{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "In this task I will implement some of the ensemble methods studied in the class (Bagging, Random Forest and AdaBoost) to build different classification models for the data from the dataset [Predict Diabetes](https://www.kaggle.com/datasets/whenamancodes/predict-diabities) from Kaggle."
      ],
      "metadata": {
        "id": "cUdCI4REuVEa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importing libraries"
      ],
      "metadata": {
        "id": "7BsISqLjd54U"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "QjpjZYbmdhtr"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Getting the data (2 options)"
      ],
      "metadata": {
        "id": "gPIrzetleBpc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# # To upload the file 'diabetes.csv' to drive\n",
        "\n",
        "# from google.colab import files\n",
        "# uploaded = files.upload()\n",
        "\n",
        "# # To read it from drive\n",
        "# import io\n",
        "# data = pd.read_csv(io.BytesIO(uploaded['diabetes.csv']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "fLbwPFYrP2xX",
        "outputId": "34236ce8-710d-48e7-920f-d282dd5de99c"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-0d311396-5552-4dde-858b-f4673b14cae4\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-0d311396-5552-4dde-858b-f4673b14cae4\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving diabetes.csv to diabetes.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# just in case the other way doesn't work, \n",
        "# then we have to download and upload the dataset diabetes.csv manually beforehand\n",
        "data = pd.read_csv('diabetes.csv')\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "sUVngcM8d_jv",
        "outputId": "9538cd62-7f1a-4543-d7db-2bb29d045121"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0            6      148             72             35        0  33.6   \n",
              "1            1       85             66             29        0  26.6   \n",
              "2            8      183             64              0        0  23.3   \n",
              "3            1       89             66             23       94  28.1   \n",
              "4            0      137             40             35      168  43.1   \n",
              "\n",
              "   DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                     0.627   50        1  \n",
              "1                     0.351   31        0  \n",
              "2                     0.672   32        1  \n",
              "3                     0.167   21        0  \n",
              "4                     2.288   33        1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c6fb9b26-b5d4-43ad-9460-5bf0af639041\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>148</td>\n",
              "      <td>72</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>183</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c6fb9b26-b5d4-43ad-9460-5bf0af639041')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-c6fb9b26-b5d4-43ad-9460-5bf0af639041 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-c6fb9b26-b5d4-43ad-9460-5bf0af639041');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Data shape: {data.shape}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dJ9pddd2fhvy",
        "outputId": "dc305b5c-3094-4deb-e51f-23f9489d3349"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data shape: (768, 9)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A4G4QGGbf0X6",
        "outputId": "4a1cb784-827f-40c2-bb41-0ae9cfb10055"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 768 entries, 0 to 767\n",
            "Data columns (total 9 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   Pregnancies               768 non-null    int64  \n",
            " 1   Glucose                   768 non-null    int64  \n",
            " 2   BloodPressure             768 non-null    int64  \n",
            " 3   SkinThickness             768 non-null    int64  \n",
            " 4   Insulin                   768 non-null    int64  \n",
            " 5   BMI                       768 non-null    float64\n",
            " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
            " 7   Age                       768 non-null    int64  \n",
            " 8   Outcome                   768 non-null    int64  \n",
            "dtypes: float64(2), int64(7)\n",
            "memory usage: 54.1 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QY3w6Rwbgfg4",
        "outputId": "ace4419d-5d44-48c3-ae20-2adc66155cf1"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                 0\n",
              "Glucose                     0\n",
              "BloodPressure               0\n",
              "SkinThickness               0\n",
              "Insulin                     0\n",
              "BMI                         0\n",
              "DiabetesPedigreeFunction    0\n",
              "Age                         0\n",
              "Outcome                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are no missing values"
      ],
      "metadata": {
        "id": "O__Wje54gZfg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Brief descriptive analysis"
      ],
      "metadata": {
        "id": "15GdPFmERJsU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "0Jqsr77of0EM",
        "outputId": "d13d1949-5201-42bd-988a-7f1a15307cc9"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
              "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
              "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
              "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
              "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
              "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
              "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
              "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
              "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
              "\n",
              "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
              "count  768.000000                768.000000  768.000000  768.000000  \n",
              "mean    31.992578                  0.471876   33.240885    0.348958  \n",
              "std      7.884160                  0.331329   11.760232    0.476951  \n",
              "min      0.000000                  0.078000   21.000000    0.000000  \n",
              "25%     27.300000                  0.243750   24.000000    0.000000  \n",
              "50%     32.000000                  0.372500   29.000000    0.000000  \n",
              "75%     36.600000                  0.626250   41.000000    1.000000  \n",
              "max     67.100000                  2.420000   81.000000    1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-79d0d252-0116-4ee2-9951-d1beccae6fa9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.845052</td>\n",
              "      <td>120.894531</td>\n",
              "      <td>69.105469</td>\n",
              "      <td>20.536458</td>\n",
              "      <td>79.799479</td>\n",
              "      <td>31.992578</td>\n",
              "      <td>0.471876</td>\n",
              "      <td>33.240885</td>\n",
              "      <td>0.348958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.369578</td>\n",
              "      <td>31.972618</td>\n",
              "      <td>19.355807</td>\n",
              "      <td>15.952218</td>\n",
              "      <td>115.244002</td>\n",
              "      <td>7.884160</td>\n",
              "      <td>0.331329</td>\n",
              "      <td>11.760232</td>\n",
              "      <td>0.476951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.078000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>27.300000</td>\n",
              "      <td>0.243750</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>30.500000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.372500</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>140.250000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>127.250000</td>\n",
              "      <td>36.600000</td>\n",
              "      <td>0.626250</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>846.000000</td>\n",
              "      <td>67.100000</td>\n",
              "      <td>2.420000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-79d0d252-0116-4ee2-9951-d1beccae6fa9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-79d0d252-0116-4ee2-9951-d1beccae6fa9 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-79d0d252-0116-4ee2-9951-d1beccae6fa9');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From dataset's kaggle page: https://www.kaggle.com/datasets/whenamancodes/predict-diabities\n",
        "\n",
        "Columns Description\n",
        "\n",
        "Pregnancies: to express the Number of pregnancies;<br>\n",
        "Glucose: to express the Glucose level in blood;<br>\n",
        "BloodPressure: to express the Blood pressure measurement;<br>\n",
        "SkinThickness: to express the thickness of the skin;<br>\n",
        "Insulin: to express the Insulin level in blood;<br>\n",
        "BMI: to express the Body mass index;<br>\n",
        "DiabetesPedigreeFunction: to express the Diabetes percentage;<br>\n",
        "Age: to express the age;<br>\n",
        "Outcome: to express the final result 1 is Yes and 0 is No."
      ],
      "metadata": {
        "id": "K8A4tXwXRTsX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pregnancies"
      ],
      "metadata": {
        "id": "nR4sjWiZU_-8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize= (12, 10))\n",
        "sns.boxplot(x='Outcome',y='Pregnancies',data=data)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "kbllbGKKR5IO",
        "outputId": "ac0cc606-4e46-43a9-a893-41ada16ae18c"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize= (12, 10))\n",
        "sns.histplot(data= data, x= 'Pregnancies',hue='Outcome', kde= True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "id": "zckCpH78VKx9",
        "outputId": "71ac1992-d64b-4cd1-e8ca-a583ccddc8ec"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Glucose"
      ],
      "metadata": {
        "id": "MiIFa6P9VxxF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,10))\n",
        "sns.boxplot(x='Outcome',y='Glucose',data=data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "mAE8lMCYUwrw",
        "outputId": "b3f20f22-3588-4bf6-d35b-38950b485ac2"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f90b402f850>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Blood Pressure"
      ],
      "metadata": {
        "id": "1cBUY9vXVzwF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,10))\n",
        "sns.boxplot(x='Outcome',y='BloodPressure',data=data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "_tRtt4sSU2Uy",
        "outputId": "1a710c92-bec6-4086-b485-23f0306b5c8a"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f90b3fee1f0>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Skin Thickness"
      ],
      "metadata": {
        "id": "pKhWnh2aWNZi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,10))\n",
        "sns.boxplot(x='Outcome',y='SkinThickness',data=data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "KZ6h0l7ub0zZ",
        "outputId": "4d9d593a-fb7e-4ab6-e86b-267891a9ce74"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe3378d4ca0>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Insulin"
      ],
      "metadata": {
        "id": "x1hzUWmGcHbM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,10))\n",
        "sns.boxplot(x='Outcome',y='Insulin',data=data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "IXfnCrsGcLBX",
        "outputId": "ba6bb700-13f2-4aaf-8a35-cc007df89045"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe3378422e0>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "BMI"
      ],
      "metadata": {
        "id": "ZopEFWVVcWGC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,10))\n",
        "sns.boxplot(x='Outcome',y='BMI',data=data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "id": "Mrqp4PswcW4W",
        "outputId": "0ec20ffc-b378-43ed-c529-cc19633558af"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe3378aa6a0>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Diabetes Pedigree Function"
      ],
      "metadata": {
        "id": "2PrJyw60cbBs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,10))\n",
        "sns.boxplot(x='Outcome',y='DiabetesPedigreeFunction',data=data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "dsH2kdSjciCZ",
        "outputId": "9ae9bd8e-b81a-4b2c-b188-470ec0377f77"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe337796610>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Age"
      ],
      "metadata": {
        "id": "6TbjeHBsco9O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,10))\n",
        "sns.boxplot(x='Outcome',y='Age',data=data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "id": "HAQorkoUcqLg",
        "outputId": "13bfccb2-4b3e-4b1b-fd3d-dfd8a365ca20"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe337848970>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model"
      ],
      "metadata": {
        "id": "CTK9N3UbiGKD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Splitting the data in train and test data"
      ],
      "metadata": {
        "id": "xKEbNGkHiSae"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X=data.drop('Outcome',axis=1)\n",
        "y=data['Outcome']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.3)\n"
      ],
      "metadata": {
        "id": "p6IlGDHAhV0-"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Random Forest"
      ],
      "metadata": {
        "id": "2uHI19abjflP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, diverse random forests are built using different numbers of trees on them. Some adjustment measures on each of them are presented"
      ],
      "metadata": {
        "id": "tEcZxQDKxTm5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score, precision_score, f1_score, recall_score\n",
        "\n",
        "index=[10,50]+list(range(100,1200,200))\n",
        "for n in index:\n",
        "    random_forest = RandomForestClassifier(n_estimators= n)\n",
        "    random_forest.fit(X_train, y_train)\n",
        "\n",
        "    # Predicting with the model\n",
        "    y_predict = random_forest.predict(X_test)\n",
        "\n",
        "    # Checking the adjustment of the model\n",
        "    print(f'number of trees={n}')\n",
        "    print(f'Accuracy: {accuracy_score(y_test,y_predict)}')\n",
        "    print(f'Precision: {precision_score(y_test,y_predict)}')\n",
        "    print(f'Recall: {recall_score(y_test,y_predict)}')\n",
        "    print(f'F1: {f1_score(y_test,y_predict)}')\n",
        "    print('\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nch0i8YSjKGW",
        "outputId": "8cfdf0b0-1c6a-4d82-d0c4-30dfb783d419"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of trees=10\n",
            "Accuracy: 0.6926406926406926\n",
            "Precision: 0.6666666666666666\n",
            "Recall: 0.4222222222222222\n",
            "F1: 0.5170068027210885\n",
            "\n",
            "\n",
            "number of trees=50\n",
            "Accuracy: 0.7186147186147186\n",
            "Precision: 0.6865671641791045\n",
            "Recall: 0.5111111111111111\n",
            "F1: 0.5859872611464968\n",
            "\n",
            "\n",
            "number of trees=100\n",
            "Accuracy: 0.7402597402597403\n",
            "Precision: 0.7083333333333334\n",
            "Recall: 0.5666666666666667\n",
            "F1: 0.6296296296296297\n",
            "\n",
            "\n",
            "number of trees=300\n",
            "Accuracy: 0.7359307359307359\n",
            "Precision: 0.6933333333333334\n",
            "Recall: 0.5777777777777777\n",
            "F1: 0.6303030303030303\n",
            "\n",
            "\n",
            "number of trees=500\n",
            "Accuracy: 0.7316017316017316\n",
            "Precision: 0.6842105263157895\n",
            "Recall: 0.5777777777777777\n",
            "F1: 0.6265060240963854\n",
            "\n",
            "\n",
            "number of trees=700\n",
            "Accuracy: 0.7445887445887446\n",
            "Precision: 0.7012987012987013\n",
            "Recall: 0.6\n",
            "F1: 0.6467065868263472\n",
            "\n",
            "\n",
            "number of trees=900\n",
            "Accuracy: 0.7402597402597403\n",
            "Precision: 0.6973684210526315\n",
            "Recall: 0.5888888888888889\n",
            "F1: 0.6385542168674698\n",
            "\n",
            "\n",
            "number of trees=1100\n",
            "Accuracy: 0.7402597402597403\n",
            "Precision: 0.6923076923076923\n",
            "Recall: 0.6\n",
            "F1: 0.6428571428571429\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Bagging"
      ],
      "metadata": {
        "id": "bUIYRh-ikhqT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Another way of building random forest can be using the BaggingClassifier"
      ],
      "metadata": {
        "id": "JnBjPUlknctr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import BaggingClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "bagging=BaggingClassifier(base_estimator=DecisionTreeClassifier(),\n",
        "                          n_estimators=100,\n",
        "                          bootstrap=True,\n",
        "                          bootstrap_features=True)\n",
        "\n",
        "bagging.fit(X_train, y_train)\n",
        "y_predict=bagging.predict(X_test)\n",
        "\n",
        "print(f'Accuracy: {accuracy_score(y_test,y_predict)}')\n",
        "print(f'Precision: {precision_score(y_test,y_predict)}')\n",
        "print(f'Recall: {recall_score(y_test,y_predict)}')\n",
        "print(f'F1: {f1_score(y_test,y_predict)}')\n",
        "print('\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ewePbtVAg2Nh",
        "outputId": "55a4c64f-1d38-445c-b69f-ea3844070322"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7186147186147186\n",
            "Precision: 0.676056338028169\n",
            "Recall: 0.5333333333333333\n",
            "F1: 0.5962732919254659\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Adaboost"
      ],
      "metadata": {
        "id": "Xh3bencKntRR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "\n",
        "index=range(50,2000,100)\n",
        "error_train_list=[]\n",
        "error_test_list=[]\n",
        "\n",
        "for n in index:\n",
        "    AdaBoost= AdaBoostClassifier(n_estimators=n,\n",
        "                                 random_state=42)\n",
        "\n",
        "    AdaBoost.fit(X_train,y_train)\n",
        "    y_predict = AdaBoost.predict(X_test)\n",
        "\n",
        "    # # Uncomment to show some scores of the model:\n",
        "    # print(f'number of iterations={n}')\n",
        "    # print(f'Accuracy: {accuracy_score(y_test,y_predict)}')\n",
        "    # print(f'Precision: {precision_score(y_test,y_predict)}')\n",
        "    # print(f'Recall: {recall_score(y_test,y_predict)}')\n",
        "    # print(f'F1: {f1_score(y_test,y_predict)}')\n",
        "    # print('\\n')\n",
        "\n",
        "    # Now we compute the Trainig and Test Error\n",
        "    y_train_predict=AdaBoost.predict(X_train)\n",
        "\n",
        "    error_train=np.sum((y_train-y_train_predict))\n",
        "    error_test=np.sum((y_test-y_predict))\n",
        "\n",
        "    # # We have other options to compute the error\n",
        "\n",
        "    # # We can compute the errors in module:\n",
        "    # error_train=np.sum(abs(y_train-y_train_predict))\n",
        "    # error_test=np.sum(abs(y_test-y_predict))\n",
        "\n",
        "    # # Or the Sum of Squared Errors \n",
        "    # error_train=np.sum((y_train-y_train_predict)^2)\n",
        "    # error_test=np.sum((y_test-y_predict)^2)\n",
        "\n",
        "    # But in this last case we observe a weird result\n",
        "\n",
        "    error_train_list.append(error_train)\n",
        "    error_test_list.append(error_test)\n",
        "    "
      ],
      "metadata": {
        "id": "yPae01BHoPWV"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the last iteration, these are the scores that were obtained:"
      ],
      "metadata": {
        "id": "PFLFObxk2-Zv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'number of iterations={n}')\n",
        "print(f'Accuracy: {accuracy_score(y_test,y_predict)}')\n",
        "print(f'Precision: {precision_score(y_test,y_predict)}')\n",
        "print(f'Recall: {recall_score(y_test,y_predict)}')\n",
        "print(f'F1: {f1_score(y_test,y_predict)}')\n",
        "print('\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NkJ6nrsj26b3",
        "outputId": "f1e87e92-a047-4496-cd8f-d35f3d1e065a"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of iterations=1950\n",
            "Accuracy: 0.696969696969697\n",
            "Precision: 0.6219512195121951\n",
            "Recall: 0.5666666666666667\n",
            "F1: 0.5930232558139535\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print(sse_train_list)\n",
        "# print(sse_test_list)\n",
        "\n",
        "plt.figure(figsize=(12,10))\n",
        "plt.plot(index,error_train_list)\n",
        "plt.plot(index,error_test_list)\n",
        "plt.legend(['Training error', 'Test error'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        },
        "id": "k-plbVTGzi1h",
        "outputId": "39849380-6f57-484b-a0cb-938b2d6ccca8"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[992, 1007, 1039, 1049, 1065, 1060, 1072, 1073, 1076, 1075, 1074, 1074, 1074, 1074, 1074]\n",
            "[341, 342, 339, 336, 329, 335, 341, 337, 322, 352, 347, 361, 347, 348, 352]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "And in this plot we can see that AdaBoost does not overfit"
      ],
      "metadata": {
        "id": "0AUFIk6W1Ate"
      }
    }
  ]
}