ6Nm7u558dJYyt3yp717+SJIU0ypGy595XHOeeFr/2rLd4oR+5MKLlR/VUzuXblB+5qnHskW1jVOfcb9WaFiWVHDc4oAAAACncEk5AJyB3W7XE489pK/WbFNO2g9lLj8zS/tXb9VDD8xUUFCghQn9SHCoSlslaev8tyrLtiTlHD6hrfPeUKmrhYXhAAAAqqJwA8AZDP71xSr89rgqSsurzfm8PmV99p3GXDPCgmT+xzFiir56/zP5KrzV5ipKy3Vw+z4pqq0FyQAAAKqjcAPAGSR276qSzJxa5wuOfK+eiV3rMZH/8ja/QNmHar9kPOvAUXltQfWYCAAAoHYUbgA4g5PfZ8kdXnuJCwgP0cms7HpM5L/sJQUKjAipdT4wMlh2m68eEwEAANSOwg0AZ/DuOxsUkdim1vmYPu21Zs1b9RfIn218SV2GXlTrdOeUi2TLO1yPgQAAAGpH4QaAMygsKNJbazeq/bA+stltP0zYpPhLuuk/e79UZsYx6wL6EV/6twoPM9TlN32lH/9W2GzqcfUlCg6RVFFqWT4AAIAf47FgAHAWFj7znL6/LktTp1yv8uxiGYZPgbHh+vuba7V0ySqr4/kVx0fPqVPf3+qC5AnK+u6obHa7Yi5oLqdRIEfOV1bHAwAAqEThBoCz9Oqrb+vVV99W6/iWsjvsOnI4U4ZhWB3LL9l3/l2BF6WodauIUwM5e6wNBAAAUAMKNwCco4wjR62OgNNK8qxOAAAAUCvu4QYAAAAAwAQUbgAAAAAATEDhBgAAAADABBRuAGfNZrPJbudj43xwOllCAzj/bGfeBACAesR/8QE4o6R+vfSnB2eoVYtYSVJ+YbEWPrNSb72+zuJkjUtwSLD++MfbdUlyX/nKKmR3O7Vz9xd69NGnlZeXb3W8xqVFZ5VHdpWnsEyy2RQY7JSr9JjkYRE1f2QEx6rcHSNPfqnsTocCgmxylRyVygqtjgYA8HMUbgB1Gjb815o7514FefJkeHIlSWEum2Y/cKu6JnTSo3MWWBuwkQgKDtLLaxYp/5ND2r9yS+V4q3bN9fLLizVu3G3KzaUsng2jfT8dL2+tXY+9KW9ZhSTJFejWryb8Ws1i3LIVn7Q4IeqTN6ytDn35vfat3SpfhVeSFBAWpAFTL1d4oEM2voQBAFiIa0MB1MrhcGjWw3crsChHhtf7w4RhyF2cp2tHX67W8S2tC9iI3HnnjSr6LF1Z32RUGc9NO64T/9qv++6/w6JkjUxImIoju+qTlz+sLNuSVO4p00fL/6kSW7Rk4682f2FzByv7RIW+fPvjyrItSaUFJdr2zHsqC2hlYToAACjcAOow6NcXK8xhSDJqnA+3leumaePqN1QjlTLoYp3Yf7jGudy040rq3aOeEzVOtj4p2r9lX82ThvTN1r0ygpvVbyhYpiIgVnvX7axxzlfhVcaeNNmCI+s3FAAAP0LhBlCrtm1bKdBR+yJE3tJStWsXX4+JGi9bzd9ZVPKVVcjhcNRPmEbMiIxT4cna73cvOJYrw8bdUv7CZ3er8GRurfM5GdmSw11/gQAA+AkKN4BaHfg2TSXe2puizRWgb775rh4TNV4+m+pcQNnudsr748v2USN7VqYiWkbVOh8ZHy27UVaPiWAlu69U4S2ia52PaddMqvDUYyIAAKqicAOo1fYPPlZ+uSRbzU2x0O7W8mVr6jdUI7Xun5vVokf7GudiLmyt7f/+pJ4TNU6+XVvUdUhCjV9e2Ox2dRrYTSrOqv9gsITDc0KJI5NqnHMGuNSqexsZJTwBAABgHQo3gFoZhqHU++aqJCRadtcPl2XaHA6VhURpxfOv6+SJ7y1M2HgsWbJK9s7N1KJn+yplMa5bW4X/6gL97xOLrQvXmHiKFXj8M108JUUBoUGVw4ERIRp42wgFVJyQjDNcv4+mo9yjyHCv+o4dJGfgD59RIc3CNfiuq+T2HLEwHAAAPBYMwBls/+A/Gj9+pv7Pg3epY8e2skk6mZWrJ5+Yq23/2mF1vEajrKxcN4y/XbdMn6gRN6TI5jMku02btn2kZ8b+RSXFJVZHbDRs6XvUrFmOht49XGUeQ7JJ7gCbXJ6jUgnPXfY39qJMxbeNUvM/jFKZp0I2u11ul08uzxEZZfz/CgBgLQo3gDP6+r/favKEmVbHaPTKysr19IIVenrBCqujNHrG94flyvtGrtMD3Kbr30py5CrJ+eHPQ0ltz1YAAKB+cUk5AAAAAAAmoHADAAAAAGACCjcAAAAAACagcAMAAAAAYAIKNwAA/s4VKFtQuORwnXnbppwBAIDzjFXKAQDwU4Y7VN/borV370GlHz6q7okXqn3bFoqqOC5VlNVPhoAwlQe1Vk56toqyChTVrqXCogLkLEiTvPWTAQAAs1C4AQDwQ4Y7RN/kBeiWyTNUVFhcOd66TUstfX6O4owMyVthbgh3qAq9cdr2+FuqKC2vHA6Ni9TA6cPkzv9K8nnNzQAAgIm4pBwAAD+UrWjdcfOsKmVbkjLSj+qRh55WsSvG9AzlQa31weJ1Vcq2JBWeyNVnb/5bvuAWpmcAAMBMFG4AAPyNza6jJ3KVm5NX4/R/PvpMuWUmXwRnd6owp1jlJaU1Th/bm6Zye6i5GQAAMBmFGwAAf2N3KDe3oM5NysrK65w/HxlKCz21zxuSr8JnbgYAAExG4QYAwN94y3VB+1a1ToeFhyo0OMDcDBVlimgZXeu0OyRQThYsBwA0chRuAAD8UGSAV5cPH1jj3B33TFKMo+4z4L+cIbe9RC17XFDjbI+r+slddtLkDAAAmIvCDQCAHwotPaH775+iaXeMV0hosCSpeYtY/c/jf9CwQYmyleSansFRmK4+Y36lLpddJGfAqdPZwVFhSp40VC3bh8iohwwAAJiJx4IBAOCPDEMRxWm6+bf9dO2YISr3+hTgtCnKli95jtVbBmfOV+raN1od+4+Szyc5bF65yk7IKDxRPxkAADARhRsAAD9mL8lWjLJPvbDkkdeGVJwll7J+PAIAQJPAJeUAAAAAAJiAwg0AAAAAgAko3AAAAAAAmIDCDQAAAACACSjcAAAAAACYgMINAAAAAIAJKNwAAAAAAJiAwg0AAAAAgAko3AAAAAAAmIDCDQAAAACACSjcAAAAAACYgMINAAAAAIAJKNwAAAAAAJiAwg0AAAAAgAko3AAAAAAAmIDCDQAAAACACSjcAAAAAACYgMINAAAAAIAJKNwAAAAAAJjAaXWAH1u3bp3efvtt7d27V/n5+WrXrp0mTpyoa6+9Vjabrdb9DMPQsmXLtHr1amVnZyshIUEPPPCAevfuXX/hAeAsxDVvpimTr1f79m2VfiRTK1e+qowjR62OBQAAABM0qDPczz//vIKCgpSamqpFixZp0KBBeuihh/TMM8/Uud+yZcs0f/58TZkyRUuWLFFsbKymTp2q9PT0ekoOAGc2ffpEvbjsKfV0xUqfZijBG6EVCx7TfffdbnU0AAAAmKBBneFetGiRoqOjK18PGDBAubm5eu6553T77bfLbq/+/UBpaamWLFmiqVOnasqUKZKkvn376oorrtDy5cv18MMP11N6AKjdoMH9NXrIEO1fvbVyLLvomLIPHdOlv7lIX119hf7x1noLEwIAAOB8a1BnuH9ctk9LSEhQYWGhiouLa9xn165dKiws1PDhwyvH3G63Lr/8cm3bts20rABwLu647UYd2rC7xrm0zZ/rphvH1W8gAAAAmK5BFe6a7Ny5U82bN1doaGiN8wcPHpQkdejQocp4x44dlZmZKY/HY3pGADiT8OBglZeU1jjnq/AqwOao8SoeAAAANF4N+r/uPv30U61du1ZTp06tdZv8/Hy53W4FBARUGQ8PD5dhGMrLyzM7JgCcke0MZdrmsMswjHpKAwAAgPrQYAv3sWPHdM899yg5OVmTJk2yOg4A/CKHM48qODqsxjl3SKC+z82lcAMAADQxDbJw5+fna9q0aYqMjNSCBQvqvMwyPDxcZWVlKi2teqlmfn6+bDabIiIizI4LAGf0xN8Wqf2VSXK4HFXG7Q67Oo1K1v8+udiiZAAAADBLg1qlXJI8Ho+mT5+ugoICvfLKKwoLq/mM0Gmn793+7rvv1LVr18rxgwcPqlWrVgoMDDQ1LwCcja+/OqCH/vI3PTLrDyr67oTKswrligpRSMfmeuxvC/XZzs+tjggAAIDzrEEV7oqKCt199906ePCgXnrpJTVv3vyM+/Tp00ehoaFat25dZeEuLy/Xhg0bNGjQILMjA8BZ2/HRpxo2fJwuHthP7drFK+PIUX2w9d/y+XxWRwMAAIAJGlThfuSRR7RlyxalpqaqsLBQu3fvrpzr1q2b3G63Jk+erMzMTG3cuFGSFBAQoOnTp2vBggWKjo5W586dtWbNGuXm5uqmm26y6J0AQM0Mw9D2bR9ruz62OgoAAABM1qAK9/bt2yVJjz76aLW5TZs2KT4+Xj6fT16vt8rctGnTZBiGVqxYoezsbCUkJGj58uVq06ZNveQGAAAAAOCnGlTh3rx58xm3WbVqVbUxm82m6dOna/r06WbEAgAAAADgnDXIVcoBAAAAAGjsKNwAAAAAAJiAwg0AAAAAgAko3AAAAAAAmIDCDQAAAACACSjcAAAAAACYgMINAAAAAIAJKNwAAAAAAJiAwg0AAAAAgAko3AAAAAAAmIDCDQAAAACACSjcAAAAAACYgMINAAAAAIAJKNwAAAAAAJiAwg0AAAAAgAko3AAAAAAAmIDCDQAAAACACSjcAAAAAACYgMINAAAAAIAJKNwAAAAAAJiAwg0AAAAAgAko3AAAAAAAmIDCDQAAAACACSjcAAAAAACYgMINAAAAAIAJKNwAAAAAAJiAwg0AAAAAgAko3AAAAAAAmIDCDQAAAACACSjcAAAAAACYgMINAAAAAIAJKNwAAAAAAJiAwg0AAAAAgAko3AAAAAAAmIDCDQAAAACACSjcAAAAAACYgMINAAAAAIAJKNwAAAAAAJiAwg0AAAAAgAko3AAAAAAAmIDCDQAAAACACSjcAAAAAACYgMINAAAAAIAJKNwAAAAAAJiAwg0AAAAAgAko3AAAAAAAmIDCDQAAAACACSjcAAAAAACYgMINAAAAAIAJKNwAAAAAAJiAwg0AAAAAgAko3AAAAAAAmIDCDQAAAACACSjcAAAAAACYgMINAAAAAIAJKNwAAAAAAJiAwg0AAAAAgAko3AAAAAAAmIDCDQAAAACACSjcAAAAAACYgMINAAAAAIAJnFYH+Km0tDQtX75ce/bs0TfffKMOHTro3XffPeN+KSkpysjIqDb++eefKyAgwIyoAAAAAADUqsEV7m+++UZbt25Vr1695PP5ZBjGWe87bNgwTZ06tcqY2+0+3xEBAAAAADijBle4U1JSdNlll0mSUlNT9eWXX571vs2aNVPv3r1NSgYAAAAAwNlrcPdw2+0NLhIAAAAAAOesSbXbd955R4mJibrooos0bdo0ffXVV1ZHAgAAAAD4qQZ3SfnPlZKSop49e6pVq1ZKT0/X4sWLNX78eL311ltq06aN1fEAAAAAAH6myZzhfvDBBzVq1CglJSVpzJgxWrVqlSRp+fLlFicDAAAAAPijJlO4fyouLk59+/bV3r17rY4CAAAAAPBDTbZwAwAAAABgpSZbuI8fP66dO3eqR48eVkcBAAAAAPihBrdoWklJibZu3SpJysjIUGFhodavXy9J6tevn6KjozV58mRlZmZq48aNkqR3331XW7Zs0eDBgxUXF6f09HQtXbpUDodDN954o2XvBQAAAADgvxpc4c7KytLMmTOrjJ1+/cILLyg5OVk+n09er7dyPj4+XidOnNDcuXNVUFCgsLAw9e/fXzNmzGCFcgAAAACAJRpc4Y6Pjz/j87NPr0B+Wu/evauNAQAAAABgpSZ7DzcAAAAAAFaicAMAAAAAYAIKNwAAAAAAJqBwAwAAAABgAgo3AAAAAAAmoHADAAAAAGACCjcAAAAAACagcAMAAAAAYAIKNwAAAAAAJqBwAwAAAABgAgo3AAAAAAAmoHADAAAAAGACCjcAAAAAACagcAMAAAAAYAIKNwAAAAAAJqBwAwAAAABgAgo3AAAAAAAmoHADAAAAAGACCjcAAAAAACagcAMAAAAAYAIKNwAAAAAAJqBwAwAAAABgAgo3AAAAAAAmoHADAAAAAGACCjcAAAAAACagcAMAAAAAYAIKNwAAAAAAJqBwAwAAAABgAgo3AAAAAAAmoHADAAAAAGACCjcAAAAAACagcAMAAAAAYAIKNwAAAAAAJqBwAwAAAABgAgo3AAAAAAAmoHADAAAAAGACCjcAAAAAACagcAMAAAAAYAIKNwAAAAAAJqBwAwAAAABgAgo3AAAAAAAmoHADAAAAAGACCjcAAAAAACagcAMAAAAAYAIKNwAAAAAAJqBwAwAAAABgAgo3AAAAAAAmoHADAAAAAGACCjcAAAAAACagcAMAAAAAYAIKNwAAAAAAJqBwAwAAAABgAgo3AAAAAAAmoHADAAAAAGACCjcAAAAAACagcAMAAAAAYAIKNwAAAAAAJqBwAwAAAABgAgo3AAAAAAAmoHADAAAAAGACCjcAAAAAACZocIU7LS1Ns2bN0ujRo9WtWzeNHDnyrPYzDENLly7VkCFD1LNnT11//fXavXu3uWEBAAAAAKhFgyvc33zzjbZu3ap27dqpY8eOZ73fsmXLNH/+fE2ZMkVLlixRbGyspk6dqvT0dBPTAgAAAABQswZXuFNSUrR161bNnz9f3bt3P6t9SktLtWTJEk2dOlVTpkzRgAED9OSTTyoyMlLLly83OTEAAAAAANU1uMJtt597pF27dqmwsFDDhw+vHHO73br88su1bdu28xkPAAAAAICz4rQ6wPlw8OBBSVKHDh2qjHfs2FErV66Ux+NRYGDgOR3zxIkTsjmk0BjvecsJAKjdYfthSdJx2xFJ0ibXqXG359TnsOND26mBT46d+nfAlnrNBwAAIEnHvs+Rw1FwVts2icKdn58vt9utgICAKuPh4eEyDEN5eXnnXLgDAgJks9kUGxt7PqMCAAAAABoxp7NQbrf77LY1OUuj9emnn1odAQAAAADQiDW4e7h/jvDwcJWVlam0tLTKeH5+vmw2myIiIixKBgAAAADwV02icJ++d/u7776rMn7w4EG1atXqnC8nBwAAAADgl2oShbtPnz4KDQ3VunXrKsfKy8u1YcMGDRo0yMJkAAAAAAB/1eDu4S4pKdHWrVslSRkZGSosLNT69eslSf369VN0dLQmT56szMxMbdy4UdKpBc6mT5+uBQsWKDo6Wp07d9aaNWuUm5urm266ybL3AgAAAADwXw2ucGdlZWnmzJlVxk6/fuGFF5ScnCyfzyevt+rjuqZNmybDMLRixQplZ2crISFBy5cvV5s2beotOwAAAAAAp9kMwzCsDgEAAAAAQFPTJO7hBgAAAACgoaFwAwAAAABgAgo3AAAAAAAmoHADAAAAAGACCjcAAAAAACagcAMAAAAAYAIKN/xGUVGRBg0apC5duuiLL76wOg780BtvvKEuXbpU++eJJ56wOhr83Jtvvqmrr75aPXr0UHJysm6++WZ5PB6rY8EPTZw4scbPyS5duui9996zOh780KZNm/S73/1OF110kS699FLNnDlT6enpVsdCI+K0OgBQXxYuXCiv12t1DEDPPvuswsLCKl83b97cwjTwd4sWLdKyZct06623qnfv3srJydGOHTv4vIQlZs+ercLCwipjK1eu1IYNGzRgwACLUsFfffzxx7rzzjt19dVX65577lFubq6eeuopTZ06Ve+8844CAwOtjohGgMINv3DgwAGtXr1a999/v2bPnm11HPi57t27Kzo62uoYgA4ePKinn35aCxcu1ODBgyvHhw0bZmEq+LNOnTpVG7v33nt1ySWX8LmJevfee++pVatWmjt3rmw2myQpOjpakydP1pdffqmkpCSLE6Ix4JJy+IU5c+Zo7Nixat++vdVRAKDBeOONNxQfH1+lbAMNya5du3TkyBFdddVVVkeBH6qoqFBISEhl2ZZUeYWaYRhWxUIjQ+FGk7d+/Xp9/fXXuuOOO6yOAkiSRo4cqYSEBA0dOlRLlizh0l1YZs+ePercubMWLlyoAQMGKDExUWPHjtWePXusjgZIkt59910FBwdr6NChVkeBH7rmmmt04MABvfTSSyooKFB6erqefPJJdevWTX369LE6HhoJLilHk1ZSUqJHH31U99xzj0JDQ62OAz8XGxuru+66S7169ZLNZtPmzZs1b948HT9+XLNmzbI6HvzQyZMn9eWXX+rrr7/W7NmzFRQUpMWLF2vq1KnasGGDYmJirI4IP1ZRUaF169YpJSVFwcHBVseBH0pKStLTTz+te++9V3/+858lSQkJCXr22WflcDgsTofGgsKNJm3RokWKiYnRtddea3UUQAMHDtTAgQMrX1966aUKCAjQypUrdeuttyouLs7CdPBHhmGouLhYTz31lLp27SpJ6tWrl1JSUvTiiy9q5syZFieEP9u+fbuys7M1cuRIq6PAT+3atUv33XefrrvuOg0ZMkS5ublauHChbrnlFq1evZpF03BWuKQcTVZGRoZWrFihGTNmqKCgQPn5+SouLpYkFRcXq6ioyOKEgDR8+HB5vV7t37/f6ijwQ+Hh4YqMjKws25IUGRmpbt266dtvv7UwGXDqcvLIyEhdeumlVkeBn5ozZ4769++v1NRU9e/fX1dccYWWLl2qffv26R//+IfV8dBIcIYbTdaRI0dUXl6uW265pdrcpEmT1KtXL7366qsWJAOAhqFTp046fPhwjXOlpaX1nAb4gcfj0fvvv69Ro0bJ5XJZHQd+6sCBA9XWD2jRooWioqJq/ewEforCjSYrISFBL7zwQpWx/fv3669//aseeeQR9ejRw6JkwA/Wrl0rh8Ohbt26WR0FfujXv/613njjDe3fv18JCQmSpJycHO3du1dTpkyxNhz82ubNm1VcXMzq5LBUq1attG/fvipjGRkZysnJUevWrS1KhcaGwo0mKzw8XMnJyTXOde/eXd27d6/nRPB3N910k5KTk9WlSxdJ0qZNm/Tqq69q0qRJio2NtTgd/NFll12mHj16aMaMGbrnnnsUEBCgpUuXyu12a/z48VbHgx9755131KpVK/Xt29fqKPBjY8eO1dy5czVnzhylpKQoNze3cn2g4cOHWx0PjQSFGwDqSfv27fX666/r2LFj8vl8uuCCC/SnP/1JEydOtDoa/JTdbtfSpUv117/+VbNmzVJ5ebmSkpL00ksv8SUQLJOXl6cPPvhAkydPrvL8Y6C+TZo0SW63W2vWrNHrr7+ukJAQ9e7dW/PmzVNUVJTV8dBI2Aye2g4AAAAAwHnHKuUAAAAAAJiAwg0AAAAAgAko3AAAAAAAmIDCDQAAAACACSjcAAAAAACYgMINAAAAAIAJKNwAAAAAAJiAwg0AAAAAgAko3AAA+KmJEydq4sSJpu9jhQULFqhLly7Kzs62OgoAwI9RuAEAQKO1ePFivf/++1bHAACgRhRuAADQaC1ZsoTCDQBosCjcAAAAAACYgMINAICJCgsL9Ze//EUpKSlKTEzUgAEDdOONN2rv3r2V2+zZs0c33XST+vbtq169emnChAnauXNnleOcvif5wIEDmjlzpvr06aPk5GTNmTNHpaWlVbZ9/fXXNWnSJA0YMECJiYkaMWKEVq9ebdp7LCsr0/z583X55ZcrMTFRgwcP1uOPP66ysrIq23Xp0kV//vOf9f7772vkyJFKTEzUlVdeqW3btlU75scff6xrrrlGPXr00GWXXaaXX3658mfw4+MVFxfrzTffVJcuXdSlSxelpqZWOU5BQYFSU1OVlJSkvn376oEHHlBJSYk5PwgAAH7CaXUAAACastmzZ+uf//ynJkyYoI4dOyo3N1c7d+7UgQMH1L17d+3YsUPTpk1TYmKi7rzzTtlsNr3xxhuaPHmyVq9erZ49e1Y53t13363WrVvr3nvv1e7du7Vq1Srl5+fr8ccfr9xmzZo1uvDCC5WSkiKn06ktW7bokUcekWEYuuGGG87r+/P5fLrtttu0c+dOXXfdderYsaO+/vprrVy5UocOHdLChQurbL9z505t2LBB48ePV0hIiFatWqUZM2Zoy5YtioqKkiTt27dPN998s2JjY3XXXXfJ5/PpmWeeUXR0dJVjPf7443rwwQfVs2dPXXfddZKktm3bVvt5xcfH6/e//7327dun1157TdHR0frjH/94Xn8OAADUhMINAICJtm7dquuuu67Kmddp06ZJkgzD0MMPP6zk5GQ9++yzstlskqSxY8fqyiuv1Lx587RixYoqx4uPj9eiRYskSTfccINCQ0O1evVqTZ06VV27dpUkvfjiiwoMDKzcZ8KECbrpppv03HPPnffC/c477+ijjz7SqlWrlJSUVDl+4YUXavbs2dq1a5f69OlTOX7gwAGtXbu2shgnJydr9OjReu+99zRhwgRJ0vz58+VwOLRmzRo1b95ckjR8+HCNGDGiyq89evRoPfzww2rTpo1Gjx5dY76EhATNnTu38nVubq7+/ve/U7gBAPWCS8oBADBReHi49uzZo+PHj1eb279/vw4dOqSrrrpKOTk5ys7OVnZ2toqLizVgwAB98skn8vl8Vfb5aWE+XVJ/fFn2j8t2QUGBsrOz1a9fP6Wnp6ugoOB8vj2tX79eHTt2VIcOHSrzZ2dnq3///pJOXRr+YxdffHGVs9Bdu3ZVaGio0tPTJUler1c7duzQ0KFDK8u2JLVr104DBw4853xjx46t8jopKUm5ubkqLCw852MBAHCuOMMNAICJ/vCHPyg1NVVDhgxR9+7dNXjwYF199dVq06aNDh06JEm6//77a92/oKBAERERla/btWtXZb5t27ay2+06cuRI5djOnTu1YMEC7d69u9r9ygUFBQoLCzsP7+yUtLQ0HThwQAMGDKhxPisrq8rrli1bVtsmIiJC+fn5ldt7PJ5q71Oq/t7PRqtWraq8Dg8PlyTl5eUpNDT0nI8HAMC5oHADAGCiESNGKCkpSRs3btT27du1fPlyLVu2TAsWLJBhGJKk++67TwkJCTXuHxwcXOfxT1+Gftrhw4c1ZcoUdejQQampqWrZsqVcLpe2bt2q559/vtoZ81/K5/Opc+fOeuCBB2qcb9GiRZXXDoejxu1O/yzON7u95ov5zPr1AAD4MQo3AAAmi4uL0w033KAbbrhBWVlZGjNmjBYvXlxZUkNDQ3XxxRef1bHS0tLUpk2bKq99Pp/i4+MlSZs3b1ZZWZkWLVpU5ezuTy/tPl/atm2r//73vxowYEC18v9zxMTEKCAgQGlpadXmahoDAKAh4x5uAABM4vV6q90zHRMTo7i4OJWVlSkxMVFt27bVihUrVFRUVG3/7OzsamMvvfRSldcvvviiJGnQoEGSfjiD/OMzuAUFBXr99dd/2ZupxfDhw3X8+HG9+uqr1eY8Ho+Ki4vP6XgOh0MXX3yxNm3aVOW+97S0NH3wwQfVtg8ODq68HB0AgIaGM9wAAJikqKhIgwcP1rBhw9S1a1cFBwfro48+0hdffKHU1FTZ7XbNmTNH06ZN08iRI3XNNdeoefPmOn78uD7++GOFhoZq8eLFVY555MgR3XrrrRo4cKB2796tt99+WyNHjqxcofySSy6Ry+XSrbfeqrFjx6qoqEivvfaaYmJidPLkyfP+HkePHq1169Zp9uzZ+vjjj9WnTx95vV4dPHhQ69ev17PPPqsePXqc0zHvvPNOffjhhxo3bpzGjRsnn8+nF198URdeeKH2799fZdvTj1Z77rnnFBcXp/j4ePXq1et8vkUAAH42CjcAACYJDAzUuHHjtH37dm3YsEGGYaht27aaPXu2xo8fL+nUY7FeeeUVLVy4UC+++KKKi4sVGxurnj176vrrr692zHnz5umpp57S3/72NzmdTk2YMEH33Xdf5XyHDh00f/58zZs3T4899piaNWumcePGKTo6Wn/605/O+3u02+165pln9Pzzz+sf//iHNm7cqKCgIMXHx2vixIlq3779OR8zMTFRy5Yt0+OPP66nnnpKLVu21IwZM3Tw4EEdPHiwyrapqamaNWuW5s2bJ4/HozFjxlC4AQANhs1g1RAAABq8BQsW6Omnn9aOHTsUHR1tdRxL3H777fr222+1YcMGq6MAAHBWuIcbAAA0OB6Pp8rrQ4cOadu2berXr59FiQAAOHdcUg4AAJSdnS2v11vrvMvlUmRkZL3lueyyyzRmzBi1adNGGRkZevnll+VyuXTzzTfXWwYAAH4pCjcAANBvf/tbZWRk1Drfr18/rVq1qt7yDBw4UO+9955Onjwpt9ut3r176/e//70uuOCCessAAMAvxT3cAABAO3fuVGlpaa3z4eHhSkxMrMdEAAA0fhRuAAAAAABMwKJpAAAAAACYgMINAAAAAIAJKNwAAAAAAJiAwg0AAAAAgAko3AAAAAAAmIDCDQAAAACACSjcAAAAAACY4P8B+SSqOFfbxngAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 760
        },
        "id": "WaW7nCRYFX1R",
        "outputId": "8bbed969-0179-45d3-8a0b-8292f34646e8"
      },
      "source": [
        "from sklearn import tree\n",
        "tree.plot_tree(clf)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 0.75, 'x[0] <= 5.45\\ngini = 0.666\\nsamples = 127\\nvalue = [43, 39, 45]'),\n",
              " Text(0.25, 0.25, 'gini = 0.264\\nsamples = 46\\nvalue = [39, 6, 1]'),\n",
              " Text(0.375, 0.5, 'True  '),\n",
              " Text(0.75, 0.25, 'gini = 0.537\\nsamples = 81\\nvalue = [4, 33, 44]'),\n",
              " Text(0.625, 0.5, '  False')]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1170x827 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf = DecisionTreeClassifier(max_depth = 5)\n",
        "clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "Edt-Pd0fs7ON",
        "outputId": "dc0e3712-087a-4e8b-853b-42b81aab9daa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=5)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\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-3 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 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-3 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-3 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-3 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-3 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-3 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-3 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-3 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-3 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-3 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-3 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 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: 0.5em;\n",
              "  text-align: center;\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-3 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-3 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-3 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-3 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-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=5)</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=5)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf.score(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IF9FYREBtAND",
        "outputId": "063a7189-7912-4e68-e6a5-ef3169d09240"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8582677165354331"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k1827FhrtAjr",
        "outputId": "ab70bb1c-9746-43a2-c7dd-ea21b00b6bad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5217391304347826"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "decision_boundary(X_train, X_test, 'sepal_length', 'sepal_width', y_train, y_test, clf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 760
        },
        "id": "qwhYc4xytFdf",
        "outputId": "62e6399e-e7b4-4ce0-ac9d-9bee65dd8b24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1170x827 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tree.plot_tree(clf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Q9_rI8qatIue",
        "outputId": "b5a8a3d9-f96c-4368-afa8-cb9377b81897"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.4659090909090909, 0.9166666666666666, 'x[0] <= 5.45\\ngini = 0.666\\nsamples = 127\\nvalue = [43, 39, 45]'),\n",
              " Text(0.18181818181818182, 0.75, 'x[1] <= 2.8\\ngini = 0.264\\nsamples = 46\\nvalue = [39, 6, 1]'),\n",
              " Text(0.32386363636363635, 0.8333333333333333, 'True  '),\n",
              " Text(0.09090909090909091, 0.5833333333333334, 'x[0] <= 4.7\\ngini = 0.449\\nsamples = 7\\nvalue = [1, 5, 1]'),\n",
              " Text(0.045454545454545456, 0.4166666666666667, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0, 0]'),\n",
              " Text(0.13636363636363635, 0.4166666666666667, 'x[0] <= 4.95\\ngini = 0.278\\nsamples = 6\\nvalue = [0, 5, 1]'),\n",
              " Text(0.09090909090909091, 0.25, 'x[1] <= 2.45\\ngini = 0.5\\nsamples = 2\\nvalue = [0, 1, 1]'),\n",
              " Text(0.045454545454545456, 0.08333333333333333, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
              " Text(0.13636363636363635, 0.08333333333333333, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
              " Text(0.18181818181818182, 0.25, 'gini = 0.0\\nsamples = 4\\nvalue = [0, 4, 0]'),\n",
              " Text(0.2727272727272727, 0.5833333333333334, 'x[0] <= 5.35\\ngini = 0.05\\nsamples = 39\\nvalue = [38, 1, 0]'),\n",
              " Text(0.22727272727272727, 0.4166666666666667, 'gini = 0.0\\nsamples = 34\\nvalue = [34, 0, 0]'),\n",
              " Text(0.3181818181818182, 0.4166666666666667, 'x[1] <= 3.2\\ngini = 0.32\\nsamples = 5\\nvalue = [4, 1, 0]'),\n",
              " Text(0.2727272727272727, 0.25, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
              " Text(0.36363636363636365, 0.25, 'gini = 0.0\\nsamples = 4\\nvalue = [4, 0, 0]'),\n",
              " Text(0.75, 0.75, 'x[0] <= 5.95\\ngini = 0.537\\nsamples = 81\\nvalue = [4, 33, 44]'),\n",
              " Text(0.6079545454545454, 0.8333333333333333, '  False'),\n",
              " Text(0.5909090909090909, 0.5833333333333334, 'x[1] <= 3.35\\ngini = 0.473\\nsamples = 26\\nvalue = [4, 18, 4]'),\n",
              " Text(0.5454545454545454, 0.4166666666666667, 'x[0] <= 5.75\\ngini = 0.298\\nsamples = 22\\nvalue = [0, 18, 4]'),\n",
              " Text(0.45454545454545453, 0.25, 'x[1] <= 2.55\\ngini = 0.133\\nsamples = 14\\nvalue = [0, 13, 1]'),\n",
              " Text(0.4090909090909091, 0.08333333333333333, 'gini = 0.278\\nsamples = 6\\nvalue = [0, 5, 1]'),\n",
              " Text(0.5, 0.08333333333333333, 'gini = 0.0\\nsamples = 8\\nvalue = [0, 8, 0]'),\n",
              " Text(0.6363636363636364, 0.25, 'x[1] <= 2.65\\ngini = 0.469\\nsamples = 8\\nvalue = [0, 5, 3]'),\n",
              " Text(0.5909090909090909, 0.08333333333333333, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
              " Text(0.6818181818181818, 0.08333333333333333, 'gini = 0.49\\nsamples = 7\\nvalue = [0, 4, 3]'),\n",
              " Text(0.6363636363636364, 0.4166666666666667, 'gini = 0.0\\nsamples = 4\\nvalue = [4, 0, 0]'),\n",
              " Text(0.9090909090909091, 0.5833333333333334, 'x[0] <= 7.05\\ngini = 0.397\\nsamples = 55\\nvalue = [0.0, 15.0, 40.0]'),\n",
              " Text(0.8636363636363636, 0.4166666666666667, 'x[0] <= 6.95\\ngini = 0.449\\nsamples = 44\\nvalue = [0.0, 15.0, 29.0]'),\n",
              " Text(0.8181818181818182, 0.25, 'x[0] <= 6.35\\ngini = 0.439\\nsamples = 43\\nvalue = [0.0, 14.0, 29.0]'),\n",
              " Text(0.7727272727272727, 0.08333333333333333, 'gini = 0.494\\nsamples = 18\\nvalue = [0, 8, 10]'),\n",
              " Text(0.8636363636363636, 0.08333333333333333, 'gini = 0.365\\nsamples = 25\\nvalue = [0, 6, 19]'),\n",
              " Text(0.9090909090909091, 0.25, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
              " Text(0.9545454545454546, 0.4166666666666667, 'gini = 0.0\\nsamples = 11\\nvalue = [0, 0, 11]')]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1170x827 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kb9ZkAigBzf9"
      },
      "source": [
        "### Max voting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cN7QdmpfB2G9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e82b755-5b4c-4fe6-c123-3ad641767228"
      },
      "source": [
        "model1 = tree.DecisionTreeClassifier()\n",
        "model2 = KNeighborsClassifier()\n",
        "model3 = LogisticRegression()\n",
        "\n",
        "model1.fit(X_train,y_train)\n",
        "model2.fit(X_train,y_train)\n",
        "model3.fit(X_train,y_train)\n",
        "\n",
        "print(model1.score(X_train,y_train))\n",
        "print(model2.score(X_train,y_train))\n",
        "print(model3.score(X_train,y_train))\n",
        "print()\n",
        "\n",
        "print(model1.score(X_test, y_test))\n",
        "print(model2.score(X_test, y_test))\n",
        "print(model3.score(X_test, y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.937007874015748\n",
            "0.8503937007874016\n",
            "0.8503937007874016\n",
            "\n",
            "0.5652173913043478\n",
            "0.6956521739130435\n",
            "0.6956521739130435\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "glLGagFAEUoy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63b451b5-8ac8-4a02-a497-cb9cd99be9d8"
      },
      "source": [
        "from sklearn.ensemble import VotingClassifier\n",
        "model1 = tree.DecisionTreeClassifier()\n",
        "model2 = KNeighborsClassifier()\n",
        "model3= LogisticRegression()\n",
        "model = VotingClassifier(estimators=[('dt', model1), ('knn', model2), ('lr', model3)], voting='hard')\n",
        "model.fit(X_train,y_train)\n",
        "model.score(X_test,y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6956521739130435"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "decision_boundary(X_train, X_test, 'sepal_length', 'sepal_width', y_train, y_test, model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 829
        },
        "id": "MpFokoFpxaNs",
        "outputId": "300aa738-876d-416a-d0b8-c28288a9c1a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.12/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.12/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1170x827 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-cYSG-yWFCB3"
      },
      "source": [
        "### Averaging"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vnqJa_siFDqx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b36afae1-a144-490f-c197-cf1d1aa93535"
      },
      "source": [
        "model1 = tree.DecisionTreeClassifier()\n",
        "model2 = KNeighborsClassifier()\n",
        "model3 = LogisticRegression()\n",
        "\n",
        "model1.fit(X_train,y_train)\n",
        "model2.fit(X_train,y_train)\n",
        "model3.fit(X_train,y_train)\n",
        "\n",
        "pred1=model1.predict_proba(X_test)\n",
        "pred2=model2.predict_proba(X_test)\n",
        "pred3=model3.predict_proba(X_test)\n",
        "\n",
        "finalpred=(pred1+pred2+pred3)/3\n",
        "finalpred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2.52694994e-02, 5.67147503e-01, 4.07582998e-01],\n",
              "       [1.66518355e-03, 4.25127658e-01, 5.73207158e-01],\n",
              "       [9.88916021e-01, 5.68132531e-03, 5.40265401e-03],\n",
              "       [7.79962502e-05, 4.09908169e-02, 9.58931187e-01],\n",
              "       [9.71995331e-01, 2.28991843e-02, 5.10548460e-03],\n",
              "       [1.96025084e-02, 5.11322449e-01, 4.69075042e-01],\n",
              "       [9.79891007e-01, 1.62073260e-02, 3.90166690e-03],\n",
              "       [2.03206427e-03, 3.81888680e-01, 6.16079256e-01],\n",
              "       [5.18271474e-04, 2.86216223e-01, 7.13265505e-01],\n",
              "       [8.70524613e-03, 3.72077433e-01, 6.19217321e-01],\n",
              "       [4.49810008e-03, 6.53461612e-01, 3.42040288e-01],\n",
              "       [9.69764346e-03, 1.74882158e-01, 8.15420199e-01],\n",
              "       [8.70524613e-03, 3.72077433e-01, 6.19217321e-01],\n",
              "       [1.81786986e-03, 2.54707941e-01, 7.43474189e-01],\n",
              "       [1.20370386e-02, 4.64159473e-01, 5.23803489e-01],\n",
              "       [9.89625473e-01, 8.50454097e-03, 1.86998636e-03],\n",
              "       [1.73560891e-02, 3.07932682e-01, 6.74711229e-01],\n",
              "       [3.48912729e-02, 9.00076711e-01, 6.50320156e-02],\n",
              "       [9.44156305e-01, 4.99774846e-02, 5.86621052e-03],\n",
              "       [9.79683653e-01, 1.23953751e-02, 7.92097156e-03],\n",
              "       [4.77933586e-02, 8.72982560e-01, 7.92240813e-02],\n",
              "       [8.38635451e-02, 8.38990446e-01, 7.71460092e-02],\n",
              "       [9.84939917e-01, 1.29879761e-02, 2.07210705e-03]])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XcxK-H_SFKe_"
      },
      "source": [
        "### Weighted average"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yAJAHx4RFMdU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "626ac305-2020-49b3-f0fc-08cdc121cba3"
      },
      "source": [
        "model1 = tree.DecisionTreeClassifier()\n",
        "model2 = KNeighborsClassifier()\n",
        "model3 = LogisticRegression()\n",
        "\n",
        "model1.fit(X_train,y_train)\n",
        "model2.fit(X_train,y_train)\n",
        "model3.fit(X_train,y_train)\n",
        "\n",
        "pred1=model1.predict_proba(X_test)\n",
        "pred2=model2.predict_proba(X_test)\n",
        "pred3=model3.predict_proba(X_test)\n",
        "\n",
        "finalpred=(pred1+pred2*0.3+pred3*0.4)\n",
        "finalpred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[3.03233993e-02, 9.20577003e-01, 7.49099598e-01],\n",
              "       [1.99822026e-03, 4.50153190e-01, 1.24784859e+00],\n",
              "       [1.68669922e+00, 6.81759037e-03, 6.48318481e-03],\n",
              "       [9.35955002e-05, 4.91889802e-02, 1.65071742e+00],\n",
              "       [1.66639440e+00, 2.74790211e-02, 6.12658152e-03],\n",
              "       [2.35230101e-02, 1.19358694e+00, 4.82890051e-01],\n",
              "       [1.67586921e+00, 1.94487912e-02, 4.68200028e-03],\n",
              "       [2.43847713e-03, 7.18266416e-01, 9.79295107e-01],\n",
              "       [6.21925769e-04, 2.83459468e-01, 1.41591861e+00],\n",
              "       [1.04462954e-02, 3.86492920e-01, 1.30306078e+00],\n",
              "       [5.39772009e-03, 1.34415393e+00, 3.50448346e-01],\n",
              "       [1.16371722e-02, 1.89858589e-01, 1.49850424e+00],\n",
              "       [1.04462954e-02, 3.86492920e-01, 1.30306078e+00],\n",
              "       [2.18144384e-03, 2.65649530e-01, 1.43216903e+00],\n",
              "       [1.44444463e-02, 8.16991367e-01, 8.68564187e-01],\n",
              "       [1.68755057e+00, 1.02054492e-02, 2.24398363e-03],\n",
              "       [2.08273069e-02, 3.29519219e-01, 1.34965347e+00],\n",
              "       [4.18695275e-02, 1.58009205e+00, 7.80384187e-02],\n",
              "       [1.63298757e+00, 5.99729815e-02, 7.03945263e-03],\n",
              "       [1.67562038e+00, 1.48744501e-02, 9.50516587e-03],\n",
              "       [5.73520304e-02, 1.54757907e+00, 9.50688975e-02],\n",
              "       [1.00636254e-01, 1.50678853e+00, 9.25752111e-02],\n",
              "       [1.68192790e+00, 1.55855713e-02, 2.48652846e-03]])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xkNuoH77sPxC"
      },
      "source": [
        "## Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HI0FFCwv0A7h"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "obMCQHyx0Eye"
      },
      "source": [
        "#clf = RandomForestClassifier()\n",
        "#clf = RandomForestClassifier(max_depth=5)\n",
        "clf = RandomForestClassifier(max_depth=5, n_estimators=400)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cHIBbxL20GcQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "905963ea-d2e3-4bdc-8c05-e92945c9cc8c"
      },
      "source": [
        "clf.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=5, n_estimators=400)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\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-4 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 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-4 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-4 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-4 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-4 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-4 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-4 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-4 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-4 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-4 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-4 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 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: 0.5em;\n",
              "  text-align: center;\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-4 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-4 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-4 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-4 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-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=5, n_estimators=400)</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=5, n_estimators=400)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(clf.score(X_train, y_train))\n",
        "print(clf.score(X_test, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aNYqqWjtRQzk",
        "outputId": "217c59f8-93e8-42bb-b565-ebfa09b289a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.889763779527559\n",
            "0.6086956521739131\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "orK9BsWr0Inb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 760
        },
        "outputId": "5f579211-7f3b-40fc-f5c9-a1a5114c85f2"
      },
      "source": [
        "decision_boundary(X_train, X_test, 'sepal_length', 'sepal_width', y_train, y_test, clf)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1170x827 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf.feature_importances_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LJk_P3uElGBK",
        "outputId": "d0c4e628-2668-467c-c46e-4921261d832e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.65079702, 0.34920298])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bi9y5NlArR_j"
      },
      "source": [
        "## Boosting"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dQwK6nr0wK9J"
      },
      "source": [
        "### AdaBoost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jesg8AMPwY9r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3aaf3e3f-9d9b-47e4-b5ea-bed66bcc4641"
      },
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "clf = AdaBoostClassifier(random_state=1)\n",
        "clf.fit(X_train, y_train)\n",
        "print(clf.score(X_train, y_train))\n",
        "print(clf.score(X_test,y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8661417322834646\n",
            "0.6086956521739131\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BXW3i7370PSg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 760
        },
        "outputId": "3a439161-79c8-43aa-d0d9-8715d9fb2d69"
      },
      "source": [
        "decision_boundary(X_train, X_test, 'sepal_length', 'sepal_width', y_train, y_test, clf)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but AdaBoostClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1170x827 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gxH7Pm4cwNjq"
      },
      "source": [
        "### Gradient Boosting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LGRG62GpxOIf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de48d235-c70c-49dd-bfe4-fb31c19123fb"
      },
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "clf= GradientBoostingClassifier(learning_rate=0.01,random_state=1)\n",
        "clf.fit(X_train, y_train)\n",
        "print(clf.score(X_train, y_train))\n",
        "print(clf.score(X_test,y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8582677165354331\n",
            "0.6956521739130435\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Txm24aFs0W0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 760
        },
        "outputId": "7f60c886-9274-48ba-9312-38d956b8845b"
      },
      "source": [
        "decision_boundary(X_train, X_test, 'sepal_length', 'sepal_width', y_train, y_test, clf)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1170x827 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HXODUwyfwQZd"
      },
      "source": [
        "### XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O0UkDhbCxLX9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f35dd2a4-a615-4c35-adea-acedac317135"
      },
      "source": [
        "import xgboost as xgb\n",
        "clf = xgb.XGBClassifier(random_state=1,learning_rate=0.01)\n",
        "clf.fit(X_train, y_train)\n",
        "print(clf.score(X_train, y_train))\n",
        "print(clf.score(X_test,y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8740157480314961\n",
            "0.6086956521739131\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SGfEaCTY0cCz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "outputId": "6c195fed-7d1a-4c9c-8b7e-346aae29a42a"
      },
      "source": [
        "decision_boundary(X_train, X_test, 'sepal_length', 'sepal_width', y_train, y_test, clf)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1170x827 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "gSbeDuGtV8Aj"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1anT0eUpwSvq"
      },
      "source": [
        "### Light GBM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9N0aJO9Nwkf9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aecbf22e-43ba-430f-dbf4-fe446c4b61bd"
      },
      "source": [
        "import lightgbm as lgb\n",
        "train_data = lgb.Dataset(X_train, label=y_train)\n",
        "\n",
        "params = {'learning_rate':0.001}\n",
        "clf = lgb.train(params, train_data, 100)\n",
        "y_pred = model.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.015901 seconds.\n",
            "You can set `force_col_wise=true` to remove the overhead.\n",
            "[LightGBM] [Info] Total Bins 44\n",
            "[LightGBM] [Info] Number of data points in the train set: 127, number of used features: 2\n",
            "[LightGBM] [Info] Start training from score 1.015748\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Z6FvvLZ0lUA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "outputId": "2754888b-7aca-4f2d-9a1f-b64b3cb820d9"
      },
      "source": [
        "decision_boundary(X_train, X_test, 'sepal_length', 'sepal_width', y_train, y_test, clf)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1170x827 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FzD27BgYwV61"
      },
      "source": [
        "### CatBoost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pbWpyq0Hwdvg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b1b5dbc4-71ea-4624-fcaf-353f39cfe9c3"
      },
      "source": [
        "!pip install catboost"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting catboost\n",
            "  Downloading catboost-1.2.8-cp312-cp312-manylinux2014_x86_64.whl.metadata (1.2 kB)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.12/dist-packages (from catboost) (0.21)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (from catboost) (3.10.0)\n",
            "Requirement already satisfied: numpy<3.0,>=1.16.0 in /usr/local/lib/python3.12/dist-packages (from catboost) (2.0.2)\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.12/dist-packages (from catboost) (2.2.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.12/dist-packages (from catboost) (1.16.3)\n",
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            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib->catboost) (1.3.3)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib->catboost) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib->catboost) (4.60.1)\n",
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            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib->catboost) (25.0)\n",
            "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib->catboost) (11.3.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib->catboost) (3.2.5)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.12/dist-packages (from plotly->catboost) (9.1.2)\n",
            "Downloading catboost-1.2.8-cp312-cp312-manylinux2014_x86_64.whl (99.2 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.2/99.2 MB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: catboost\n",
            "Successfully installed catboost-1.2.8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "55B0gxW1wb4Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f908fc20-da06-4f81-f6ca-8aa1fe2fc629"
      },
      "source": [
        "from catboost import CatBoostClassifier\n",
        "clf = CatBoostClassifier()\n",
        "# categorical_features_indices = np.where(df.dtypes != np.float)[0]\n",
        "clf.fit(X_train, y_train)\n",
        "clf.score(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Learning rate set to 0.071223\n",
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        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "np.float64(0.5217391304347826)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1OCdUtpsTqIW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "outputId": "bc3b13a3-af93-4714-800f-511d50569bf8"
      },
      "source": [
        "decision_boundary(X_train, X_test, 'sepal_length', 'sepal_width', y_train, y_test, clf)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1170x827 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ch2WhD9b1Fd4"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}