AlgorithmComparison1.ipynb

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        "<a href=\"https://colab.research.google.com/github/RochaErik/AlgorithmComparison/blob/main/AlgorithmComparison1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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            "Requirement already satisfied: catboost in /usr/local/lib/python3.10/dist-packages (1.2.1.1)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (from catboost) (0.20.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from catboost) (3.7.1)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from catboost) (1.23.5)\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.10/dist-packages (from catboost) (1.5.3)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from catboost) (1.11.2)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from catboost) (5.15.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from catboost) (1.16.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2023.3.post1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.1.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (0.11.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (4.42.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (23.1)\n",
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            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->catboost) (8.2.3)\n",
            "Requirement already satisfied: lightgbm in /usr/local/lib/python3.10/dist-packages (4.0.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from lightgbm) (1.23.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from lightgbm) (1.11.2)\n",
            "Requirement already satisfied: xgboost in /usr/local/lib/python3.10/dist-packages (1.7.6)\n",
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            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.11.2)\n"
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      ],
      "source": [
        "!pip install catboost\n",
        "!pip install lightgbm\n",
        "!pip install xgboost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "sp9bGvxdqiOw"
      },
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import time\n",
        "\n",
        "from sklearn import metrics\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import RepeatedKFold\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from catboost import CatBoostClassifier\n",
        "from lightgbm import LGBMClassifier\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\", category=FutureWarning, module=\"xgboost\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5BBqjJH0_5DI"
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      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DaYEQozCxuDa"
      },
      "source": [
        "# **(Unbalanced) Wine Dataset**"
      ]
    },
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      "cell_type": "markdown",
      "metadata": {
        "id": "EEzpklZG_6m1"
      },
      "source": [
        "---"
      ]
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      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "cLB_RFl4272X"
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      "outputs": [],
      "source": [
        "wine_df = pd.read_csv('/content/drive/MyDrive/DatasetSeminario/Wine/wine.data', header=None)"
      ]
    },
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              "   0      1     2     3     4    5     6     7     8     9     10    11    12  \\\n",
              "0   1  14.23  1.71  2.43  15.6  127  2.80  3.06  0.28  2.29  5.64  1.04  3.92   \n",
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              "2   1  13.16  2.36  2.67  18.6  101  2.80  3.24  0.30  2.81  5.68  1.03  3.17   \n",
              "3   1  14.37  1.95  2.50  16.8  113  3.85  3.49  0.24  2.18  7.80  0.86  3.45   \n",
              "4   1  13.24  2.59  2.87  21.0  118  2.80  2.69  0.39  1.82  4.32  1.04  2.93   \n",
              "\n",
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          "metadata": {},
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      "source": [
        "wine_df.head()"
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              "(178, 14)"
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        "wine_df.shape"
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    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0mA92PG9z08L",
        "outputId": "2a383ff0-94cd-4f13-cd71-a82aed3efcfd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 178 entries, 0 to 177\n",
            "Data columns (total 14 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   0       178 non-null    int64  \n",
            " 1   1       178 non-null    float64\n",
            " 2   2       178 non-null    float64\n",
            " 3   3       178 non-null    float64\n",
            " 4   4       178 non-null    float64\n",
            " 5   5       178 non-null    int64  \n",
            " 6   6       178 non-null    float64\n",
            " 7   7       178 non-null    float64\n",
            " 8   8       178 non-null    float64\n",
            " 9   9       178 non-null    float64\n",
            " 10  10      178 non-null    float64\n",
            " 11  11      178 non-null    float64\n",
            " 12  12      178 non-null    float64\n",
            " 13  13      178 non-null    int64  \n",
            "dtypes: float64(11), int64(3)\n",
            "memory usage: 19.6 KB\n"
          ]
        }
      ],
      "source": [
        "wine_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zbW32kDBz-b2",
        "outputId": "d65dee60-bae9-4764-94ca-2170b6382374"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       3\n",
              "1     126\n",
              "2     133\n",
              "3      79\n",
              "4      63\n",
              "5      53\n",
              "6      97\n",
              "7     132\n",
              "8      39\n",
              "9     101\n",
              "10    132\n",
              "11     78\n",
              "12    122\n",
              "13    121\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "wine_df.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cDhqUiGjvoMo",
        "outputId": "12495ab9-72fd-4ea3-9c55-ad64eff25f7b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    71\n",
              "1    59\n",
              "3    48\n",
              "Name: 0, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# Unbalanced dataset\n",
        "\n",
        "wine_df[0].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Y0p7i8i6Dg5",
        "outputId": "30fc13a5-a051-497f-c57b-313b915f04d6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     0\n",
              "1     0\n",
              "2     0\n",
              "3     0\n",
              "4     0\n",
              "5     0\n",
              "6     0\n",
              "7     0\n",
              "8     0\n",
              "9     0\n",
              "10    0\n",
              "11    0\n",
              "12    0\n",
              "13    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "wine_df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "M14nUml_1ENr"
      },
      "outputs": [],
      "source": [
        "X = wine_df.iloc[:, 1:]\n",
        "y = wine_df.iloc[:, 0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_Y3bk16q2Esj",
        "outputId": "18bfb32d-5d5a-46fc-865c-e311731ab254"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      1     2     3     4    5     6     7     8     9     10    11    12  \\\n",
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              "1  13.20  1.78  2.14  11.2  100  2.65  2.76  0.26  1.28  4.38  1.05  3.40   \n",
              "2  13.16  2.36  2.67  18.6  101  2.80  3.24  0.30  2.81  5.68  1.03  3.17   \n",
              "3  14.37  1.95  2.50  16.8  113  3.85  3.49  0.24  2.18  7.80  0.86  3.45   \n",
              "4  13.24  2.59  2.87  21.0  118  2.80  2.69  0.39  1.82  4.32  1.04  2.93   \n",
              "\n",
              "     13  \n",
              "0  1065  \n",
              "1  1050  \n",
              "2  1185  \n",
              "3  1480  \n",
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              "      <th>0</th>\n",
              "      <td>14.23</td>\n",
              "      <td>1.71</td>\n",
              "      <td>2.43</td>\n",
              "      <td>15.6</td>\n",
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              "      <td>1050</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.16</td>\n",
              "      <td>2.36</td>\n",
              "      <td>2.67</td>\n",
              "      <td>18.6</td>\n",
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              "      <td>3.24</td>\n",
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              "      <td>5.68</td>\n",
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              "      <td>3.17</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>3.45</td>\n",
              "      <td>1480</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13.24</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.87</td>\n",
              "      <td>21.0</td>\n",
              "      <td>118</td>\n",
              "      <td>2.80</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.82</td>\n",
              "      <td>4.32</td>\n",
              "      <td>1.04</td>\n",
              "      <td>2.93</td>\n",
              "      <td>735</td>\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
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              "        document.querySelector('#df-4b10141b-fe88-40a1-b981-995903ed31bd 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-4b10141b-fe88-40a1-b981-995903ed31bd');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-62472244-60f5-4389-b431-ab71ae8d5672\">\n",
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              "\n",
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              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      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",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\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",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-62472244-60f5-4389-b431-ab71ae8d5672 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "X.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "STE2y_VH6z9W",
        "outputId": "0ac46cbe-f7fe-47f6-8cbe-37e93fe65242"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(178, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "X.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2VEgeRAq2MPl",
        "outputId": "645dd638-6f9f-4826-c669-104b5a94e85a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    1\n",
              "2    1\n",
              "3    1\n",
              "4    1\n",
              "Name: 0, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "y.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2CiRHPzR62CI",
        "outputId": "c565e505-02ba-43b9-c0e9-eb259fed5221"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(178,)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "y.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "ROjsNxIAL9lJ"
      },
      "outputs": [],
      "source": [
        "# Since version 1.3.2 XGBoost needs target columns to start with 0 value\n",
        "\n",
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LWaCPbhOngY0",
        "outputId": "928d7ea0-b9b7-4d51-d31a-8e16001dffe0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "I7HeY9sNRVZ4"
      },
      "outputs": [],
      "source": [
        "names = [\n",
        "          'AdaBoost',\n",
        "          'GradBoost',\n",
        "          'CatBoost',\n",
        "          'LightGBM',\n",
        "          'XGBoost'\n",
        "        ]\n",
        "\n",
        "classifiers = [\n",
        "                AdaBoostClassifier(),\n",
        "                GradientBoostingClassifier(),\n",
        "                CatBoostClassifier(silent=True),\n",
        "                LGBMClassifier(verbosity=-1),\n",
        "                XGBClassifier()\n",
        "              ]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "OYE0u0vBO1SP"
      },
      "outputs": [],
      "source": [
        "rkf = RepeatedKFold(n_splits=10, n_repeats=10, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P75qlMx0QRwC",
        "outputId": "28d3f670-ff31-407f-bd1c-4eeb9352e20b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------- AdaBoost on Wine Dataset ---------\n",
            "[0.94444444 0.88888889 0.72222222 0.88888889 1.         0.94444444\n",
            " 0.94444444 0.94444444 1.         0.76470588 0.94444444 0.83333333\n",
            " 0.88888889 0.83333333 0.88888889 0.83333333 1.         0.83333333\n",
            " 0.82352941 0.94117647 0.94444444 0.88888889 0.94444444 1.\n",
            " 1.         0.66666667 0.94444444 0.88888889 0.88235294 0.76470588\n",
            " 1.         1.         0.88888889 1.         0.88888889 1.\n",
            " 1.         0.77777778 0.82352941 0.94117647 0.94444444 0.83333333\n",
            " 1.         1.         0.94444444 0.94444444 0.83333333 0.83333333\n",
            " 0.94117647 0.88235294 0.94444444 1.         0.94444444 0.88888889\n",
            " 0.94444444 0.88888889 0.94444444 0.83333333 0.94117647 0.88235294\n",
            " 1.         0.88888889 0.72222222 0.88888889 1.         0.94444444\n",
            " 0.94444444 0.88888889 0.88235294 0.88235294 0.66666667 0.88888889\n",
            " 0.88888889 0.77777778 0.77777778 1.         1.         0.72222222\n",
            " 1.         0.88235294 0.88888889 0.88888889 1.         0.88888889\n",
            " 0.94444444 0.72222222 1.         0.83333333 1.         0.82352941\n",
            " 0.88888889 0.94444444 0.94444444 0.88888889 0.88888889 0.5\n",
            " 0.88888889 0.88888889 0.94117647 0.94117647]\n",
            "Accuracy: 89.83% (8.94%)\n",
            "Execution Time: 12.68 seconds\n",
            "------------------------------\n",
            "--------- GradBoost on Wine Dataset ---------\n",
            "[0.94444444 0.94444444 1.         0.94444444 0.88888889 0.94444444\n",
            " 0.94444444 0.94444444 1.         0.94117647 1.         1.\n",
            " 0.83333333 0.94444444 0.94444444 0.94444444 1.         1.\n",
            " 1.         0.94117647 0.94444444 0.94444444 0.94444444 0.88888889\n",
            " 1.         1.         0.94444444 0.94444444 1.         1.\n",
            " 0.94444444 0.94444444 1.         1.         0.94444444 1.\n",
            " 1.         0.83333333 0.94117647 0.88235294 0.94444444 0.94444444\n",
            " 0.94444444 0.94444444 0.88888889 0.83333333 0.88888889 1.\n",
            " 1.         1.         0.83333333 1.         1.         0.83333333\n",
            " 0.94444444 0.94444444 1.         0.88888889 0.94117647 1.\n",
            " 1.         0.83333333 1.         0.88888889 1.         1.\n",
            " 1.         0.94444444 0.94117647 0.94117647 0.94444444 0.94444444\n",
            " 0.94444444 0.94444444 0.94444444 0.94444444 1.         1.\n",
            " 0.88235294 0.94117647 0.94444444 0.88888889 0.94444444 0.94444444\n",
            " 0.94444444 0.88888889 1.         0.94444444 1.         1.\n",
            " 0.94444444 1.         0.88888889 0.88888889 0.94444444 1.\n",
            " 0.94444444 1.         1.         0.94117647]\n",
            "Accuracy: 95.07% (4.69%)\n",
            "Execution Time: 35.46 seconds\n",
            "------------------------------\n",
            "--------- CatBoost on Wine Dataset ---------\n",
            "[1.         1.         1.         1.         1.         0.88888889\n",
            " 0.94444444 0.94444444 1.         1.         1.         1.\n",
            " 0.94444444 0.94444444 0.94444444 1.         1.         1.\n",
            " 1.         0.94117647 1.         0.94444444 1.         0.94444444\n",
            " 1.         1.         0.94444444 1.         1.         1.\n",
            " 1.         1.         1.         1.         1.         1.\n",
            " 1.         0.94444444 1.         0.88235294 1.         0.94444444\n",
            " 1.         0.94444444 1.         0.88888889 1.         1.\n",
            " 1.         1.         0.94444444 1.         1.         1.\n",
            " 1.         1.         1.         0.88888889 0.94117647 1.\n",
            " 1.         0.88888889 1.         0.94444444 1.         1.\n",
            " 1.         1.         1.         0.94117647 0.94444444 1.\n",
            " 1.         1.         0.94444444 0.94444444 1.         1.\n",
            " 1.         1.         0.94444444 1.         1.         1.\n",
            " 1.         0.94444444 1.         0.94444444 0.94117647 1.\n",
            " 1.         1.         0.94444444 1.         1.         1.\n",
            " 0.94444444 1.         1.         0.94117647]\n",
            "Accuracy: 97.98% (3.23%)\n",
            "Execution Time: 252.54 seconds\n",
            "------------------------------\n",
            "--------- LightGBM on Wine Dataset ---------\n",
            "[1.         1.         1.         1.         0.94444444 0.94444444\n",
            " 0.94444444 0.94444444 1.         1.         1.         1.\n",
            " 0.94444444 1.         0.94444444 1.         0.94444444 1.\n",
            " 1.         0.94117647 0.94444444 0.94444444 1.         0.94444444\n",
            " 1.         0.94444444 0.94444444 1.         1.         1.\n",
            " 0.94444444 1.         1.         1.         0.94444444 1.\n",
            " 1.         0.94444444 1.         0.94117647 1.         0.94444444\n",
            " 1.         0.94444444 1.         0.88888889 1.         0.88888889\n",
            " 1.         1.         0.94444444 1.         1.         0.94444444\n",
            " 1.         1.         1.         0.88888889 0.94117647 1.\n",
            " 1.         0.88888889 1.         0.94444444 1.         0.94444444\n",
            " 1.         1.         0.94117647 0.94117647 1.         1.\n",
            " 1.         0.94444444 0.94444444 0.94444444 1.         1.\n",
            " 1.         1.         0.94444444 1.         1.         1.\n",
            " 0.94444444 0.94444444 1.         0.94444444 0.94117647 1.\n",
            " 1.         1.         0.94444444 0.94444444 1.         1.\n",
            " 0.94444444 1.         1.         0.94117647]\n",
            "Accuracy: 97.42% (3.21%)\n",
            "Execution Time: 3.02 seconds\n",
            "------------------------------\n",
            "--------- XGBoost on Wine Dataset ---------\n",
            "[0.94444444 1.         1.         1.         1.         0.88888889\n",
            " 0.94444444 0.94444444 1.         1.         1.         1.\n",
            " 0.88888889 0.94444444 0.94444444 1.         1.         1.\n",
            " 0.94117647 0.88235294 0.94444444 0.94444444 0.94444444 0.94444444\n",
            " 1.         1.         0.94444444 1.         1.         0.88235294\n",
            " 1.         1.         1.         1.         0.94444444 1.\n",
            " 1.         0.94444444 1.         0.88235294 1.         0.94444444\n",
            " 1.         0.88888889 0.94444444 0.94444444 1.         1.\n",
            " 1.         1.         0.88888889 1.         1.         0.94444444\n",
            " 1.         1.         1.         0.88888889 0.88235294 1.\n",
            " 1.         0.88888889 1.         0.88888889 1.         1.\n",
            " 1.         1.         1.         0.94117647 0.94444444 1.\n",
            " 1.         0.94444444 0.88888889 0.94444444 1.         1.\n",
            " 1.         0.88235294 0.94444444 0.88888889 1.         1.\n",
            " 1.         0.94444444 1.         0.94444444 0.94117647 1.\n",
            " 1.         1.         0.88888889 0.88888889 1.         1.\n",
            " 0.94444444 1.         1.         0.94117647]\n",
            "Accuracy: 96.68% (4.24%)\n",
            "Execution Time: 3.69 seconds\n",
            "------------------------------\n"
          ]
        }
      ],
      "source": [
        "wine_scores = []\n",
        "wine_scores_mean = []\n",
        "wine_scores_std = []\n",
        "model_names = []\n",
        "execution_times = []\n",
        "\n",
        "for name, clf in zip(names, classifiers):\n",
        "  start_time = time.time()\n",
        "  results = cross_val_score(clf, X, y, cv=rkf)\n",
        "  end_time = time.time()\n",
        "\n",
        "  wine_scores.append(results)\n",
        "  wine_scores_mean.append(results.mean()*100)\n",
        "  wine_scores_std.append(results.std()*100)\n",
        "  model_names.append(name)\n",
        "  execution_time = end_time - start_time\n",
        "  execution_times.append(execution_time)\n",
        "\n",
        "  print(f'--------- {name} on Wine Dataset ---------')\n",
        "  print(results)\n",
        "  print('Accuracy: %.2f%% (%.2f%%)' % (results.mean()*100, results.std()*100))\n",
        "  print(f'Execution Time: {execution_time:.2f} seconds')\n",
        "  print('------------------------------')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "JyeMIPhKc-ur",
        "outputId": "48d15274-2413-495f-b1b7-3686c54daaac"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# algorithm comparison\n",
        "\n",
        "fig = plt.figure()\n",
        "fig.suptitle('Comparison between different Wine scores')\n",
        "ax = fig.add_subplot(111)\n",
        "plt.ylim(0.3, 1)\n",
        "plt.boxplot(wine_scores, showmeans=True)\n",
        "ax.set_xticklabels(model_names)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "vAnV1iX3nfAA"
      },
      "outputs": [],
      "source": [
        "Algo_results = pd.DataFrame()\n",
        "Algo_results['Names'] = names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "UEQWSVTF2_f5"
      },
      "outputs": [],
      "source": [
        "Algo_results['Wine'] = wine_scores_mean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Fpz-TrKQSsGC",
        "outputId": "747924ff-3d5c-4baa-d366-b9ff2f0ad302"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Names       Wine\n",
              "0   AdaBoost  89.830065\n",
              "1  GradBoost  95.071895\n",
              "2   CatBoost  97.977124\n",
              "3   LightGBM  97.421569\n",
              "4    XGBoost  96.676471"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bbbbaad1-fb5f-4a21-85b3-a90d7d2fe360\" 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>Names</th>\n",
              "      <th>Wine</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AdaBoost</td>\n",
              "      <td>89.830065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GradBoost</td>\n",
              "      <td>95.071895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CatBoost</td>\n",
              "      <td>97.977124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LightGBM</td>\n",
              "      <td>97.421569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>96.676471</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bbbbaad1-fb5f-4a21-85b3-a90d7d2fe360')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bbbbaad1-fb5f-4a21-85b3-a90d7d2fe360 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-bbbbaad1-fb5f-4a21-85b3-a90d7d2fe360');\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-38e33cf8-64a7-47a4-b6a5-0331a8144ae0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-38e33cf8-64a7-47a4-b6a5-0331a8144ae0')\"\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-38e33cf8-64a7-47a4-b6a5-0331a8144ae0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "Algo_results"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Algo_results_std = pd.DataFrame()\n",
        "Algo_results_std['Names'] = names"
      ],
      "metadata": {
        "id": "IJusGqz94KKB"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Algo_results_std['Wine'] = wine_scores_std"
      ],
      "metadata": {
        "id": "ra8V1SzAx6ye"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Algo_results_std"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "fIBecoKP4VIl",
        "outputId": "e8b64126-3176-4c75-f18e-497d927e583a"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Names      Wine\n",
              "0   AdaBoost  8.935178\n",
              "1  GradBoost  4.689441\n",
              "2   CatBoost  3.231395\n",
              "3   LightGBM  3.206245\n",
              "4    XGBoost  4.235288"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-47cc93ae-5d05-4ee4-a293-3fbf0deb8ed7\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "      <th>Names</th>\n",
              "      <th>Wine</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AdaBoost</td>\n",
              "      <td>8.935178</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GradBoost</td>\n",
              "      <td>4.689441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CatBoost</td>\n",
              "      <td>3.231395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LightGBM</td>\n",
              "      <td>3.206245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>4.235288</td>\n",
              "    </tr>\n",
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              "\n",
              "  <style>\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      cursor: pointer;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      background-color: #3B4455;\n",
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              "\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",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-47cc93ae-5d05-4ee4-a293-3fbf0deb8ed7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-47cc93ae-5d05-4ee4-a293-3fbf0deb8ed7');\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",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
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              "    border-radius: 50%;\n",
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              "    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",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    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-7db15048-e776-4610-ad72-88c32da11567 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "A6Mkl_OPDpX8"
      },
      "outputs": [],
      "source": [
        "Algo_time_results = pd.DataFrame()\n",
        "Algo_time_results['Names'] = names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "MCpH5_-FDpX8"
      },
      "outputs": [],
      "source": [
        "Algo_time_results['Wine'] = pd.Series(execution_times)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "m0XdbT-5DpX8",
        "outputId": "eabf1319-d240-424c-b8c2-00e597ced8e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Names        Wine\n",
              "0   AdaBoost   12.676296\n",
              "1  GradBoost   35.455523\n",
              "2   CatBoost  252.544651\n",
              "3   LightGBM    3.015574\n",
              "4    XGBoost    3.691690"
            ],
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-06306fd1-96a6-4c21-977c-a218eb766daa');\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",
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              "\n",
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              "    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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              "    60% {\n",
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              "      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",
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              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\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",
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              "        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",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "Algo_time_results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9RgnZO38_8oT"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nAozSx7Oy3yP"
      },
      "source": [
        "# **(Unbalanced) Breast Cancer Dataset**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5RpTejMN_-SK"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "JbPSbYacy4e0"
      },
      "outputs": [],
      "source": [
        "breast_cancer_df = pd.read_csv('/content/drive/MyDrive/DatasetSeminario/BreastCancer/Breast.dat', sep=',', header=None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "eCQAkcSI1_BO",
        "outputId": "698ea926-17ce-47eb-9351-0f10d7b34892"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   0  1  2  3  4   5  6  7  8  9\n",
              "0  5  1  1  1  2   1  3  1  1  2\n",
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              "3  6  8  8  1  3   4  3  7  1  2\n",
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              "        const element = document.querySelector('#df-32ede02f-2ef8-4e70-bd83-53c863833c81');\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-e21ce938-7274-4927-94bf-2f735c142e35\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e21ce938-7274-4927-94bf-2f735c142e35')\"\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-e21ce938-7274-4927-94bf-2f735c142e35 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "breast_cancer_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "di68UM453Qna",
        "outputId": "d6398215-275b-450e-ab44-1aa88cfdeb3e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(683, 10)\n"
          ]
        }
      ],
      "source": [
        "print(breast_cancer_df.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V-x6_f8y4pLk",
        "outputId": "4de53885-1de2-40e5-d096-02b0db4be53b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 683 entries, 0 to 682\n",
            "Data columns (total 10 columns):\n",
            " #   Column  Non-Null Count  Dtype\n",
            "---  ------  --------------  -----\n",
            " 0   0       683 non-null    int64\n",
            " 1   1       683 non-null    int64\n",
            " 2   2       683 non-null    int64\n",
            " 3   3       683 non-null    int64\n",
            " 4   4       683 non-null    int64\n",
            " 5   5       683 non-null    int64\n",
            " 6   6       683 non-null    int64\n",
            " 7   7       683 non-null    int64\n",
            " 8   8       683 non-null    int64\n",
            " 9   9       683 non-null    int64\n",
            "dtypes: int64(10)\n",
            "memory usage: 53.5 KB\n"
          ]
        }
      ],
      "source": [
        "breast_cancer_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "upSoCWls5AAa",
        "outputId": "ef1ce920-9a13-4e80-a5cd-131aa95600ad"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    10\n",
              "1    10\n",
              "2    10\n",
              "3    10\n",
              "4    10\n",
              "5    10\n",
              "6    10\n",
              "7    10\n",
              "8     9\n",
              "9     2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "breast_cancer_df.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q5ZuOxqSwzJK",
        "outputId": "8e62e4b1-b368-4c0d-c788-374d500c1ddf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    444\n",
              "4    239\n",
              "Name: 9, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "# Unbalanced dataset\n",
        "\n",
        "breast_cancer_df[9].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I8IXu3xq5Ah0",
        "outputId": "378ef763-83a0-443b-e85f-767b4887f8e4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    0\n",
              "2    0\n",
              "3    0\n",
              "4    0\n",
              "5    0\n",
              "6    0\n",
              "7    0\n",
              "8    0\n",
              "9    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "breast_cancer_df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "WpheUIpA6o_K"
      },
      "outputs": [],
      "source": [
        "X = breast_cancer_df.iloc[:, :-1]\n",
        "y = breast_cancer_df.iloc[:, -1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Lr4bR7qX65-M",
        "outputId": "dfcd7406-0a2f-488b-91d9-d28081825844"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   0  1  2  3  4   5  6  7  8\n",
              "0  5  1  1  1  2   1  3  1  1\n",
              "1  5  4  4  5  7  10  3  2  1\n",
              "2  3  1  1  1  2   2  3  1  1\n",
              "3  6  8  8  1  3   4  3  7  1\n",
              "4  4  1  1  3  2   1  3  1  1"
            ],
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      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-9aab60e5-4a5d-4dc0-8553-121a20b88579 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-9aab60e5-4a5d-4dc0-8553-121a20b88579');\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",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-240acb42-28ab-49c1-8056-156103f56c8a')\"\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",
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              "    </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",
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              "  }\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-240acb42-28ab-49c1-8056-156103f56c8a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "X.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "slq2CZ2y8zKb",
        "outputId": "71e6fc54-dfe6-489b-efa5-a8d2d6e1f6f6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2\n",
              "1    2\n",
              "2    2\n",
              "3    2\n",
              "4    2\n",
              "Name: 9, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "y.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "WAeFqL_F80wj"
      },
      "outputs": [],
      "source": [
        "# Since version 1.3.2 XGBoost needs target columns to start with 0 value\n",
        "\n",
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A9RzMyY68_MF",
        "outputId": "f782ece5-a90a-4386-e760-0d4865910954"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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          "metadata": {},
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      "source": [
        "y"
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    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ccEf4KDr9WrG",
        "outputId": "32a775bd-5b27-404e-95be-d200e9d7287f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------- AdaBoost on Breast Cancer Dataset ---------\n",
            "[0.98550725 0.97101449 0.97101449 0.92647059 0.92647059 0.95588235\n",
            " 0.92647059 0.98529412 0.94117647 1.         0.95652174 0.95652174\n",
            " 0.95652174 0.97058824 0.95588235 0.95588235 0.95588235 0.92647059\n",
            " 0.98529412 0.97058824 0.97101449 0.97101449 0.89855072 0.98529412\n",
            " 1.         0.94117647 0.95588235 0.94117647 0.97058824 0.97058824\n",
            " 0.98550725 0.88405797 0.94202899 0.98529412 1.         0.95588235\n",
            " 1.         0.92647059 0.97058824 0.95588235 1.         0.89855072\n",
            " 0.92753623 0.95588235 0.94117647 0.94117647 1.         0.91176471\n",
            " 0.98529412 0.98529412 0.97101449 0.95652174 0.95652174 0.95588235\n",
            " 0.97058824 0.92647059 0.91176471 0.97058824 1.         0.92647059\n",
            " 0.94202899 0.94202899 0.98550725 0.92647059 0.97058824 0.98529412\n",
            " 0.97058824 0.98529412 0.95588235 0.95588235 0.97101449 0.97101449\n",
            " 0.94202899 0.94117647 0.97058824 0.98529412 0.94117647 0.98529412\n",
            " 0.92647059 0.95588235 0.95652174 0.92753623 0.91304348 0.94117647\n",
            " 0.95588235 0.95588235 0.97058824 0.95588235 0.98529412 0.95588235\n",
            " 0.92753623 0.98550725 0.94202899 0.95588235 0.92647059 0.98529412\n",
            " 1.         0.94117647 0.97058824 0.95588235]\n",
            "Accuracy: 95.80% (2.54%)\n",
            "Execution Time: 8.14 seconds\n",
            "------------------------------\n",
            "--------- GradBoost on Breast Cancer Dataset ---------\n",
            "[0.98550725 0.97101449 0.98550725 0.97058824 0.91176471 0.95588235\n",
            " 0.95588235 0.98529412 0.92647059 0.98529412 0.98550725 0.95652174\n",
            " 0.97101449 0.97058824 0.98529412 0.94117647 0.95588235 0.92647059\n",
            " 0.98529412 0.98529412 0.98550725 0.95652174 0.97101449 0.97058824\n",
            " 0.98529412 0.94117647 0.95588235 0.95588235 0.92647059 1.\n",
            " 0.98550725 0.86956522 0.95652174 1.         1.         0.97058824\n",
            " 1.         0.94117647 0.97058824 0.98529412 1.         0.97101449\n",
            " 0.92753623 0.95588235 0.97058824 0.92647059 1.         0.92647059\n",
            " 0.97058824 0.98529412 0.97101449 0.97101449 0.97101449 0.95588235\n",
            " 0.98529412 0.91176471 0.91176471 1.         1.         0.95588235\n",
            " 0.94202899 0.94202899 0.98550725 0.92647059 0.98529412 0.98529412\n",
            " 0.97058824 1.         0.95588235 0.98529412 1.         0.97101449\n",
            " 0.94202899 0.95588235 0.97058824 0.98529412 0.95588235 0.98529412\n",
            " 0.92647059 0.95588235 0.97101449 0.97101449 0.95652174 0.97058824\n",
            " 0.98529412 0.94117647 0.95588235 0.95588235 0.98529412 0.95588235\n",
            " 0.92753623 0.98550725 0.94202899 0.98529412 0.94117647 0.98529412\n",
            " 0.98529412 0.98529412 0.98529412 0.92647059]\n",
            "Accuracy: 96.52% (2.52%)\n",
            "Execution Time: 14.58 seconds\n",
            "------------------------------\n",
            "--------- CatBoost on Breast Cancer Dataset ---------\n",
            "[0.98550725 0.98550725 0.98550725 0.97058824 0.91176471 0.97058824\n",
            " 0.95588235 1.         0.92647059 0.98529412 0.98550725 0.94202899\n",
            " 0.97101449 0.97058824 0.98529412 0.95588235 0.98529412 0.94117647\n",
            " 1.         0.98529412 0.98550725 0.97101449 0.97101449 0.98529412\n",
            " 0.98529412 0.94117647 0.95588235 0.97058824 0.97058824 1.\n",
            " 1.         0.88405797 0.95652174 1.         1.         0.95588235\n",
            " 1.         0.94117647 0.97058824 0.98529412 1.         0.97101449\n",
            " 0.95652174 0.97058824 0.97058824 0.95588235 1.         0.92647059\n",
            " 0.97058824 0.98529412 0.97101449 0.98550725 0.95652174 0.95588235\n",
            " 0.98529412 0.95588235 0.95588235 1.         1.         0.95588235\n",
            " 0.95652174 0.95652174 0.97101449 0.92647059 0.98529412 0.97058824\n",
            " 0.98529412 1.         0.95588235 0.97058824 1.         0.98550725\n",
            " 0.94202899 0.94117647 0.97058824 0.98529412 0.95588235 1.\n",
            " 0.94117647 0.98529412 0.97101449 0.97101449 0.94202899 0.97058824\n",
            " 1.         0.97058824 0.97058824 0.95588235 0.98529412 0.97058824\n",
            " 0.95652174 1.         0.95652174 0.98529412 0.94117647 0.98529412\n",
            " 1.         0.98529412 0.98529412 0.94117647]\n",
            "Accuracy: 97.09% (2.22%)\n",
            "Execution Time: 65.53 seconds\n",
            "------------------------------\n",
            "--------- LightGBM on Breast Cancer Dataset ---------\n",
            "[0.98550725 0.97101449 0.98550725 0.97058824 0.91176471 0.97058824\n",
            " 0.95588235 0.98529412 0.92647059 0.97058824 0.98550725 0.94202899\n",
            " 0.97101449 0.97058824 0.98529412 0.95588235 0.97058824 0.92647059\n",
            " 0.97058824 0.98529412 0.98550725 0.95652174 0.97101449 0.95588235\n",
            " 0.98529412 0.95588235 0.97058824 0.98529412 0.95588235 1.\n",
            " 0.98550725 0.88405797 0.95652174 1.         1.         0.97058824\n",
            " 1.         0.94117647 0.97058824 0.97058824 1.         0.97101449\n",
            " 0.94202899 0.95588235 0.97058824 0.94117647 1.         0.89705882\n",
            " 0.97058824 0.97058824 0.97101449 0.95652174 0.95652174 0.94117647\n",
            " 0.98529412 0.94117647 0.92647059 1.         1.         0.94117647\n",
            " 0.94202899 0.95652174 0.98550725 0.92647059 1.         0.98529412\n",
            " 0.98529412 0.97058824 0.97058824 0.97058824 0.98550725 1.\n",
            " 0.94202899 0.94117647 0.98529412 0.98529412 0.95588235 0.98529412\n",
            " 0.92647059 0.97058824 0.95652174 0.97101449 0.97101449 0.95588235\n",
            " 0.98529412 0.94117647 0.97058824 0.95588235 1.         0.97058824\n",
            " 0.94202899 0.98550725 0.94202899 0.98529412 0.92647059 0.97058824\n",
            " 1.         0.97058824 0.98529412 0.94117647]\n",
            "Accuracy: 96.63% (2.37%)\n",
            "Execution Time: 2.53 seconds\n",
            "------------------------------\n",
            "--------- XGBoost on Breast Cancer Dataset ---------\n",
            "[1.         0.95652174 0.98550725 0.97058824 0.92647059 0.97058824\n",
            " 0.95588235 0.98529412 0.94117647 0.97058824 0.97101449 0.95652174\n",
            " 0.97101449 0.97058824 0.98529412 0.94117647 0.97058824 0.94117647\n",
            " 0.97058824 0.97058824 0.97101449 0.94202899 0.98550725 0.94117647\n",
            " 0.98529412 0.92647059 0.94117647 0.95588235 0.95588235 1.\n",
            " 0.98550725 0.88405797 0.95652174 1.         1.         0.95588235\n",
            " 1.         0.94117647 0.95588235 0.97058824 0.98550725 0.97101449\n",
            " 0.91304348 0.94117647 0.97058824 0.95588235 1.         0.92647059\n",
            " 0.97058824 0.97058824 0.95652174 0.97101449 0.97101449 0.95588235\n",
            " 0.97058824 0.92647059 0.92647059 0.98529412 1.         0.95588235\n",
            " 0.92753623 0.97101449 0.97101449 0.94117647 0.95588235 0.97058824\n",
            " 0.98529412 0.97058824 0.94117647 0.98529412 1.         0.97101449\n",
            " 0.94202899 0.95588235 0.95588235 0.98529412 0.94117647 0.98529412\n",
            " 0.94117647 0.98529412 0.97101449 0.95652174 0.92753623 0.98529412\n",
            " 0.98529412 0.95588235 0.97058824 0.95588235 0.98529412 0.97058824\n",
            " 0.92753623 0.98550725 0.94202899 0.98529412 0.94117647 0.98529412\n",
            " 1.         0.97058824 0.98529412 0.97058824]\n",
            "Accuracy: 96.43% (2.26%)\n",
            "Execution Time: 5.29 seconds\n",
            "------------------------------\n"
          ]
        }
      ],
      "source": [
        "breast_cancer_scores = []\n",
        "breast_cancer_mean = []\n",
        "breast_cancer_std = []\n",
        "model_names = []\n",
        "execution_times = []\n",
        "\n",
        "for name, clf in zip(names, classifiers):\n",
        "  start_time = time.time()\n",
        "  results = cross_val_score(clf, X, y, cv=rkf)\n",
        "  end_time = time.time()\n",
        "\n",
        "  breast_cancer_scores.append(results)\n",
        "  breast_cancer_mean.append(results.mean()*100)\n",
        "  breast_cancer_std.append(results.std()*100)\n",
        "  model_names.append(name)\n",
        "  execution_time = end_time - start_time\n",
        "  execution_times.append(execution_time)\n",
        "\n",
        "  print(f'--------- {name} on Breast Cancer Dataset ---------')\n",
        "  print(results)\n",
        "  print('Accuracy: %.2f%% (%.2f%%)' % (results.mean()*100, results.std()*100))\n",
        "  print(f'Execution Time: {execution_time:.2f} seconds')\n",
        "  print('------------------------------')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AkTUk4gn-FKB",
        "outputId": "556c7728-3007-4256-89a7-6cab6d2b31ff"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[95.8005115089514,\n",
              " 96.51577152600169,\n",
              " 97.08716965046888,\n",
              " 96.63299232736571,\n",
              " 96.42902813299233]"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "breast_cancer_mean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xMQWf2ij-FxY",
        "outputId": "1beeea77-bb39-4cdb-f0b0-4f55c40c8f38"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2.5424281877731705,\n",
              " 2.515754192531237,\n",
              " 2.2230461706335842,\n",
              " 2.373965102772195,\n",
              " 2.256038579109944]"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "breast_cancer_std"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "Yh_3nnzm06js",
        "outputId": "1cef4d31-9702-4292-8e43-43a2458f0fd4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# algorithm comparison\n",
        "\n",
        "fig = plt.figure()\n",
        "fig.suptitle('Comparison between different breast cancer scores')\n",
        "ax = fig.add_subplot(111)\n",
        "plt.ylim(0.3, 1)\n",
        "plt.boxplot(breast_cancer_scores, showmeans=True)\n",
        "ax.set_xticklabels(model_names)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "9nQQkiPHN73a"
      },
      "outputs": [],
      "source": [
        "Algo_results['Breast_Cancer'] = breast_cancer_mean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "i7iNa-cK1RLM",
        "outputId": "deda1133-6028-45f2-8d39-866b406adddf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Names       Wine  Breast_Cancer\n",
              "0   AdaBoost  89.830065      95.800512\n",
              "1  GradBoost  95.071895      96.515772\n",
              "2   CatBoost  97.977124      97.087170\n",
              "3   LightGBM  97.421569      96.632992\n",
              "4    XGBoost  96.676471      96.429028"
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          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "Algo_results"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Algo_results_std['Breast_Cancer'] = breast_cancer_std"
      ],
      "metadata": {
        "id": "_qWZaf4FyF5J"
      },
      "execution_count": 200,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "XnvRKO15DpX_"
      },
      "outputs": [],
      "source": [
        "Algo_time_results['Breast_Cancer'] = pd.Series(execution_times)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZSA6hkPxABDD"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZSumSo7AG3Uj"
      },
      "source": [
        "# **(Unbalanced) Sonar Dataset** #"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A5yoLZVMACu6"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "TE511TBWG607"
      },
      "outputs": [],
      "source": [
        "sonar_df = pd.read_csv('/content/drive/MyDrive/DatasetSeminario/Sonar/Sonar.csv', header=None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4nqaFrWdI2mE",
        "outputId": "4b4c0f0e-bcb3-42e8-fbec-6f2973397e9a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(208, 61)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "sonar_df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "tre-aKRXI4u0",
        "outputId": "84acb1ea-1a44-4666-e9ac-9962d127ddfc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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              "       0       1       2       3       4       5       6       7       8   \\\n",
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              "      <th>4</th>\n",
              "      <td>0.0762</td>\n",
              "      <td>0.0666</td>\n",
              "      <td>0.0481</td>\n",
              "      <td>0.0394</td>\n",
              "      <td>0.0590</td>\n",
              "      <td>0.0649</td>\n",
              "      <td>0.1209</td>\n",
              "      <td>0.2467</td>\n",
              "      <td>0.3564</td>\n",
              "      <td>0.4459</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0031</td>\n",
              "      <td>0.0054</td>\n",
              "      <td>0.0105</td>\n",
              "      <td>0.0110</td>\n",
              "      <td>0.0015</td>\n",
              "      <td>0.0072</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.0107</td>\n",
              "      <td>0.0094</td>\n",
              "      <td>R</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 61 columns</p>\n",
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              "      buttonEl.style.display =\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "        dataTable['output_type'] = 'display_data';\n",
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              "  @keyframes spin {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    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",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "sonar_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hnem-MjxI8Gc",
        "outputId": "06c7eb0f-f050-4ed8-ad7b-8f89b4530a24"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 208 entries, 0 to 207\n",
            "Data columns (total 61 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   0       208 non-null    float64\n",
            " 1   1       208 non-null    float64\n",
            " 2   2       208 non-null    float64\n",
            " 3   3       208 non-null    float64\n",
            " 4   4       208 non-null    float64\n",
            " 5   5       208 non-null    float64\n",
            " 6   6       208 non-null    float64\n",
            " 7   7       208 non-null    float64\n",
            " 8   8       208 non-null    float64\n",
            " 9   9       208 non-null    float64\n",
            " 10  10      208 non-null    float64\n",
            " 11  11      208 non-null    float64\n",
            " 12  12      208 non-null    float64\n",
            " 13  13      208 non-null    float64\n",
            " 14  14      208 non-null    float64\n",
            " 15  15      208 non-null    float64\n",
            " 16  16      208 non-null    float64\n",
            " 17  17      208 non-null    float64\n",
            " 18  18      208 non-null    float64\n",
            " 19  19      208 non-null    float64\n",
            " 20  20      208 non-null    float64\n",
            " 21  21      208 non-null    float64\n",
            " 22  22      208 non-null    float64\n",
            " 23  23      208 non-null    float64\n",
            " 24  24      208 non-null    float64\n",
            " 25  25      208 non-null    float64\n",
            " 26  26      208 non-null    float64\n",
            " 27  27      208 non-null    float64\n",
            " 28  28      208 non-null    float64\n",
            " 29  29      208 non-null    float64\n",
            " 30  30      208 non-null    float64\n",
            " 31  31      208 non-null    float64\n",
            " 32  32      208 non-null    float64\n",
            " 33  33      208 non-null    float64\n",
            " 34  34      208 non-null    float64\n",
            " 35  35      208 non-null    float64\n",
            " 36  36      208 non-null    float64\n",
            " 37  37      208 non-null    float64\n",
            " 38  38      208 non-null    float64\n",
            " 39  39      208 non-null    float64\n",
            " 40  40      208 non-null    float64\n",
            " 41  41      208 non-null    float64\n",
            " 42  42      208 non-null    float64\n",
            " 43  43      208 non-null    float64\n",
            " 44  44      208 non-null    float64\n",
            " 45  45      208 non-null    float64\n",
            " 46  46      208 non-null    float64\n",
            " 47  47      208 non-null    float64\n",
            " 48  48      208 non-null    float64\n",
            " 49  49      208 non-null    float64\n",
            " 50  50      208 non-null    float64\n",
            " 51  51      208 non-null    float64\n",
            " 52  52      208 non-null    float64\n",
            " 53  53      208 non-null    float64\n",
            " 54  54      208 non-null    float64\n",
            " 55  55      208 non-null    float64\n",
            " 56  56      208 non-null    float64\n",
            " 57  57      208 non-null    float64\n",
            " 58  58      208 non-null    float64\n",
            " 59  59      208 non-null    float64\n",
            " 60  60      208 non-null    object \n",
            "dtypes: float64(60), object(1)\n",
            "memory usage: 99.2+ KB\n"
          ]
        }
      ],
      "source": [
        "sonar_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3_9Ryg6UOVpT",
        "outputId": "5ce92bcf-2807-4671-e340-9ba621c658a9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "M    111\n",
              "R     97\n",
              "Name: 60, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ],
      "source": [
        "# A little unbalanced dataset\n",
        "\n",
        "sonar_df[60].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "SPh4tBE9O7Ad"
      },
      "outputs": [],
      "source": [
        "X = sonar_df.iloc[:, :-1]\n",
        "y = sonar_df.iloc[:, -1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "MZbLtYxvPO3L",
        "outputId": "8248db45-5780-4d6c-8110-b6f8fbfc6798"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "[5 rows x 60 columns]"
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              "      <td>0.0033</td>\n",
              "      <td>0.0232</td>\n",
              "      <td>0.0166</td>\n",
              "      <td>0.0095</td>\n",
              "      <td>0.0180</td>\n",
              "      <td>0.0244</td>\n",
              "      <td>0.0316</td>\n",
              "      <td>0.0164</td>\n",
              "      <td>0.0095</td>\n",
              "      <td>0.0078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0100</td>\n",
              "      <td>0.0171</td>\n",
              "      <td>0.0623</td>\n",
              "      <td>0.0205</td>\n",
              "      <td>0.0205</td>\n",
              "      <td>0.0368</td>\n",
              "      <td>0.1098</td>\n",
              "      <td>0.1276</td>\n",
              "      <td>0.0598</td>\n",
              "      <td>0.1264</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0241</td>\n",
              "      <td>0.0121</td>\n",
              "      <td>0.0036</td>\n",
              "      <td>0.0150</td>\n",
              "      <td>0.0085</td>\n",
              "      <td>0.0073</td>\n",
              "      <td>0.0050</td>\n",
              "      <td>0.0044</td>\n",
              "      <td>0.0040</td>\n",
              "      <td>0.0117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0762</td>\n",
              "      <td>0.0666</td>\n",
              "      <td>0.0481</td>\n",
              "      <td>0.0394</td>\n",
              "      <td>0.0590</td>\n",
              "      <td>0.0649</td>\n",
              "      <td>0.1209</td>\n",
              "      <td>0.2467</td>\n",
              "      <td>0.3564</td>\n",
              "      <td>0.4459</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0156</td>\n",
              "      <td>0.0031</td>\n",
              "      <td>0.0054</td>\n",
              "      <td>0.0105</td>\n",
              "      <td>0.0110</td>\n",
              "      <td>0.0015</td>\n",
              "      <td>0.0072</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.0107</td>\n",
              "      <td>0.0094</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 60 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5085157c-8957-4988-9798-686ed91e48d7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  <style>\n",
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              "      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-5085157c-8957-4988-9798-686ed91e48d7 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-5085157c-8957-4988-9798-686ed91e48d7');\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-741ac65e-88b4-40e4-ab5f-9d4840838dc3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-741ac65e-88b4-40e4-ab5f-9d4840838dc3')\"\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-741ac65e-88b4-40e4-ab5f-9d4840838dc3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "X.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WuwAUv8zPSBw",
        "outputId": "4ca8227b-8abe-4f9c-90c8-e7f95d56685b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    R\n",
              "1    R\n",
              "2    R\n",
              "3    R\n",
              "4    R\n",
              "Name: 60, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "y.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "jVOAmeJdPTbz"
      },
      "outputs": [],
      "source": [
        "# Since version 1.3.2 XGBoost needs target columns to start with 0 value\n",
        "\n",
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dHjeR07gPbGY",
        "outputId": "bd1782a2-f18d-412c-f3e5-d8133613ae38"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------- AdaBoost on Sonar Dataset ---------\n",
            "[0.9047619  0.76190476 0.76190476 0.76190476 0.85714286 0.80952381\n",
            " 0.9047619  0.85714286 0.9        0.8        0.80952381 0.85714286\n",
            " 0.57142857 0.76190476 0.80952381 0.80952381 0.85714286 0.80952381\n",
            " 0.75       0.85       0.9047619  0.85714286 0.9047619  0.76190476\n",
            " 0.85714286 0.85714286 0.85714286 0.80952381 0.8        0.9\n",
            " 0.76190476 0.85714286 0.80952381 0.9047619  0.85714286 0.80952381\n",
            " 0.76190476 0.71428571 0.75       0.95       0.76190476 0.80952381\n",
            " 0.76190476 0.95238095 0.9047619  0.80952381 0.95238095 0.85714286\n",
            " 0.8        0.8        0.9047619  0.9047619  0.9047619  0.66666667\n",
            " 0.76190476 0.80952381 0.9047619  0.85714286 0.9        0.95\n",
            " 0.9047619  0.85714286 0.85714286 0.9047619  0.9047619  0.80952381\n",
            " 0.85714286 0.76190476 0.85       0.7        0.66666667 0.95238095\n",
            " 0.80952381 0.66666667 0.76190476 0.9047619  0.71428571 0.9047619\n",
            " 1.         0.95       0.61904762 0.9047619  0.85714286 0.9047619\n",
            " 0.80952381 0.85714286 0.71428571 0.85714286 0.85       0.8\n",
            " 0.85714286 0.9047619  0.76190476 0.80952381 0.95238095 0.80952381\n",
            " 0.71428571 0.9047619  0.85       0.8       ]\n",
            "Accuracy: 83.14% (7.84%)\n",
            "Execution Time: 12.05 seconds\n",
            "------------------------------\n",
            "--------- GradBoost on Sonar Dataset ---------\n",
            "[0.95238095 0.71428571 0.80952381 0.76190476 0.80952381 0.80952381\n",
            " 0.80952381 0.9047619  0.85       0.8        0.95238095 0.85714286\n",
            " 0.66666667 0.95238095 0.80952381 0.76190476 0.9047619  0.9047619\n",
            " 0.75       0.85       0.95238095 0.80952381 0.95238095 0.80952381\n",
            " 0.71428571 0.80952381 0.76190476 0.95238095 0.75       0.9\n",
            " 0.85714286 0.95238095 0.76190476 0.9047619  0.76190476 0.85714286\n",
            " 0.71428571 0.80952381 0.8        1.         0.85714286 0.9047619\n",
            " 0.76190476 0.95238095 0.9047619  0.85714286 0.9047619  0.9047619\n",
            " 0.75       0.8        0.85714286 0.95238095 0.85714286 0.71428571\n",
            " 0.66666667 0.76190476 0.85714286 0.9047619  0.9        1.\n",
            " 1.         0.9047619  0.71428571 0.9047619  0.80952381 0.80952381\n",
            " 0.85714286 0.85714286 0.9        0.6        0.61904762 0.9047619\n",
            " 0.66666667 0.80952381 0.95238095 0.85714286 0.9047619  0.85714286\n",
            " 0.95       0.9        0.80952381 0.85714286 0.76190476 0.95238095\n",
            " 0.80952381 0.85714286 0.76190476 0.9047619  0.85       0.8\n",
            " 0.85714286 0.85714286 0.85714286 0.80952381 0.9047619  0.85714286\n",
            " 0.76190476 0.85714286 0.9        0.85      ]\n",
            "Accuracy: 84.19% (8.34%)\n",
            "Execution Time: 27.72 seconds\n",
            "------------------------------\n",
            "--------- CatBoost on Sonar Dataset ---------\n",
            "[0.95238095 0.80952381 0.80952381 0.85714286 0.80952381 0.85714286\n",
            " 0.9047619  0.85714286 0.95       0.8        0.95238095 0.9047619\n",
            " 0.76190476 0.85714286 0.85714286 0.85714286 0.95238095 0.85714286\n",
            " 0.75       0.95       0.9047619  0.85714286 1.         0.9047619\n",
            " 0.85714286 0.85714286 0.80952381 0.95238095 0.75       0.9\n",
            " 0.9047619  0.85714286 0.85714286 0.9047619  0.9047619  0.9047619\n",
            " 0.85714286 0.76190476 0.85       1.         0.9047619  0.9047619\n",
            " 0.76190476 0.85714286 0.95238095 0.95238095 0.95238095 0.95238095\n",
            " 0.75       0.85       1.         0.95238095 0.95238095 0.76190476\n",
            " 0.71428571 0.71428571 0.85714286 0.9047619  0.95       0.95\n",
            " 1.         0.9047619  0.80952381 0.9047619  0.85714286 0.85714286\n",
            " 0.95238095 0.80952381 0.9        0.65       0.66666667 0.95238095\n",
            " 0.76190476 0.95238095 1.         0.95238095 0.80952381 0.9047619\n",
            " 0.95       0.85       0.80952381 0.95238095 0.80952381 0.95238095\n",
            " 0.85714286 0.85714286 0.71428571 0.85714286 0.85       0.8\n",
            " 0.95238095 0.85714286 0.95238095 0.71428571 1.         0.95238095\n",
            " 0.71428571 0.85714286 0.85       0.9       ]\n",
            "Accuracy: 87.15% (8.02%)\n",
            "Execution Time: 745.91 seconds\n",
            "------------------------------\n",
            "--------- LightGBM on Sonar Dataset ---------\n",
            "[0.95238095 0.80952381 0.80952381 0.85714286 0.85714286 0.85714286\n",
            " 1.         0.85714286 0.95       0.9        0.95238095 0.9047619\n",
            " 0.80952381 0.85714286 0.9047619  0.80952381 0.9047619  0.95238095\n",
            " 0.75       0.85       0.85714286 0.85714286 1.         0.85714286\n",
            " 0.9047619  0.9047619  0.80952381 0.95238095 0.8        0.95\n",
            " 0.85714286 0.9047619  0.95238095 0.9047619  0.85714286 1.\n",
            " 0.85714286 0.71428571 0.85       1.         0.76190476 0.9047619\n",
            " 0.80952381 0.95238095 0.9047619  0.9047619  0.95238095 1.\n",
            " 0.8        0.85       0.9047619  0.95238095 0.95238095 0.80952381\n",
            " 0.76190476 0.85714286 0.80952381 0.95238095 0.9        0.9\n",
            " 0.95238095 0.95238095 0.85714286 0.9047619  0.95238095 0.9047619\n",
            " 0.85714286 0.80952381 0.95       0.7        0.61904762 0.95238095\n",
            " 0.76190476 0.9047619  0.95238095 0.85714286 0.80952381 0.85714286\n",
            " 1.         0.95       0.80952381 0.9047619  0.85714286 0.95238095\n",
            " 0.85714286 0.80952381 0.80952381 0.95238095 0.85       0.85\n",
            " 0.95238095 0.95238095 0.9047619  0.85714286 1.         0.9047619\n",
            " 0.76190476 0.85714286 0.9        0.9       ]\n",
            "Accuracy: 88.12% (7.25%)\n",
            "Execution Time: 2.90 seconds\n",
            "------------------------------\n",
            "--------- XGBoost on Sonar Dataset ---------\n",
            "[0.9047619  0.71428571 0.71428571 0.85714286 0.80952381 0.95238095\n",
            " 0.85714286 0.76190476 0.95       0.8        0.95238095 0.80952381\n",
            " 0.76190476 0.85714286 0.76190476 0.80952381 0.85714286 0.9047619\n",
            " 0.75       0.9        0.9047619  0.76190476 0.95238095 0.80952381\n",
            " 0.95238095 0.80952381 0.76190476 0.95238095 0.75       0.95\n",
            " 0.85714286 0.85714286 0.71428571 0.85714286 0.85714286 1.\n",
            " 0.76190476 0.85714286 0.9        0.95       0.76190476 0.9047619\n",
            " 0.76190476 0.85714286 0.9047619  0.9047619  0.95238095 0.9047619\n",
            " 0.7        0.8        0.80952381 0.95238095 0.80952381 0.76190476\n",
            " 0.76190476 0.80952381 0.9047619  0.9047619  0.95       1.\n",
            " 0.95238095 0.85714286 0.9047619  0.9047619  0.95238095 0.80952381\n",
            " 0.85714286 0.85714286 0.85       0.7        0.66666667 1.\n",
            " 0.71428571 0.95238095 0.9047619  0.85714286 0.80952381 0.76190476\n",
            " 0.95       0.95       0.76190476 0.95238095 0.85714286 0.9047619\n",
            " 0.80952381 0.85714286 0.61904762 0.9047619  0.8        0.85\n",
            " 0.80952381 0.9047619  0.80952381 0.76190476 0.95238095 0.95238095\n",
            " 0.76190476 0.85714286 0.85       0.8       ]\n",
            "Accuracy: 85.01% (8.25%)\n",
            "Execution Time: 8.71 seconds\n",
            "------------------------------\n"
          ]
        }
      ],
      "source": [
        "sonar_scores = []\n",
        "sonar_mean = []\n",
        "sonar_std = []\n",
        "model_names = []\n",
        "execution_times = []\n",
        "\n",
        "\n",
        "for name, clf in zip(names, classifiers):\n",
        "  start_time = time.time()\n",
        "  results = cross_val_score(clf, X, y, cv=rkf)\n",
        "  end_time = time.time()\n",
        "\n",
        "  sonar_scores.append(results)\n",
        "  sonar_mean.append(results.mean()*100)\n",
        "  sonar_std.append(results.std()*100)\n",
        "  model_names.append(name)\n",
        "  execution_time = end_time - start_time\n",
        "  execution_times.append(execution_time)\n",
        "\n",
        "  print(f'--------- {name} on Sonar Dataset ---------')\n",
        "  print(results)\n",
        "  print('Accuracy: %.2f%% (%.2f%%)' % (results.mean()*100, results.std()*100))\n",
        "  print(f'Execution Time: {execution_time:.2f} seconds')\n",
        "  print('------------------------------')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eh8lLhqlPqM8",
        "outputId": "d0e683b1-f0f4-4ad1-8f2c-666cb6116b0f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[83.14047619047616,\n",
              " 84.1857142857143,\n",
              " 87.15238095238097,\n",
              " 88.12380952380954,\n",
              " 85.00714285714285]"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "source": [
        "sonar_mean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "myzV6qKpPr4B",
        "outputId": "880ca83e-dd00-4eb5-d1ec-d29edf809949"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[7.844412125028975,\n",
              " 8.340077543046963,\n",
              " 8.016770969663867,\n",
              " 7.246883762213835,\n",
              " 8.24734238785956]"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ],
      "source": [
        "sonar_std"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "cfuxv8kN1rWS",
        "outputId": "22a9ba6b-e7b3-43a5-b50d-6693ea6a758b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# algorithm comparison\n",
        "\n",
        "fig = plt.figure()\n",
        "fig.suptitle('Comparison between different Sonar scores')\n",
        "ax = fig.add_subplot(111)\n",
        "plt.ylim(0.3, 1)\n",
        "plt.boxplot(sonar_scores, showmeans=True)\n",
        "ax.set_xticklabels(model_names)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "x-H2UhXA2veS"
      },
      "outputs": [],
      "source": [
        "Algo_results['Sonar'] = sonar_mean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "rP_87sjC3MlD",
        "outputId": "1c5f6f65-8ecb-4a4a-f2f7-e9ad0a1c9057"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Names       Wine  Breast_Cancer      Sonar\n",
              "0   AdaBoost  89.830065      95.800512  83.140476\n",
              "1  GradBoost  95.071895      96.515772  84.185714\n",
              "2   CatBoost  97.977124      97.087170  87.152381\n",
              "3   LightGBM  97.421569      96.632992  88.123810\n",
              "4    XGBoost  96.676471      96.429028  85.007143"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-121c4526-9825-445a-bf38-f8b638eba23d\" 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>Names</th>\n",
              "      <th>Wine</th>\n",
              "      <th>Breast_Cancer</th>\n",
              "      <th>Sonar</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AdaBoost</td>\n",
              "      <td>89.830065</td>\n",
              "      <td>95.800512</td>\n",
              "      <td>83.140476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GradBoost</td>\n",
              "      <td>95.071895</td>\n",
              "      <td>96.515772</td>\n",
              "      <td>84.185714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CatBoost</td>\n",
              "      <td>97.977124</td>\n",
              "      <td>97.087170</td>\n",
              "      <td>87.152381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LightGBM</td>\n",
              "      <td>97.421569</td>\n",
              "      <td>96.632992</td>\n",
              "      <td>88.123810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>96.676471</td>\n",
              "      <td>96.429028</td>\n",
              "      <td>85.007143</td>\n",
              "    </tr>\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-121c4526-9825-445a-bf38-f8b638eba23d 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-121c4526-9825-445a-bf38-f8b638eba23d');\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",
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              "\n",
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              "            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",
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              "    </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",
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              "    fill: var(--fill-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    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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              "    20% {\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    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",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2caf0c44-76e2-4dbf-b080-c3621bba0b1c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ],
      "source": [
        "Algo_results"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Algo_results_std['Sonar'] = sonar_std"
      ],
      "metadata": {
        "id": "5aVExdmnyPoB"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "kC6292DXDpYC"
      },
      "outputs": [],
      "source": [
        "Algo_time_results['Sonar'] = pd.Series(execution_times)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O7KMhnwc_uPM"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kz0ZzSmO_iJr"
      },
      "source": [
        "# **(Unbalanced) Ionosphere Dataset**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DDtq-OWc_zUk"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "id": "ajQJ-r9f_X7S"
      },
      "outputs": [],
      "source": [
        "ionosphere_df = pd.read_csv('/content/drive/MyDrive/DatasetSeminario/Ionosphere/ionosphere.data', header=None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ITgOuyvx_UB5",
        "outputId": "6e138bf2-63a7-4753-f5b2-58f9fa927bb9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(351, 35)"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ],
      "source": [
        "ionosphere_df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "vmmoDlxyBpdV",
        "outputId": "50d242d7-269c-4b29-da4c-1cb0bb0bdd4a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   0   1        2        3        4        5        6        7        8   \\\n",
              "0   1   0  0.99539 -0.05889  0.85243  0.02306  0.83398 -0.37708  1.00000   \n",
              "1   1   0  1.00000 -0.18829  0.93035 -0.36156 -0.10868 -0.93597  1.00000   \n",
              "2   1   0  1.00000 -0.03365  1.00000  0.00485  1.00000 -0.12062  0.88965   \n",
              "3   1   0  1.00000 -0.45161  1.00000  1.00000  0.71216 -1.00000  0.00000   \n",
              "4   1   0  1.00000 -0.02401  0.94140  0.06531  0.92106 -0.23255  0.77152   \n",
              "\n",
              "        9   ...       25       26       27       28       29       30  \\\n",
              "0  0.03760  ... -0.51171  0.41078 -0.46168  0.21266 -0.34090  0.42267   \n",
              "1 -0.04549  ... -0.26569 -0.20468 -0.18401 -0.19040 -0.11593 -0.16626   \n",
              "2  0.01198  ... -0.40220  0.58984 -0.22145  0.43100 -0.17365  0.60436   \n",
              "3  0.00000  ...  0.90695  0.51613  1.00000  1.00000 -0.20099  0.25682   \n",
              "4 -0.16399  ... -0.65158  0.13290 -0.53206  0.02431 -0.62197 -0.05707   \n",
              "\n",
              "        31       32       33  34  \n",
              "0 -0.54487  0.18641 -0.45300   g  \n",
              "1 -0.06288 -0.13738 -0.02447   b  \n",
              "2 -0.24180  0.56045 -0.38238   g  \n",
              "3  1.00000 -0.32382  1.00000   b  \n",
              "4 -0.59573 -0.04608 -0.65697   g  \n",
              "\n",
              "[5 rows x 35 columns]"
            ],
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              "      <th></th>\n",
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              "      <th>8</th>\n",
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              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>32</th>\n",
              "      <th>33</th>\n",
              "      <th>34</th>\n",
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              "      <td>g</td>\n",
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              "      <td>1.00000</td>\n",
              "      <td>-0.04549</td>\n",
              "      <td>...</td>\n",
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              "      <td>-0.11593</td>\n",
              "      <td>-0.16626</td>\n",
              "      <td>-0.06288</td>\n",
              "      <td>-0.13738</td>\n",
              "      <td>-0.02447</td>\n",
              "      <td>b</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>-0.03365</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.00485</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>-0.12062</td>\n",
              "      <td>0.88965</td>\n",
              "      <td>0.01198</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.40220</td>\n",
              "      <td>0.58984</td>\n",
              "      <td>-0.22145</td>\n",
              "      <td>0.43100</td>\n",
              "      <td>-0.17365</td>\n",
              "      <td>0.60436</td>\n",
              "      <td>-0.24180</td>\n",
              "      <td>0.56045</td>\n",
              "      <td>-0.38238</td>\n",
              "      <td>g</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>-0.45161</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.71216</td>\n",
              "      <td>-1.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.90695</td>\n",
              "      <td>0.51613</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>-0.20099</td>\n",
              "      <td>0.25682</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>-0.32382</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>b</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>-0.02401</td>\n",
              "      <td>0.94140</td>\n",
              "      <td>0.06531</td>\n",
              "      <td>0.92106</td>\n",
              "      <td>-0.23255</td>\n",
              "      <td>0.77152</td>\n",
              "      <td>-0.16399</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.65158</td>\n",
              "      <td>0.13290</td>\n",
              "      <td>-0.53206</td>\n",
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              "      <td>-0.62197</td>\n",
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              "      <td>-0.59573</td>\n",
              "      <td>-0.04608</td>\n",
              "      <td>-0.65697</td>\n",
              "      <td>g</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 35 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ],
      "source": [
        "ionosphere_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "akdiGMMcBvF9",
        "outputId": "a5583aa0-d943-4fae-8d41-65bdc5a7e830"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 351 entries, 0 to 350\n",
            "Data columns (total 35 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   0       351 non-null    int64  \n",
            " 1   1       351 non-null    int64  \n",
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            " 6   6       351 non-null    float64\n",
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            " 10  10      351 non-null    float64\n",
            " 11  11      351 non-null    float64\n",
            " 12  12      351 non-null    float64\n",
            " 13  13      351 non-null    float64\n",
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            " 33  33      351 non-null    float64\n",
            " 34  34      351 non-null    object \n",
            "dtypes: float64(32), int64(2), object(1)\n",
            "memory usage: 96.1+ KB\n"
          ]
        }
      ],
      "source": [
        "ionosphere_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MXxFZefgC9KO",
        "outputId": "078e1ee4-f279-44b3-f8ab-5ff435fe1387"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "0       2\n",
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              "2     219\n",
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              "24    256\n",
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              "26    256\n",
              "27    281\n",
              "28    244\n",
              "29    266\n",
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              "32    245\n",
              "33    263\n",
              "34      2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ],
      "source": [
        "ionosphere_df.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vc6vnLxumv_p",
        "outputId": "2aa4aded-ea21-476a-837e-29f04bb18544"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     0\n",
              "1     0\n",
              "2     0\n",
              "3     0\n",
              "4     0\n",
              "5     0\n",
              "6     0\n",
              "7     0\n",
              "8     0\n",
              "9     0\n",
              "10    0\n",
              "11    0\n",
              "12    0\n",
              "13    0\n",
              "14    0\n",
              "15    0\n",
              "16    0\n",
              "17    0\n",
              "18    0\n",
              "19    0\n",
              "20    0\n",
              "21    0\n",
              "22    0\n",
              "23    0\n",
              "24    0\n",
              "25    0\n",
              "26    0\n",
              "27    0\n",
              "28    0\n",
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              "30    0\n",
              "31    0\n",
              "32    0\n",
              "33    0\n",
              "34    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ],
      "source": [
        "ionosphere_df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Nm_j2Vcm242",
        "outputId": "84b7de75-b759-4f6d-a167-9846bb82559c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "g    225\n",
              "b    126\n",
              "Name: 34, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ],
      "source": [
        "# This is a unbalanced dataset\n",
        "\n",
        "ionosphere_df[34].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "id": "tnlzrTEPEoFt"
      },
      "outputs": [],
      "source": [
        "X = ionosphere_df.iloc[:, :-1]\n",
        "y = ionosphere_df.iloc[:, -1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "gDpMlk9SFPgV",
        "outputId": "c4211986-87eb-4c06-e4c0-f96a5dfad2a6"
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      "outputs": [
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          "output_type": "execute_result",
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              "      <td>0.88965</td>\n",
              "      <td>0.01198</td>\n",
              "      <td>...</td>\n",
              "      <td>0.57528</td>\n",
              "      <td>-0.40220</td>\n",
              "      <td>0.58984</td>\n",
              "      <td>-0.22145</td>\n",
              "      <td>0.43100</td>\n",
              "      <td>-0.17365</td>\n",
              "      <td>0.60436</td>\n",
              "      <td>-0.24180</td>\n",
              "      <td>0.56045</td>\n",
              "      <td>-0.38238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>-0.45161</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.71216</td>\n",
              "      <td>-1.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.90695</td>\n",
              "      <td>0.51613</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>-0.20099</td>\n",
              "      <td>0.25682</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>-0.32382</td>\n",
              "      <td>1.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>-0.02401</td>\n",
              "      <td>0.94140</td>\n",
              "      <td>0.06531</td>\n",
              "      <td>0.92106</td>\n",
              "      <td>-0.23255</td>\n",
              "      <td>0.77152</td>\n",
              "      <td>-0.16399</td>\n",
              "      <td>...</td>\n",
              "      <td>0.03286</td>\n",
              "      <td>-0.65158</td>\n",
              "      <td>0.13290</td>\n",
              "      <td>-0.53206</td>\n",
              "      <td>0.02431</td>\n",
              "      <td>-0.62197</td>\n",
              "      <td>-0.05707</td>\n",
              "      <td>-0.59573</td>\n",
              "      <td>-0.04608</td>\n",
              "      <td>-0.65697</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 34 columns</p>\n",
              "</div>\n",
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              "\n",
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              "    .colab-df-convert:hover {\n",
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              "    .colab-df-buttons div {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "        document.querySelector('#df-b221b060-44fe-45da-a47d-049593a6b0a8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        const element = document.querySelector('#df-b221b060-44fe-45da-a47d-049593a6b0a8');\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",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cb2fdaf3-85a0-40f9-b24d-61028e876a05')\"\n",
              "            title=\"Suggest charts.\"\n",
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              "\n",
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              "    </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-cb2fdaf3-85a0-40f9-b24d-61028e876a05 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ],
      "source": [
        "X.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K6Ll2GwgFW80",
        "outputId": "f155c77e-cad4-4d9d-dfb2-f0bab012cc8d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    g\n",
              "1    b\n",
              "2    g\n",
              "3    b\n",
              "4    g\n",
              "Name: 34, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ],
      "source": [
        "y.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "id": "Z1rL4KSzFa-S"
      },
      "outputs": [],
      "source": [
        "# Since version 1.3.2 XGBoost needs target columns to start with 0 value\n",
        "\n",
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1RMApy4YFi4X",
        "outputId": "af53dada-99b1-40ea-eee2-4d4fbd8326f6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------- AdaBoost on Ionosphere Dataset ---------\n",
            "[0.88888889 0.91428571 0.97142857 0.91428571 0.97142857 0.94285714\n",
            " 0.94285714 1.         0.88571429 0.94285714 0.88888889 0.85714286\n",
            " 0.97142857 1.         0.97142857 0.94285714 0.94285714 0.94285714\n",
            " 0.85714286 0.91428571 0.94444444 0.97142857 0.88571429 0.88571429\n",
            " 0.97142857 0.97142857 0.91428571 0.94285714 0.91428571 0.94285714\n",
            " 0.91666667 0.94285714 0.97142857 0.88571429 0.85714286 0.94285714\n",
            " 1.         0.97142857 0.88571429 0.97142857 0.91666667 0.97142857\n",
            " 0.97142857 0.85714286 0.85714286 0.91428571 0.94285714 0.97142857\n",
            " 0.97142857 0.97142857 0.91666667 0.91428571 0.94285714 1.\n",
            " 0.97142857 0.91428571 0.94285714 0.85714286 0.97142857 0.88571429\n",
            " 0.86111111 0.91428571 0.94285714 0.88571429 0.91428571 0.91428571\n",
            " 0.91428571 1.         1.         0.91428571 0.80555556 1.\n",
            " 0.97142857 0.85714286 0.94285714 0.91428571 0.97142857 0.85714286\n",
            " 1.         0.91428571 0.97222222 0.97142857 0.88571429 0.91428571\n",
            " 0.85714286 0.94285714 0.94285714 1.         0.97142857 0.82857143\n",
            " 0.91666667 0.97142857 0.94285714 0.94285714 0.85714286 0.97142857\n",
            " 0.94285714 0.88571429 0.91428571 0.88571429]\n",
            "Accuracy: 93.03% (4.48%)\n",
            "Execution Time: 11.09 seconds\n",
            "------------------------------\n",
            "--------- GradBoost on Ionosphere Dataset ---------\n",
            "[0.91666667 0.94285714 1.         0.91428571 0.97142857 0.88571429\n",
            " 0.88571429 1.         0.91428571 0.94285714 0.86111111 0.8\n",
            " 1.         1.         0.97142857 0.94285714 0.94285714 0.94285714\n",
            " 0.85714286 0.97142857 1.         0.97142857 0.85714286 0.88571429\n",
            " 0.94285714 0.97142857 0.94285714 0.91428571 0.94285714 0.94285714\n",
            " 0.91666667 0.94285714 0.91428571 0.91428571 0.85714286 0.94285714\n",
            " 1.         0.97142857 0.88571429 0.94285714 0.91666667 0.88571429\n",
            " 0.94285714 0.82857143 0.91428571 0.94285714 0.94285714 0.97142857\n",
            " 1.         0.97142857 0.91666667 0.94285714 0.94285714 1.\n",
            " 0.94285714 0.94285714 0.94285714 0.82857143 0.97142857 0.88571429\n",
            " 0.88888889 0.88571429 0.97142857 0.88571429 0.88571429 0.94285714\n",
            " 0.91428571 0.97142857 0.91428571 0.94285714 0.80555556 0.91428571\n",
            " 1.         0.85714286 0.94285714 0.91428571 0.97142857 0.88571429\n",
            " 1.         0.91428571 0.94444444 1.         0.94285714 0.8\n",
            " 0.85714286 0.97142857 0.91428571 1.         0.97142857 0.85714286\n",
            " 0.88888889 1.         1.         0.97142857 0.91428571 0.94285714\n",
            " 0.97142857 0.94285714 0.91428571 0.88571429]\n",
            "Accuracy: 93.06% (4.86%)\n",
            "Execution Time: 23.87 seconds\n",
            "------------------------------\n",
            "--------- CatBoost on Ionosphere Dataset ---------\n",
            "[0.91666667 0.94285714 1.         0.91428571 0.94285714 0.91428571\n",
            " 0.88571429 1.         0.88571429 0.94285714 0.86111111 0.88571429\n",
            " 1.         1.         0.97142857 0.97142857 0.91428571 0.94285714\n",
            " 0.82857143 0.97142857 1.         0.97142857 0.88571429 0.94285714\n",
            " 0.94285714 0.97142857 0.94285714 0.91428571 0.91428571 0.94285714\n",
            " 0.91666667 0.97142857 0.94285714 0.85714286 0.88571429 0.94285714\n",
            " 1.         0.94285714 0.88571429 0.97142857 0.91666667 0.94285714\n",
            " 0.97142857 0.8        0.91428571 0.94285714 0.94285714 0.97142857\n",
            " 1.         0.94285714 0.94444444 0.91428571 0.94285714 1.\n",
            " 0.94285714 0.94285714 0.97142857 0.85714286 0.94285714 0.88571429\n",
            " 0.91666667 0.91428571 0.94285714 0.88571429 0.94285714 0.94285714\n",
            " 0.91428571 0.97142857 0.94285714 0.94285714 0.83333333 1.\n",
            " 1.         0.88571429 0.94285714 0.88571429 0.91428571 0.91428571\n",
            " 1.         0.97142857 0.97222222 1.         0.94285714 0.82857143\n",
            " 0.85714286 0.94285714 0.94285714 1.         0.97142857 0.82857143\n",
            " 0.86111111 1.         1.         0.94285714 0.91428571 0.94285714\n",
            " 0.97142857 0.94285714 0.91428571 0.88571429]\n",
            "Accuracy: 93.45% (4.59%)\n",
            "Execution Time: 545.45 seconds\n",
            "------------------------------\n",
            "--------- LightGBM on Ionosphere Dataset ---------\n",
            "[0.91666667 0.94285714 1.         0.94285714 0.94285714 0.88571429\n",
            " 0.94285714 1.         0.85714286 0.94285714 0.91666667 0.85714286\n",
            " 1.         1.         0.97142857 0.97142857 0.91428571 0.94285714\n",
            " 0.85714286 0.97142857 1.         0.97142857 0.88571429 0.85714286\n",
            " 0.97142857 0.97142857 0.91428571 0.91428571 0.94285714 0.94285714\n",
            " 0.91666667 0.97142857 0.97142857 0.88571429 0.82857143 0.94285714\n",
            " 1.         0.94285714 0.88571429 0.97142857 0.91666667 0.94285714\n",
            " 0.97142857 0.8        0.91428571 0.94285714 0.94285714 0.97142857\n",
            " 1.         0.91428571 0.94444444 0.91428571 0.88571429 1.\n",
            " 0.94285714 0.94285714 0.97142857 0.88571429 0.97142857 0.91428571\n",
            " 0.97222222 0.91428571 0.97142857 0.88571429 0.94285714 0.91428571\n",
            " 0.91428571 0.97142857 0.94285714 0.94285714 0.86111111 1.\n",
            " 1.         0.91428571 0.94285714 0.91428571 0.91428571 0.91428571\n",
            " 0.97142857 0.97142857 0.97222222 1.         0.91428571 0.88571429\n",
            " 0.85714286 0.97142857 0.91428571 1.         0.97142857 0.88571429\n",
            " 0.86111111 1.         1.         0.94285714 0.91428571 0.97142857\n",
            " 0.97142857 0.94285714 0.91428571 0.88571429]\n",
            "Accuracy: 93.71% (4.41%)\n",
            "Execution Time: 5.44 seconds\n",
            "------------------------------\n",
            "--------- XGBoost on Ionosphere Dataset ---------\n",
            "[0.91666667 0.94285714 1.         0.91428571 0.94285714 0.91428571\n",
            " 0.88571429 1.         0.82857143 0.91428571 0.88888889 0.91428571\n",
            " 1.         1.         0.97142857 0.97142857 0.91428571 0.94285714\n",
            " 0.8        0.94285714 1.         0.97142857 0.88571429 0.91428571\n",
            " 0.94285714 0.91428571 0.97142857 0.91428571 0.91428571 0.88571429\n",
            " 0.88888889 0.97142857 0.97142857 0.88571429 0.8        0.94285714\n",
            " 1.         0.94285714 0.88571429 0.91428571 0.86111111 0.97142857\n",
            " 0.94285714 0.8        0.88571429 0.94285714 0.94285714 0.97142857\n",
            " 1.         0.91428571 0.91666667 0.91428571 0.88571429 0.97142857\n",
            " 0.94285714 0.88571429 0.97142857 0.88571429 0.97142857 0.88571429\n",
            " 0.83333333 0.91428571 0.97142857 0.88571429 0.91428571 0.94285714\n",
            " 0.91428571 0.91428571 0.97142857 0.94285714 0.83333333 0.91428571\n",
            " 1.         0.91428571 0.97142857 0.91428571 0.88571429 0.88571429\n",
            " 1.         0.97142857 0.97222222 1.         0.94285714 0.74285714\n",
            " 0.88571429 0.97142857 0.88571429 1.         0.97142857 0.82857143\n",
            " 0.83333333 0.97142857 0.91428571 0.94285714 0.85714286 0.97142857\n",
            " 0.97142857 0.94285714 0.91428571 0.82857143]\n",
            "Accuracy: 92.43% (5.31%)\n",
            "Execution Time: 8.40 seconds\n",
            "------------------------------\n"
          ]
        }
      ],
      "source": [
        "ionosphere_scores = []\n",
        "ionosphere_mean = []\n",
        "ionosphere_std = []\n",
        "model_names = []\n",
        "execution_times = []\n",
        "\n",
        "for name, clf in zip(names, classifiers):\n",
        "  start_time = time.time()\n",
        "  results = cross_val_score(clf, X, y, cv=rkf)\n",
        "  end_time = time.time()\n",
        "\n",
        "  ionosphere_scores.append(results)\n",
        "  ionosphere_mean.append(results.mean()*100)\n",
        "  ionosphere_std.append(results.std()*100)\n",
        "  model_names.append(name)\n",
        "  execution_time = end_time - start_time\n",
        "  execution_times.append(execution_time)\n",
        "\n",
        "  print(f'--------- {name} on Ionosphere Dataset ---------')\n",
        "  print(results)\n",
        "  print('Accuracy: %.2f%% (%.2f%%)' % (results.mean()*100, results.std()*100))\n",
        "  print(f'Execution Time: {execution_time:.2f} seconds')\n",
        "  print('------------------------------')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zCwmHNHDFs5j",
        "outputId": "6fba0c7e-efab-40a9-bb0f-d97e99f564e9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[93.02777777777777,\n",
              " 93.05555555555556,\n",
              " 93.4531746031746,\n",
              " 93.7063492063492,\n",
              " 92.43015873015872]"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ],
      "source": [
        "ionosphere_mean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PEK182-LFuYN",
        "outputId": "3ce8f338-57ac-4ff1-9e10-87a33e5362e0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[4.4762520399909125,\n",
              " 4.859171948822011,\n",
              " 4.589216591001476,\n",
              " 4.414603619768994,\n",
              " 5.314317716237489]"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ],
      "source": [
        "ionosphere_std"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "nJETohzo1-r8",
        "outputId": "a5d0c95a-77ab-46b2-f09d-f6c2c9759b4f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# algorithm comparison\n",
        "\n",
        "fig = plt.figure()\n",
        "fig.suptitle('Comparison between different Ionosphere scores')\n",
        "ax = fig.add_subplot(111)\n",
        "plt.ylim(0.3, 1)\n",
        "plt.boxplot(ionosphere_scores, showmeans=True)\n",
        "ax.set_xticklabels(model_names)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "id": "fo0Bqj0SMK6T"
      },
      "outputs": [],
      "source": [
        "Algo_results['Ionosphere'] = ionosphere_mean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "XrvHCsfxMcaI",
        "outputId": "6a71c12d-c3da-489e-f11e-c24add0cd594"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Names       Wine  Breast_Cancer      Sonar  Ionosphere\n",
              "0   AdaBoost  89.830065      95.800512  83.140476   93.027778\n",
              "1  GradBoost  95.071895      96.515772  84.185714   93.055556\n",
              "2   CatBoost  97.977124      97.087170  87.152381   93.453175\n",
              "3   LightGBM  97.421569      96.632992  88.123810   93.706349\n",
              "4    XGBoost  96.676471      96.429028  85.007143   92.430159"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-95fa7155-2a9a-4db1-a5e3-ab7e445c3494\" 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>Names</th>\n",
              "      <th>Wine</th>\n",
              "      <th>Breast_Cancer</th>\n",
              "      <th>Sonar</th>\n",
              "      <th>Ionosphere</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AdaBoost</td>\n",
              "      <td>89.830065</td>\n",
              "      <td>95.800512</td>\n",
              "      <td>83.140476</td>\n",
              "      <td>93.027778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GradBoost</td>\n",
              "      <td>95.071895</td>\n",
              "      <td>96.515772</td>\n",
              "      <td>84.185714</td>\n",
              "      <td>93.055556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CatBoost</td>\n",
              "      <td>97.977124</td>\n",
              "      <td>97.087170</td>\n",
              "      <td>87.152381</td>\n",
              "      <td>93.453175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LightGBM</td>\n",
              "      <td>97.421569</td>\n",
              "      <td>96.632992</td>\n",
              "      <td>88.123810</td>\n",
              "      <td>93.706349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>96.676471</td>\n",
              "      <td>96.429028</td>\n",
              "      <td>85.007143</td>\n",
              "      <td>92.430159</td>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-95fa7155-2a9a-4db1-a5e3-ab7e445c3494 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-95fa7155-2a9a-4db1-a5e3-ab7e445c3494');\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-4fc6a83e-ce96-4022-a8be-a87280a4f24a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4fc6a83e-ce96-4022-a8be-a87280a4f24a')\"\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",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "\n",
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              "\n",
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              "    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",
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              "\n",
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              "    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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              "    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-4fc6a83e-ce96-4022-a8be-a87280a4f24a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ],
      "source": [
        "Algo_results"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Algo_results_std['Ionosphere'] = ionosphere_std"
      ],
      "metadata": {
        "id": "c87kqFj_ycvN"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "id": "PEb7RggkDpYE"
      },
      "outputs": [],
      "source": [
        "Algo_time_results['Ionosphere'] = pd.Series(execution_times)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EM2SfsPt7_9i"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EDFLJEX_7_3e"
      },
      "source": [
        "# **(Unbalanced) Bupa**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZgymIqS_7_wq"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "id": "gTOBrsuz7-1D"
      },
      "outputs": [],
      "source": [
        "bupa_df = pd.read_csv('/content/drive/MyDrive/DatasetSeminario/Bupa/Bupa.data', header=None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rw3t4eya8XiH",
        "outputId": "6bc28858-45fe-4321-c59c-303e79210c07"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(345, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ],
      "source": [
        "bupa_df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "RGgy7sw38R6S",
        "outputId": "02788f83-0781-47a1-c2f6-20227b38013e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    0   1   2   3   4    5  6\n",
              "0  85  92  45  27  31  0.0  1\n",
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              "2  86  54  33  16  54  0.0  2\n",
              "3  91  78  34  24  36  0.0  2\n",
              "4  87  70  12  28  10  0.0  2"
            ],
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              "          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",
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              "<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",
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              "    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-c88074dc-eeec-4967-9063-f47594f364a3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ],
      "source": [
        "bupa_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TGr6Oz__8UG1",
        "outputId": "f4d9b1f6-f0ad-4719-d275-3dce4978058f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 345 entries, 0 to 344\n",
            "Data columns (total 7 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   0       345 non-null    int64  \n",
            " 1   1       345 non-null    int64  \n",
            " 2   2       345 non-null    int64  \n",
            " 3   3       345 non-null    int64  \n",
            " 4   4       345 non-null    int64  \n",
            " 5   5       345 non-null    float64\n",
            " 6   6       345 non-null    int64  \n",
            "dtypes: float64(1), int64(6)\n",
            "memory usage: 19.0 KB\n"
          ]
        }
      ],
      "source": [
        "bupa_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eaYnRrkf8n4c",
        "outputId": "2b2bc3b6-c960-4174-fedc-147a16dc3dc8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    26\n",
              "1    78\n",
              "2    67\n",
              "3    47\n",
              "4    94\n",
              "5    16\n",
              "6     2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ],
      "source": [
        "bupa_df.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tZigCKS_8u3E",
        "outputId": "b4dd8c36-d75e-424d-9b4b-d9d797e34812"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    0\n",
              "2    0\n",
              "3    0\n",
              "4    0\n",
              "5    0\n",
              "6    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ],
      "source": [
        "bupa_df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ShgSkjYb8238",
        "outputId": "4f5da604-70a4-4f1e-e82b-04cc4a9a612a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    200\n",
              "1    145\n",
              "Name: 6, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ],
      "source": [
        "# Unbalanced dataset\n",
        "\n",
        "bupa_df[6].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "id": "ZBUXN1kp9CFs"
      },
      "outputs": [],
      "source": [
        "X = bupa_df.iloc[:, :-1]\n",
        "y = bupa_df.iloc[:, -1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "pcqPfXCE9jAe",
        "outputId": "7503a5a4-41d3-44e0-d501-68aeb4364a9f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    0   1   2   3   4    5\n",
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              "1  85  64  59  32  23  0.0\n",
              "2  86  54  33  16  54  0.0\n",
              "3  91  78  34  24  36  0.0\n",
              "4  87  70  12  28  10  0.0"
            ],
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      width: 32px;\n",
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              "\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-6e6ce3c1-afe4-4e6a-9a68-f4e4d35ca69f 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-6e6ce3c1-afe4-4e6a-9a68-f4e4d35ca69f');\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",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-54bc7f02-41d7-4846-a9f5-dc39f451368f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-54bc7f02-41d7-4846-a9f5-dc39f451368f')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </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",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    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-54bc7f02-41d7-4846-a9f5-dc39f451368f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ],
      "source": [
        "X.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WxcLKUgo9kCc",
        "outputId": "a409286c-49ed-43dd-8b68-d2b77576fa29"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    2\n",
              "2    2\n",
              "3    2\n",
              "4    2\n",
              "Name: 6, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ],
      "source": [
        "y.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {
        "id": "x6zJOJjI9l3j"
      },
      "outputs": [],
      "source": [
        "# Since version 1.3.2 XGBoost needs target columns to start with 0 value\n",
        "\n",
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hD8SYMMs9qsl",
        "outputId": "bd8327bb-81b9-4c05-d19e-76e9fd3e15f8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------- AdaBoost on Bupa Dataset ---------\n",
            "[0.8        0.8        0.65714286 0.74285714 0.71428571 0.64705882\n",
            " 0.67647059 0.79411765 0.70588235 0.64705882 0.71428571 0.77142857\n",
            " 0.71428571 0.65714286 0.65714286 0.79411765 0.55882353 0.76470588\n",
            " 0.70588235 0.82352941 0.74285714 0.62857143 0.71428571 0.74285714\n",
            " 0.68571429 0.73529412 0.79411765 0.82352941 0.82352941 0.58823529\n",
            " 0.62857143 0.74285714 0.8        0.62857143 0.71428571 0.79411765\n",
            " 0.70588235 0.67647059 0.67647059 0.55882353 0.74285714 0.77142857\n",
            " 0.74285714 0.71428571 0.77142857 0.79411765 0.70588235 0.73529412\n",
            " 0.73529412 0.70588235 0.68571429 0.8        0.62857143 0.82857143\n",
            " 0.71428571 0.67647059 0.64705882 0.70588235 0.85294118 0.73529412\n",
            " 0.62857143 0.62857143 0.77142857 0.68571429 0.8        0.61764706\n",
            " 0.61764706 0.85294118 0.76470588 0.76470588 0.71428571 0.68571429\n",
            " 0.82857143 0.74285714 0.71428571 0.76470588 0.70588235 0.85294118\n",
            " 0.61764706 0.70588235 0.88571429 0.6        0.74285714 0.71428571\n",
            " 0.74285714 0.73529412 0.70588235 0.70588235 0.67647059 0.82352941\n",
            " 0.8        0.8        0.74285714 0.65714286 0.8        0.61764706\n",
            " 0.64705882 0.61764706 0.76470588 0.76470588]\n",
            "Accuracy: 72.25% (7.03%)\n",
            "Execution Time: 7.65 seconds\n",
            "------------------------------\n",
            "--------- GradBoost on Bupa Dataset ---------\n",
            "[0.68571429 0.77142857 0.71428571 0.68571429 0.68571429 0.73529412\n",
            " 0.76470588 0.76470588 0.73529412 0.67647059 0.71428571 0.68571429\n",
            " 0.71428571 0.62857143 0.71428571 0.82352941 0.64705882 0.67647059\n",
            " 0.70588235 0.88235294 0.74285714 0.65714286 0.8        0.8\n",
            " 0.65714286 0.73529412 0.67647059 0.73529412 0.73529412 0.73529412\n",
            " 0.71428571 0.71428571 0.77142857 0.65714286 0.74285714 0.73529412\n",
            " 0.79411765 0.64705882 0.58823529 0.5        0.71428571 0.82857143\n",
            " 0.62857143 0.74285714 0.77142857 0.79411765 0.76470588 0.64705882\n",
            " 0.73529412 0.58823529 0.74285714 0.74285714 0.6        0.74285714\n",
            " 0.71428571 0.79411765 0.67647059 0.73529412 0.73529412 0.79411765\n",
            " 0.74285714 0.68571429 0.82857143 0.68571429 0.8        0.73529412\n",
            " 0.70588235 0.85294118 0.76470588 0.82352941 0.65714286 0.65714286\n",
            " 0.82857143 0.77142857 0.77142857 0.76470588 0.73529412 0.73529412\n",
            " 0.70588235 0.79411765 0.8        0.65714286 0.62857143 0.8\n",
            " 0.62857143 0.70588235 0.67647059 0.79411765 0.67647059 0.82352941\n",
            " 0.71428571 0.74285714 0.71428571 0.6        0.82857143 0.58823529\n",
            " 0.70588235 0.70588235 0.79411765 0.73529412]\n",
            "Accuracy: 72.41% (6.67%)\n",
            "Execution Time: 7.72 seconds\n",
            "------------------------------\n",
            "--------- CatBoost on Bupa Dataset ---------\n",
            "[0.8        0.77142857 0.71428571 0.71428571 0.68571429 0.73529412\n",
            " 0.70588235 0.76470588 0.73529412 0.64705882 0.71428571 0.74285714\n",
            " 0.85714286 0.65714286 0.74285714 0.76470588 0.67647059 0.73529412\n",
            " 0.73529412 0.88235294 0.74285714 0.71428571 0.82857143 0.77142857\n",
            " 0.68571429 0.79411765 0.70588235 0.79411765 0.76470588 0.70588235\n",
            " 0.77142857 0.8        0.8        0.71428571 0.77142857 0.82352941\n",
            " 0.67647059 0.70588235 0.64705882 0.64705882 0.74285714 0.8\n",
            " 0.65714286 0.77142857 0.85714286 0.76470588 0.76470588 0.70588235\n",
            " 0.67647059 0.67647059 0.82857143 0.71428571 0.65714286 0.77142857\n",
            " 0.65714286 0.73529412 0.76470588 0.79411765 0.79411765 0.82352941\n",
            " 0.68571429 0.65714286 0.82857143 0.62857143 0.85714286 0.79411765\n",
            " 0.70588235 0.91176471 0.70588235 0.79411765 0.6        0.68571429\n",
            " 0.82857143 0.74285714 0.77142857 0.76470588 0.76470588 0.79411765\n",
            " 0.70588235 0.76470588 0.8        0.65714286 0.8        0.82857143\n",
            " 0.65714286 0.67647059 0.73529412 0.73529412 0.76470588 0.82352941\n",
            " 0.77142857 0.85714286 0.65714286 0.65714286 0.85714286 0.64705882\n",
            " 0.70588235 0.70588235 0.79411765 0.67647059]\n",
            "Accuracy: 74.40% (6.45%)\n",
            "Execution Time: 71.48 seconds\n",
            "------------------------------\n",
            "--------- LightGBM on Bupa Dataset ---------\n",
            "[0.77142857 0.77142857 0.68571429 0.71428571 0.62857143 0.70588235\n",
            " 0.64705882 0.85294118 0.76470588 0.70588235 0.6        0.68571429\n",
            " 0.77142857 0.68571429 0.71428571 0.73529412 0.61764706 0.70588235\n",
            " 0.67647059 0.76470588 0.71428571 0.65714286 0.74285714 0.71428571\n",
            " 0.77142857 0.73529412 0.61764706 0.67647059 0.76470588 0.67647059\n",
            " 0.8        0.77142857 0.77142857 0.71428571 0.65714286 0.73529412\n",
            " 0.70588235 0.67647059 0.67647059 0.61764706 0.77142857 0.8\n",
            " 0.77142857 0.74285714 0.8        0.73529412 0.73529412 0.70588235\n",
            " 0.64705882 0.67647059 0.71428571 0.65714286 0.62857143 0.74285714\n",
            " 0.71428571 0.67647059 0.76470588 0.82352941 0.73529412 0.85294118\n",
            " 0.54285714 0.71428571 0.8        0.65714286 0.68571429 0.70588235\n",
            " 0.70588235 0.73529412 0.70588235 0.79411765 0.62857143 0.65714286\n",
            " 0.88571429 0.74285714 0.65714286 0.82352941 0.67647059 0.76470588\n",
            " 0.73529412 0.73529412 0.8        0.62857143 0.68571429 0.74285714\n",
            " 0.6        0.70588235 0.67647059 0.73529412 0.73529412 0.79411765\n",
            " 0.77142857 0.82857143 0.62857143 0.65714286 0.74285714 0.67647059\n",
            " 0.70588235 0.67647059 0.79411765 0.76470588]\n",
            "Accuracy: 71.80% (6.22%)\n",
            "Execution Time: 1.48 seconds\n",
            "------------------------------\n",
            "--------- XGBoost on Bupa Dataset ---------\n",
            "[0.82857143 0.77142857 0.65714286 0.74285714 0.71428571 0.70588235\n",
            " 0.70588235 0.73529412 0.76470588 0.64705882 0.6        0.68571429\n",
            " 0.74285714 0.62857143 0.71428571 0.67647059 0.64705882 0.73529412\n",
            " 0.64705882 0.70588235 0.65714286 0.68571429 0.71428571 0.77142857\n",
            " 0.71428571 0.76470588 0.61764706 0.70588235 0.64705882 0.67647059\n",
            " 0.74285714 0.71428571 0.74285714 0.74285714 0.6        0.82352941\n",
            " 0.73529412 0.58823529 0.64705882 0.61764706 0.8        0.82857143\n",
            " 0.65714286 0.71428571 0.8        0.64705882 0.70588235 0.67647059\n",
            " 0.64705882 0.67647059 0.68571429 0.62857143 0.6        0.65714286\n",
            " 0.71428571 0.76470588 0.79411765 0.76470588 0.76470588 0.76470588\n",
            " 0.65714286 0.74285714 0.8        0.62857143 0.68571429 0.70588235\n",
            " 0.70588235 0.82352941 0.61764706 0.76470588 0.51428571 0.6\n",
            " 0.85714286 0.71428571 0.65714286 0.70588235 0.70588235 0.73529412\n",
            " 0.76470588 0.73529412 0.8        0.62857143 0.71428571 0.77142857\n",
            " 0.54285714 0.73529412 0.64705882 0.76470588 0.70588235 0.79411765\n",
            " 0.71428571 0.8        0.65714286 0.68571429 0.85714286 0.58823529\n",
            " 0.70588235 0.70588235 0.64705882 0.73529412]\n",
            "Accuracy: 70.58% (6.77%)\n",
            "Execution Time: 3.57 seconds\n",
            "------------------------------\n"
          ]
        }
      ],
      "source": [
        "bupa_scores = []\n",
        "bupa_mean = []\n",
        "bupa_std = []\n",
        "model_names = []\n",
        "execution_times = []\n",
        "\n",
        "for name, clf in zip(names, classifiers):\n",
        "  start_time = time.time()\n",
        "  results = cross_val_score(clf, X, y, cv=rkf)\n",
        "  end_time = time.time()\n",
        "\n",
        "  bupa_scores.append(results)\n",
        "  bupa_mean.append(results.mean()*100)\n",
        "  bupa_std.append(results.std()*100)\n",
        "  model_names.append(name)\n",
        "  execution_time = end_time - start_time\n",
        "  execution_times.append(execution_time)\n",
        "\n",
        "  print(f'--------- {name} on Bupa Dataset ---------')\n",
        "  print(results)\n",
        "  print('Accuracy: %.2f%% (%.2f%%)' % (results.mean()*100, results.std()*100))\n",
        "  print(f'Execution Time: {execution_time:.2f} seconds')\n",
        "  print('------------------------------')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6qxJTM3D-7nZ",
        "outputId": "2d26baae-a86b-444c-e8b5-6f92c08288a2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[72.25462184873949,\n",
              " 72.4109243697479,\n",
              " 74.40336134453781,\n",
              " 71.8016806722689,\n",
              " 70.57983193277309]"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ],
      "source": [
        "bupa_mean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4FjORnk--9w4",
        "outputId": "c5211727-4321-41ed-9552-4f3634c7a877"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[7.026728304847883,\n",
              " 6.671651803480113,\n",
              " 6.450640429588332,\n",
              " 6.215299205426823,\n",
              " 6.773062326505143]"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ],
      "source": [
        "bupa_std"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "0T_OcTxz2umM",
        "outputId": "aebc2511-4b9d-4c28-a261-28dab8f2beab"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# algorithm comparison\n",
        "\n",
        "fig = plt.figure()\n",
        "fig.suptitle('Comparison between different Bupa scores')\n",
        "ax = fig.add_subplot(111)\n",
        "plt.ylim(0.3, 1)\n",
        "plt.boxplot(bupa_scores, showmeans=True)\n",
        "ax.set_xticklabels(model_names)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "metadata": {
        "id": "qhCCuXSE_QTD"
      },
      "outputs": [],
      "source": [
        "Algo_results['Bupa'] = bupa_mean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "pJ4oJxNu_T0X",
        "outputId": "95329f0f-e1f5-4b41-8b0d-7112fec32f18"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Names       Wine  Breast_Cancer      Sonar  Ionosphere       Bupa\n",
              "0   AdaBoost  89.830065      95.800512  83.140476   93.027778  72.254622\n",
              "1  GradBoost  95.071895      96.515772  84.185714   93.055556  72.410924\n",
              "2   CatBoost  97.977124      97.087170  87.152381   93.453175  74.403361\n",
              "3   LightGBM  97.421569      96.632992  88.123810   93.706349  71.801681\n",
              "4    XGBoost  96.676471      96.429028  85.007143   92.430159  70.579832"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9dde1546-6355-45ec-8e7f-04906551a87a\" 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>Names</th>\n",
              "      <th>Wine</th>\n",
              "      <th>Breast_Cancer</th>\n",
              "      <th>Sonar</th>\n",
              "      <th>Ionosphere</th>\n",
              "      <th>Bupa</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AdaBoost</td>\n",
              "      <td>89.830065</td>\n",
              "      <td>95.800512</td>\n",
              "      <td>83.140476</td>\n",
              "      <td>93.027778</td>\n",
              "      <td>72.254622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GradBoost</td>\n",
              "      <td>95.071895</td>\n",
              "      <td>96.515772</td>\n",
              "      <td>84.185714</td>\n",
              "      <td>93.055556</td>\n",
              "      <td>72.410924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CatBoost</td>\n",
              "      <td>97.977124</td>\n",
              "      <td>97.087170</td>\n",
              "      <td>87.152381</td>\n",
              "      <td>93.453175</td>\n",
              "      <td>74.403361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LightGBM</td>\n",
              "      <td>97.421569</td>\n",
              "      <td>96.632992</td>\n",
              "      <td>88.123810</td>\n",
              "      <td>93.706349</td>\n",
              "      <td>71.801681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>96.676471</td>\n",
              "      <td>96.429028</td>\n",
              "      <td>85.007143</td>\n",
              "      <td>92.430159</td>\n",
              "      <td>70.579832</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9dde1546-6355-45ec-8e7f-04906551a87a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9dde1546-6355-45ec-8e7f-04906551a87a 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-9dde1546-6355-45ec-8e7f-04906551a87a');\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-cccedf0a-6cd4-4022-b83a-8c26a220a8fd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cccedf0a-6cd4-4022-b83a-8c26a220a8fd')\"\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-cccedf0a-6cd4-4022-b83a-8c26a220a8fd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ],
      "source": [
        "Algo_results"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Algo_results_std['Bupa'] = bupa_std"
      ],
      "metadata": {
        "id": "qmY_Y7XXyjao"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "metadata": {
        "id": "gkZ0Bl5ODpYG"
      },
      "outputs": [],
      "source": [
        "Algo_time_results['Bupa'] = pd.Series(execution_times)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uaPWZXnE8OVw"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qE054BbS8OIJ"
      },
      "source": [
        "# **(Unbalanced) Pima**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0wAdeR3l8OE1"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {
        "id": "v0D6PiaS6unA"
      },
      "outputs": [],
      "source": [
        "pima_df = pd.read_csv('/content/drive/MyDrive/DatasetSeminario/Pima/Diabetes.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8FVGKn9T8oQa",
        "outputId": "1fd30054-ca38-4b78-8d3a-dbdb67a237e5"
      },
      "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": [
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              "  <div id=\"df-3a9a7ab1-7509-4801-918c-093e2cda0101\" class=\"colab-df-container\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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-3a9a7ab1-7509-4801-918c-093e2cda0101 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-3a9a7ab1-7509-4801-918c-093e2cda0101');\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",
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              "\n",
              "\n",
              "<div id=\"df-03f494d9-2708-4163-a738-66fbbd527eb5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-03f494d9-2708-4163-a738-66fbbd527eb5')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
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              "    </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",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
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              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "        document.querySelector('#df-03f494d9-2708-4163-a738-66fbbd527eb5 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ],
      "source": [
        "pima_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1u04WXBl87wb",
        "outputId": "fc296250-efd6-4b9f-f2b1-d845752a36bc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(768, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ],
      "source": [
        "pima_df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 117,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rufrOK0T9Gl2",
        "outputId": "af020426-99df-4fed-b797-71cb33d85e8e"
      },
      "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"
          ]
        }
      ],
      "source": [
        "pima_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9ptJ30jJ9IlD",
        "outputId": "68f6b362-b59b-4f7d-e488-6875fbfa5cf3"
      },
      "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": 118
        }
      ],
      "source": [
        "pima_df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 119,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e3Yof4IC_PRD",
        "outputId": "eb08730c-8922-45f3-db8a-d09a75a09d7d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                  17\n",
              "Glucose                     136\n",
              "BloodPressure                47\n",
              "SkinThickness                51\n",
              "Insulin                     186\n",
              "BMI                         248\n",
              "DiabetesPedigreeFunction    517\n",
              "Age                          52\n",
              "Outcome                       2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ],
      "source": [
        "pima_df.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q7PpLSxS_U7D",
        "outputId": "4d8f5685-d5ce-477a-fe08-0c926bdbe0c7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    500\n",
              "1    268\n",
              "Name: Outcome, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ],
      "source": [
        "# It is a unbalanced dataset\n",
        "\n",
        "pima_df['Outcome'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {
        "id": "08el-PRF_hrl"
      },
      "outputs": [],
      "source": [
        "X = pima_df.iloc[:, :-1]\n",
        "y = pima_df.iloc[:, -1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 122,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "iw4P1s3DAHun",
        "outputId": "00deb831-1044-4773-be8d-4e1795e8b3af"
      },
      "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  \n",
              "0                     0.627   50  \n",
              "1                     0.351   31  \n",
              "2                     0.672   32  \n",
              "3                     0.167   21  \n",
              "4                     2.288   33  "
            ],
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              "      buttonEl.style.display =\n",
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              "        const element = document.querySelector('#df-f5e90a12-1d7b-4d83-bec1-788869cafe83');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "        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",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ],
      "source": [
        "X.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 123,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KggzEPYuAIyG",
        "outputId": "f0c04575-a814-4434-8fd0-5b100e7fe243"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    0\n",
              "2    1\n",
              "3    0\n",
              "4    1\n",
              "Name: Outcome, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ],
      "source": [
        "y.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 124,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I4CT9myEALFN",
        "outputId": "a4ccbae2-40e6-4dc1-ee4c-300bf72c0b15"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------- AdaBoost on Pima Dataset ---------\n",
            "[0.75324675 0.77922078 0.71428571 0.76623377 0.80519481 0.7012987\n",
            " 0.80519481 0.74025974 0.67105263 0.84210526 0.79220779 0.76623377\n",
            " 0.76623377 0.83116883 0.76623377 0.71428571 0.67532468 0.75324675\n",
            " 0.69736842 0.71052632 0.72727273 0.79220779 0.77922078 0.77922078\n",
            " 0.67532468 0.68831169 0.7012987  0.87012987 0.77631579 0.82894737\n",
            " 0.71428571 0.76623377 0.81818182 0.67532468 0.68831169 0.75324675\n",
            " 0.74025974 0.75324675 0.71052632 0.81578947 0.76623377 0.74025974\n",
            " 0.77922078 0.67532468 0.75324675 0.68831169 0.75324675 0.72727273\n",
            " 0.77631579 0.77631579 0.75324675 0.76623377 0.75324675 0.72727273\n",
            " 0.72727273 0.72727273 0.75324675 0.76623377 0.77631579 0.73684211\n",
            " 0.72727273 0.75324675 0.64935065 0.83116883 0.71428571 0.80519481\n",
            " 0.75324675 0.67532468 0.73684211 0.84210526 0.79220779 0.67532468\n",
            " 0.72727273 0.79220779 0.74025974 0.81818182 0.7012987  0.77922078\n",
            " 0.71052632 0.78947368 0.76623377 0.79220779 0.87012987 0.80519481\n",
            " 0.66233766 0.68831169 0.68831169 0.74025974 0.76315789 0.67105263\n",
            " 0.87012987 0.75324675 0.83116883 0.83116883 0.76623377 0.67532468\n",
            " 0.67532468 0.72727273 0.76315789 0.73684211]\n",
            "Accuracy: 75.12% (5.08%)\n",
            "Execution Time: 9.10 seconds\n",
            "------------------------------\n",
            "--------- GradBoost on Pima Dataset ---------\n",
            "[0.72727273 0.77922078 0.74025974 0.85714286 0.83116883 0.72727273\n",
            " 0.81818182 0.74025974 0.65789474 0.78947368 0.79220779 0.81818182\n",
            " 0.72727273 0.75324675 0.81818182 0.79220779 0.63636364 0.72727273\n",
            " 0.67105263 0.77631579 0.74025974 0.76623377 0.76623377 0.76623377\n",
            " 0.71428571 0.75324675 0.72727273 0.83116883 0.78947368 0.85526316\n",
            " 0.72727273 0.79220779 0.80519481 0.67532468 0.7012987  0.76623377\n",
            " 0.71428571 0.79220779 0.73684211 0.80263158 0.76623377 0.77922078\n",
            " 0.76623377 0.7012987  0.7012987  0.76623377 0.74025974 0.75324675\n",
            " 0.75       0.78947368 0.79220779 0.77922078 0.76623377 0.76623377\n",
            " 0.74025974 0.76623377 0.74025974 0.81818182 0.84210526 0.71052632\n",
            " 0.74025974 0.76623377 0.74025974 0.81818182 0.76623377 0.79220779\n",
            " 0.77922078 0.67532468 0.73684211 0.82894737 0.84415584 0.76623377\n",
            " 0.72727273 0.81818182 0.77922078 0.81818182 0.74025974 0.77922078\n",
            " 0.68421053 0.78947368 0.71428571 0.81818182 0.81818182 0.81818182\n",
            " 0.71428571 0.74025974 0.71428571 0.72727273 0.72368421 0.71052632\n",
            " 0.8961039  0.77922078 0.79220779 0.81818182 0.75324675 0.64935065\n",
            " 0.7012987  0.75324675 0.75       0.76315789]\n",
            "Accuracy: 76.18% (4.81%)\n",
            "Execution Time: 14.60 seconds\n",
            "------------------------------\n",
            "--------- CatBoost on Pima Dataset ---------\n",
            "[0.7012987  0.77922078 0.76623377 0.87012987 0.81818182 0.72727273\n",
            " 0.83116883 0.74025974 0.68421053 0.81578947 0.80519481 0.81818182\n",
            " 0.71428571 0.80519481 0.79220779 0.7012987  0.68831169 0.74025974\n",
            " 0.69736842 0.80263158 0.72727273 0.74025974 0.72727273 0.74025974\n",
            " 0.71428571 0.74025974 0.71428571 0.87012987 0.77631579 0.81578947\n",
            " 0.74025974 0.79220779 0.80519481 0.66233766 0.76623377 0.75324675\n",
            " 0.71428571 0.81818182 0.73684211 0.86842105 0.75324675 0.77922078\n",
            " 0.72727273 0.71428571 0.77922078 0.74025974 0.80519481 0.77922078\n",
            " 0.73684211 0.81578947 0.81818182 0.74025974 0.72727273 0.74025974\n",
            " 0.77922078 0.75324675 0.76623377 0.77922078 0.81578947 0.71052632\n",
            " 0.74025974 0.75324675 0.79220779 0.83116883 0.75324675 0.75324675\n",
            " 0.77922078 0.68831169 0.68421053 0.85526316 0.80519481 0.71428571\n",
            " 0.72727273 0.84415584 0.77922078 0.77922078 0.72727273 0.80519481\n",
            " 0.72368421 0.77631579 0.79220779 0.81818182 0.84415584 0.83116883\n",
            " 0.64935065 0.75324675 0.74025974 0.74025974 0.73684211 0.69736842\n",
            " 0.87012987 0.79220779 0.77922078 0.81818182 0.74025974 0.68831169\n",
            " 0.66233766 0.75324675 0.72368421 0.72368421]\n",
            "Accuracy: 76.25% (4.99%)\n",
            "Execution Time: 123.90 seconds\n",
            "------------------------------\n",
            "--------- LightGBM on Pima Dataset ---------\n",
            "[0.71428571 0.72727273 0.67532468 0.80519481 0.80519481 0.66233766\n",
            " 0.77922078 0.71428571 0.68421053 0.78947368 0.79220779 0.75324675\n",
            " 0.67532468 0.77922078 0.84415584 0.72727273 0.58441558 0.71428571\n",
            " 0.63157895 0.76315789 0.72727273 0.74025974 0.71428571 0.76623377\n",
            " 0.63636364 0.72727273 0.75324675 0.81818182 0.73684211 0.78947368\n",
            " 0.72727273 0.77922078 0.74025974 0.63636364 0.76623377 0.77922078\n",
            " 0.74025974 0.84415584 0.73684211 0.84210526 0.7012987  0.79220779\n",
            " 0.63636364 0.72727273 0.72727273 0.72727273 0.84415584 0.72727273\n",
            " 0.73684211 0.77631579 0.80519481 0.68831169 0.76623377 0.68831169\n",
            " 0.71428571 0.72727273 0.75324675 0.74025974 0.75       0.75\n",
            " 0.66233766 0.74025974 0.71428571 0.81818182 0.76623377 0.71428571\n",
            " 0.77922078 0.68831169 0.71052632 0.81578947 0.75324675 0.68831169\n",
            " 0.71428571 0.80519481 0.74025974 0.75324675 0.74025974 0.75324675\n",
            " 0.68421053 0.78947368 0.76623377 0.81818182 0.81818182 0.74025974\n",
            " 0.64935065 0.67532468 0.71428571 0.76623377 0.76315789 0.67105263\n",
            " 0.87012987 0.74025974 0.77922078 0.74025974 0.72727273 0.67532468\n",
            " 0.66233766 0.74025974 0.71052632 0.72368421]\n",
            "Accuracy: 73.99% (5.30%)\n",
            "Execution Time: 3.41 seconds\n",
            "------------------------------\n",
            "--------- XGBoost on Pima Dataset ---------\n",
            "[0.72727273 0.72727273 0.7012987  0.80519481 0.77922078 0.66233766\n",
            " 0.80519481 0.75324675 0.67105263 0.77631579 0.79220779 0.76623377\n",
            " 0.72727273 0.74025974 0.81818182 0.76623377 0.63636364 0.7012987\n",
            " 0.65789474 0.76315789 0.72727273 0.71428571 0.74025974 0.80519481\n",
            " 0.67532468 0.72727273 0.75324675 0.79220779 0.73684211 0.75\n",
            " 0.7012987  0.75324675 0.71428571 0.62337662 0.72727273 0.75324675\n",
            " 0.75324675 0.81818182 0.75       0.84210526 0.7012987  0.71428571\n",
            " 0.64935065 0.7012987  0.71428571 0.68831169 0.79220779 0.75324675\n",
            " 0.72368421 0.78947368 0.80519481 0.72727273 0.71428571 0.72727273\n",
            " 0.75324675 0.7012987  0.72727273 0.76623377 0.78947368 0.73684211\n",
            " 0.66233766 0.74025974 0.7012987  0.79220779 0.75324675 0.72727273\n",
            " 0.77922078 0.66233766 0.68421053 0.77631579 0.77922078 0.72727273\n",
            " 0.68831169 0.84415584 0.7012987  0.76623377 0.74025974 0.75324675\n",
            " 0.73684211 0.77631579 0.79220779 0.80519481 0.81818182 0.72727273\n",
            " 0.64935065 0.68831169 0.7012987  0.75324675 0.73684211 0.72368421\n",
            " 0.84415584 0.7012987  0.76623377 0.75324675 0.75324675 0.64935065\n",
            " 0.67532468 0.75324675 0.73684211 0.71052632]\n",
            "Accuracy: 73.84% (4.74%)\n",
            "Execution Time: 8.65 seconds\n",
            "------------------------------\n"
          ]
        }
      ],
      "source": [
        "pima_scores = []\n",
        "pima_mean = []\n",
        "pima_std = []\n",
        "model_names = []\n",
        "execution_times = []\n",
        "\n",
        "for name, clf in zip(names, classifiers):\n",
        "  start_time = time.time()\n",
        "  results = cross_val_score(clf, X, y, cv=rkf)\n",
        "  end_time = time.time()\n",
        "\n",
        "  pima_scores.append(results)\n",
        "  pima_mean.append(results.mean()*100)\n",
        "  pima_std.append(results.std()*100)\n",
        "  model_names.append(name)\n",
        "  execution_time = end_time - start_time\n",
        "  execution_times.append(execution_time)\n",
        "\n",
        "  print(f'--------- {name} on Pima Dataset ---------')\n",
        "  print(results)\n",
        "  print('Accuracy: %.2f%% (%.2f%%)' % (results.mean()*100, results.std()*100))\n",
        "  print(f'Execution Time: {execution_time:.2f} seconds')\n",
        "  print('------------------------------')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 125,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gLhEYTIGCUkM",
        "outputId": "9f11a466-d963-4251-dd6a-b02ba8ad5295"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[75.11859193438143,\n",
              " 76.18386876281612,\n",
              " 76.24931647300069,\n",
              " 73.98513328776488,\n",
              " 73.84244702665757]"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ],
      "source": [
        "pima_mean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 126,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fxRpG7ImCnwE",
        "outputId": "58d3f02f-8991-486b-ae27-fcb81d2eed04"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[5.077770573084834,\n",
              " 4.805911871661041,\n",
              " 4.987577768282925,\n",
              " 5.301428805296274,\n",
              " 4.741009133635159]"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ],
      "source": [
        "pima_std"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 127,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "mje4vBW43JG7",
        "outputId": "d6db91c7-fa6d-4a3a-8699-dd9978f020fe"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# algorithm comparison\n",
        "\n",
        "fig = plt.figure()\n",
        "fig.suptitle('Comparison between different Pima scores')\n",
        "ax = fig.add_subplot(111)\n",
        "plt.ylim(0.3, 1)\n",
        "plt.boxplot(pima_scores, showmeans=True)\n",
        "ax.set_xticklabels(model_names)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 128,
      "metadata": {
        "id": "QpOXO8vUCRpb"
      },
      "outputs": [],
      "source": [
        "Algo_results['Pima'] = pima_mean"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Algo_results_std['Pima'] = pima_std"
      ],
      "metadata": {
        "id": "aNRQdvYeyozS"
      },
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 130,
      "metadata": {
        "id": "xfZmYQBjDpYI"
      },
      "outputs": [],
      "source": [
        "Algo_time_results['Pima'] = pd.Series(execution_times)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g46GU-iICvoq"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fxa2x7XtCvij"
      },
      "source": [
        "# **(Unbalanced) Heart**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-P_hqPQGCvb5"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 131,
      "metadata": {
        "id": "a4EKbE7NCuqY"
      },
      "outputs": [],
      "source": [
        "heart_df = pd.read_csv('/content/drive/MyDrive/DatasetSeminario/Heart/Heart.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 132,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "z1vwoJd6HGFw",
        "outputId": "1bf67828-6519-4aa9-bf34-487e0c1d71d0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "0  70.0  1.0  4.0     130.0  322.0  0.0      2.0    109.0    0.0      2.4   \n",
              "1  67.0  0.0  3.0     115.0  564.0  0.0      2.0    160.0    0.0      1.6   \n",
              "2  57.0  1.0  2.0     124.0  261.0  0.0      0.0    141.0    0.0      0.3   \n",
              "3  64.0  1.0  4.0     128.0  263.0  0.0      0.0    105.0    1.0      0.2   \n",
              "4  74.0  0.0  2.0     120.0  269.0  0.0      2.0    121.0    1.0      0.2   \n",
              "\n",
              "   slope   ca  thal  presence  \n",
              "0    2.0  3.0   3.0         2  \n",
              "1    2.0  0.0   7.0         1  \n",
              "2    1.0  0.0   7.0         2  \n",
              "3    2.0  1.0   7.0         1  \n",
              "4    1.0  1.0   3.0         1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-793a2654-5b6e-48f1-8e82-888bcb76937e\" 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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>presence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>70.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>322.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>109.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>564.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>57.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>124.0</td>\n",
              "      <td>261.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>64.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>263.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>105.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>74.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>269.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ],
      "source": [
        "heart_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 133,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NySHrvriHIft",
        "outputId": "73f95d5f-8706-4c27-a9de-498766c5e4e1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(270, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ],
      "source": [
        "heart_df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 134,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "67Tux5-WHMUd",
        "outputId": "ee77107b-9afa-4f1e-ed5f-d3af32358b2c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 270 entries, 0 to 269\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       270 non-null    float64\n",
            " 1   sex       270 non-null    float64\n",
            " 2   cp        270 non-null    float64\n",
            " 3   trestbps  270 non-null    float64\n",
            " 4   chol      270 non-null    float64\n",
            " 5   fbs       270 non-null    float64\n",
            " 6   restecg   270 non-null    float64\n",
            " 7   thalach   270 non-null    float64\n",
            " 8   exang     270 non-null    float64\n",
            " 9   oldpeak   270 non-null    float64\n",
            " 10  slope     270 non-null    float64\n",
            " 11  ca        270 non-null    float64\n",
            " 12  thal      270 non-null    float64\n",
            " 13  presence  270 non-null    int64  \n",
            "dtypes: float64(13), int64(1)\n",
            "memory usage: 29.7 KB\n"
          ]
        }
      ],
      "source": [
        "heart_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 135,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AV15hP78HPmd",
        "outputId": "82484bc1-39b7-4a13-a68c-19d70c5b58fd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         0\n",
              "sex         0\n",
              "cp          0\n",
              "trestbps    0\n",
              "chol        0\n",
              "fbs         0\n",
              "restecg     0\n",
              "thalach     0\n",
              "exang       0\n",
              "oldpeak     0\n",
              "slope       0\n",
              "ca          0\n",
              "thal        0\n",
              "presence    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ],
      "source": [
        "heart_df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 136,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S8qI9ZrJKzoF",
        "outputId": "ed240e9b-fa26-4bc6-c0b0-fa74c63de1ee"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    150\n",
              "2    120\n",
              "Name: presence, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ],
      "source": [
        "# Almost balanced dataset\n",
        "\n",
        "heart_df['presence'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 137,
      "metadata": {
        "id": "bvq_IMi9K8al"
      },
      "outputs": [],
      "source": [
        "X = heart_df.iloc[:, :-1]\n",
        "y = heart_df.iloc[:, -1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 138,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "CDviVZ3iLSsf",
        "outputId": "d5df8795-eec8-4871-c4ab-3a0ab760e5a6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "0  70.0  1.0  4.0     130.0  322.0  0.0      2.0    109.0    0.0      2.4   \n",
              "1  67.0  0.0  3.0     115.0  564.0  0.0      2.0    160.0    0.0      1.6   \n",
              "2  57.0  1.0  2.0     124.0  261.0  0.0      0.0    141.0    0.0      0.3   \n",
              "3  64.0  1.0  4.0     128.0  263.0  0.0      0.0    105.0    1.0      0.2   \n",
              "4  74.0  0.0  2.0     120.0  269.0  0.0      2.0    121.0    1.0      0.2   \n",
              "\n",
              "   slope   ca  thal  \n",
              "0    2.0  3.0   3.0  \n",
              "1    2.0  0.0   7.0  \n",
              "2    1.0  0.0   7.0  \n",
              "3    2.0  1.0   7.0  \n",
              "4    1.0  1.0   3.0  "
            ],
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              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>261.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>64.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>263.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>105.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>74.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>269.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
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              "\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ],
      "source": [
        "X.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 139,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NoDhUUSQLoMN",
        "outputId": "9b4f118b-1107-4c9a-dc6b-ebd13e6539c6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2\n",
              "1    1\n",
              "2    2\n",
              "3    1\n",
              "4    1\n",
              "Name: presence, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ],
      "source": [
        "y.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 140,
      "metadata": {
        "id": "lB7hCzyWTb_9"
      },
      "outputs": [],
      "source": [
        "# Since version 1.3.2 XGBoost needs target columns to start with 0 value\n",
        "\n",
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 141,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vQnWtQsHTdsu",
        "outputId": "fb8d6acf-d858-4a63-df48-192cd0279f00"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       1, 0, 0, 0, 0, 1])"
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          },
          "metadata": {},
          "execution_count": 141
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      ],
      "source": [
        "y"
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    },
    {
      "cell_type": "code",
      "execution_count": 142,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X3TRFpCWLput",
        "outputId": "b3af4575-7927-4468-c514-daca6b4811c0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------- AdaBoost on Heart Dataset ---------\n",
            "[0.77777778 0.88888889 0.77777778 0.77777778 0.74074074 0.77777778\n",
            " 0.77777778 0.81481481 0.81481481 0.7037037  0.77777778 0.92592593\n",
            " 0.81481481 0.77777778 0.74074074 0.74074074 0.74074074 0.81481481\n",
            " 0.85185185 0.85185185 0.74074074 0.74074074 0.7037037  0.7037037\n",
            " 0.85185185 0.77777778 0.74074074 0.81481481 0.85185185 0.85185185\n",
            " 0.81481481 0.7037037  0.81481481 0.81481481 0.88888889 0.74074074\n",
            " 0.81481481 0.77777778 0.81481481 0.81481481 0.7037037  0.77777778\n",
            " 0.81481481 0.88888889 0.74074074 0.77777778 0.77777778 0.88888889\n",
            " 0.88888889 0.88888889 0.85185185 0.66666667 0.77777778 0.81481481\n",
            " 0.74074074 0.81481481 0.77777778 0.81481481 0.88888889 0.81481481\n",
            " 0.77777778 0.92592593 0.85185185 0.81481481 0.81481481 0.77777778\n",
            " 0.77777778 0.92592593 0.77777778 0.66666667 0.88888889 0.81481481\n",
            " 0.85185185 0.66666667 0.81481481 0.81481481 0.88888889 0.77777778\n",
            " 0.77777778 0.81481481 0.81481481 0.7037037  0.81481481 0.85185185\n",
            " 0.81481481 0.74074074 0.7037037  0.74074074 0.85185185 0.81481481\n",
            " 0.81481481 0.85185185 0.66666667 0.7037037  0.88888889 0.7037037\n",
            " 0.81481481 0.81481481 0.88888889 0.81481481]\n",
            "Accuracy: 79.81% (6.14%)\n",
            "Execution Time: 7.60 seconds\n",
            "------------------------------\n",
            "--------- GradBoost on Heart Dataset ---------\n",
            "[0.81481481 0.88888889 0.66666667 0.7037037  0.81481481 0.81481481\n",
            " 0.88888889 0.81481481 0.77777778 0.7037037  0.77777778 0.77777778\n",
            " 0.74074074 0.77777778 0.81481481 0.66666667 0.88888889 0.88888889\n",
            " 0.81481481 0.81481481 0.74074074 0.81481481 0.77777778 0.74074074\n",
            " 0.85185185 0.77777778 0.85185185 0.92592593 0.81481481 0.88888889\n",
            " 0.81481481 0.7037037  0.77777778 0.85185185 0.92592593 0.81481481\n",
            " 0.77777778 0.74074074 0.74074074 0.77777778 0.77777778 0.85185185\n",
            " 0.74074074 0.85185185 0.81481481 0.74074074 0.81481481 0.85185185\n",
            " 0.85185185 0.88888889 0.88888889 0.85185185 0.81481481 0.74074074\n",
            " 0.77777778 0.88888889 0.77777778 0.74074074 0.81481481 0.74074074\n",
            " 0.85185185 0.85185185 0.88888889 0.74074074 0.74074074 0.92592593\n",
            " 0.81481481 0.85185185 0.62962963 0.59259259 0.81481481 0.85185185\n",
            " 0.88888889 0.59259259 0.96296296 0.85185185 0.74074074 0.77777778\n",
            " 0.85185185 0.85185185 0.81481481 0.74074074 0.74074074 0.77777778\n",
            " 0.77777778 0.7037037  0.77777778 0.77777778 0.85185185 0.88888889\n",
            " 0.77777778 0.85185185 0.7037037  0.74074074 0.88888889 0.66666667\n",
            " 0.85185185 0.88888889 0.85185185 0.74074074]\n",
            "Accuracy: 80.07% (7.18%)\n",
            "Execution Time: 7.70 seconds\n",
            "------------------------------\n",
            "--------- CatBoost on Heart Dataset ---------\n",
            "[0.85185185 0.92592593 0.7037037  0.74074074 0.85185185 0.88888889\n",
            " 0.88888889 0.85185185 0.85185185 0.7037037  0.81481481 0.81481481\n",
            " 0.77777778 0.77777778 0.81481481 0.7037037  0.88888889 0.81481481\n",
            " 0.92592593 0.85185185 0.74074074 0.74074074 0.77777778 0.74074074\n",
            " 0.85185185 0.77777778 0.81481481 0.85185185 0.92592593 0.85185185\n",
            " 0.85185185 0.74074074 0.88888889 0.85185185 0.92592593 0.85185185\n",
            " 0.85185185 0.74074074 0.85185185 0.77777778 0.81481481 0.85185185\n",
            " 0.7037037  0.81481481 0.74074074 0.81481481 0.88888889 0.92592593\n",
            " 0.81481481 0.88888889 0.92592593 0.77777778 0.77777778 0.74074074\n",
            " 0.81481481 0.81481481 0.81481481 0.81481481 0.85185185 0.81481481\n",
            " 0.88888889 0.88888889 0.88888889 0.77777778 0.81481481 0.88888889\n",
            " 0.77777778 0.85185185 0.62962963 0.62962963 0.88888889 0.81481481\n",
            " 0.88888889 0.66666667 0.88888889 0.81481481 0.77777778 0.81481481\n",
            " 0.81481481 0.81481481 0.77777778 0.74074074 0.88888889 0.77777778\n",
            " 0.85185185 0.81481481 0.81481481 0.7037037  0.88888889 0.92592593\n",
            " 0.81481481 0.92592593 0.7037037  0.74074074 0.88888889 0.74074074\n",
            " 0.88888889 0.88888889 0.85185185 0.77777778]\n",
            "Accuracy: 81.89% (6.81%)\n",
            "Execution Time: 79.37 seconds\n",
            "------------------------------\n",
            "--------- LightGBM on Heart Dataset ---------\n",
            "[0.85185185 0.92592593 0.74074074 0.7037037  0.85185185 0.88888889\n",
            " 0.88888889 0.85185185 0.77777778 0.7037037  0.7037037  0.77777778\n",
            " 0.7037037  0.85185185 0.77777778 0.7037037  0.88888889 0.88888889\n",
            " 0.92592593 0.88888889 0.66666667 0.74074074 0.81481481 0.7037037\n",
            " 0.85185185 0.77777778 0.81481481 0.88888889 0.81481481 0.88888889\n",
            " 0.85185185 0.74074074 0.81481481 0.85185185 0.88888889 0.77777778\n",
            " 0.81481481 0.74074074 0.77777778 0.7037037  0.66666667 0.85185185\n",
            " 0.7037037  0.88888889 0.77777778 0.77777778 0.81481481 0.85185185\n",
            " 0.85185185 0.88888889 0.85185185 0.85185185 0.74074074 0.77777778\n",
            " 0.77777778 0.77777778 0.74074074 0.74074074 0.88888889 0.85185185\n",
            " 0.88888889 0.92592593 0.88888889 0.85185185 0.7037037  0.88888889\n",
            " 0.81481481 0.85185185 0.7037037  0.62962963 0.85185185 0.81481481\n",
            " 0.88888889 0.62962963 0.88888889 0.85185185 0.77777778 0.81481481\n",
            " 0.85185185 0.81481481 0.81481481 0.77777778 0.77777778 0.77777778\n",
            " 0.81481481 0.74074074 0.81481481 0.77777778 0.85185185 0.92592593\n",
            " 0.88888889 0.88888889 0.74074074 0.74074074 0.85185185 0.66666667\n",
            " 0.92592593 0.88888889 0.81481481 0.77777778]\n",
            "Accuracy: 80.89% (7.25%)\n",
            "Execution Time: 1.34 seconds\n",
            "------------------------------\n",
            "--------- XGBoost on Heart Dataset ---------\n",
            "[0.85185185 0.85185185 0.7037037  0.66666667 0.85185185 0.81481481\n",
            " 0.88888889 0.85185185 0.85185185 0.7037037  0.81481481 0.81481481\n",
            " 0.7037037  0.81481481 0.81481481 0.7037037  0.92592593 0.92592593\n",
            " 0.85185185 0.85185185 0.7037037  0.77777778 0.77777778 0.74074074\n",
            " 0.85185185 0.7037037  0.81481481 0.81481481 0.85185185 0.85185185\n",
            " 0.81481481 0.81481481 0.77777778 0.88888889 0.85185185 0.77777778\n",
            " 0.77777778 0.7037037  0.85185185 0.77777778 0.7037037  0.81481481\n",
            " 0.74074074 0.88888889 0.77777778 0.81481481 0.81481481 0.85185185\n",
            " 0.85185185 0.88888889 0.85185185 0.85185185 0.77777778 0.77777778\n",
            " 0.81481481 0.77777778 0.77777778 0.74074074 0.88888889 0.74074074\n",
            " 0.85185185 0.77777778 0.88888889 0.88888889 0.7037037  0.88888889\n",
            " 0.77777778 0.85185185 0.7037037  0.59259259 0.85185185 0.81481481\n",
            " 0.92592593 0.74074074 0.92592593 0.81481481 0.74074074 0.77777778\n",
            " 0.74074074 0.81481481 0.81481481 0.74074074 0.81481481 0.74074074\n",
            " 0.77777778 0.77777778 0.81481481 0.74074074 0.81481481 0.88888889\n",
            " 0.77777778 0.85185185 0.74074074 0.74074074 0.85185185 0.66666667\n",
            " 0.88888889 0.88888889 0.85185185 0.77777778]\n",
            "Accuracy: 80.37% (6.59%)\n",
            "Execution Time: 3.37 seconds\n",
            "------------------------------\n"
          ]
        }
      ],
      "source": [
        "heart_scores = []\n",
        "heart_mean = []\n",
        "heart_std = []\n",
        "model_names = []\n",
        "execution_times = []\n",
        "\n",
        "for name, clf in zip(names, classifiers):\n",
        "  start_time = time.time()\n",
        "  results = cross_val_score(clf, X, y, cv=rkf)\n",
        "  end_time = time.time()\n",
        "\n",
        "  heart_scores.append(results)\n",
        "  heart_mean.append(results.mean()*100)\n",
        "  heart_std.append(results.std()*100)\n",
        "  model_names.append(name)\n",
        "  execution_time = end_time - start_time\n",
        "  execution_times.append(execution_time)\n",
        "\n",
        "  print(f'--------- {name} on Heart Dataset ---------')\n",
        "  print(results)\n",
        "  print('Accuracy: %.2f%% (%.2f%%)' % (results.mean()*100, results.std()*100))\n",
        "  print(f'Execution Time: {execution_time:.2f} seconds')\n",
        "  print('------------------------------')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 143,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E_aJHJuhL3jF",
        "outputId": "5a091cf7-4de4-4dd7-d685-1d1933afa055"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[79.8148148148148,\n",
              " 80.07407407407408,\n",
              " 81.88888888888887,\n",
              " 80.88888888888889,\n",
              " 80.37037037037035]"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ],
      "source": [
        "heart_mean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 144,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MpVZdwPdL6vD",
        "outputId": "0682a62f-fdd2-4133-e906-cde6e803f7f8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[6.1391053534262126,\n",
              " 7.177544620300597,\n",
              " 6.807060806071759,\n",
              " 7.252453347732925,\n",
              " 6.594256968431428]"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ],
      "source": [
        "heart_std"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 145,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "sRbMBvIh3d7a",
        "outputId": "34522bad-6ae8-494b-f703-2f01bdab00f4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# algorithm comparison\n",
        "\n",
        "fig = plt.figure()\n",
        "fig.suptitle('Comparison between different Heart scores')\n",
        "ax = fig.add_subplot(111)\n",
        "plt.ylim(0.3, 1)\n",
        "plt.boxplot(heart_scores, showmeans=True)\n",
        "ax.set_xticklabels(model_names)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 146,
      "metadata": {
        "id": "pC4VNw3uL-ck"
      },
      "outputs": [],
      "source": [
        "Algo_results['Heart'] = heart_mean"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Algo_results_std['Heart'] = heart_std"
      ],
      "metadata": {
        "id": "yOLDzHJuyw2V"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 148,
      "metadata": {
        "id": "9zI6gWjBDpYL"
      },
      "outputs": [],
      "source": [
        "Algo_time_results['Heart'] = pd.Series(execution_times)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kxbJ_3R4DpYL"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "26ewM4zRDpYL"
      },
      "source": [
        "# **(Unbalanced) Tic-Tac-Toe**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mUg54r4MDpYL"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 149,
      "metadata": {
        "id": "lFFh7D9cDpYL"
      },
      "outputs": [],
      "source": [
        "tictactoe_df = pd.read_csv('/content/drive/MyDrive/DatasetSeminario/TicTacToe/TicTacToe.data', header=None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 150,
      "metadata": {
        "id": "I5HwZBKyDpYL",
        "outputId": "211f58d8-26cc-4204-d089-c3e30dfc7be9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "3  x  x  x  x  o  o  o  b  b  positive\n",
              "4  x  x  x  x  o  o  b  o  b  positive"
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            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ],
      "source": [
        "tictactoe_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 151,
      "metadata": {
        "id": "vB52jXiPDpYL"
      },
      "outputs": [],
      "source": [
        "X = tictactoe_df.iloc[:, :-1]\n",
        "y = tictactoe_df.iloc[:, -1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 152,
      "metadata": {
        "id": "v07ok5kRDpYL"
      },
      "outputs": [],
      "source": [
        "dummies = {\n",
        "            'x': 0,\n",
        "            'o': 1,\n",
        "            'b': 2,\n",
        "          }\n",
        "Xdummies = X.iloc[:, 0: 9].replace(dummies)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 153,
      "metadata": {
        "id": "slq27ObgDpYL"
      },
      "outputs": [],
      "source": [
        "X = X.iloc[:, 0:9].astype('category')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 154,
      "metadata": {
        "id": "nokha51LDpYL",
        "outputId": "48deab4f-f85c-4416-8c75-7a87f5bfeef5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 958 entries, 0 to 957\n",
            "Data columns (total 9 columns):\n",
            " #   Column  Non-Null Count  Dtype   \n",
            "---  ------  --------------  -----   \n",
            " 0   0       958 non-null    category\n",
            " 1   1       958 non-null    category\n",
            " 2   2       958 non-null    category\n",
            " 3   3       958 non-null    category\n",
            " 4   4       958 non-null    category\n",
            " 5   5       958 non-null    category\n",
            " 6   6       958 non-null    category\n",
            " 7   7       958 non-null    category\n",
            " 8   8       958 non-null    category\n",
            "dtypes: category(9)\n",
            "memory usage: 9.7 KB\n"
          ]
        }
      ],
      "source": [
        "X.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 155,
      "metadata": {
        "id": "rjDRt-Z4DpYM"
      },
      "outputs": [],
      "source": [
        "# Since version 1.3.2 XGBoost needs target columns to start with 0 value\n",
        "\n",
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 156,
      "metadata": {
        "id": "06RQB2OIDpYM"
      },
      "outputs": [],
      "source": [
        "d_names = [\n",
        "          'AdaBoost',\n",
        "          'GradBoost',\n",
        "          ]\n",
        "c_names =   [\n",
        "          'CatBoost',\n",
        "          'LightGBM',\n",
        "          'XGBoost'\n",
        "          ]\n",
        "\n",
        "classi1 = [\n",
        "            AdaBoostClassifier(),\n",
        "            GradientBoostingClassifier()\n",
        "          ]\n",
        "\n",
        "classi2 = [\n",
        "            CatBoostClassifier(cat_features=list(X.columns), silent=True),\n",
        "            LGBMClassifier(categorical_feature=list(X.columns), feature_name=list(X.columns), verbosity=-1),\n",
        "            XGBClassifier(enable_categorical=True, tree_method='hist')\n",
        "          ]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 157,
      "metadata": {
        "id": "FV-zK0Z7DpYM",
        "outputId": "63925a88-b461-4293-b087-57f35c7a55d2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------- AdaBoost on Tictactoe Dataset ---------\n",
            "[0.73958333 0.8125     0.69791667 0.8125     0.82291667 0.82291667\n",
            " 0.72916667 0.6875     0.76842105 0.77894737 0.875      0.69791667\n",
            " 0.84375    0.78125    0.8125     0.6875     0.76041667 0.77083333\n",
            " 0.72631579 0.83157895 0.82291667 0.75       0.70833333 0.72916667\n",
            " 0.70833333 0.75       0.77083333 0.82291667 0.81052632 0.78947368\n",
            " 0.79166667 0.82291667 0.71875    0.75       0.83333333 0.64583333\n",
            " 0.80208333 0.79166667 0.75789474 0.81052632 0.77083333 0.76041667\n",
            " 0.80208333 0.84375    0.72916667 0.80208333 0.83333333 0.6875\n",
            " 0.77894737 0.76842105 0.77083333 0.77083333 0.75       0.76041667\n",
            " 0.73958333 0.75       0.78125    0.82291667 0.8        0.84210526\n",
            " 0.80208333 0.66666667 0.67708333 0.80208333 0.80208333 0.78125\n",
            " 0.77083333 0.80208333 0.75789474 0.8        0.82291667 0.80208333\n",
            " 0.75       0.78125    0.8125     0.79166667 0.76041667 0.79166667\n",
            " 0.8        0.75789474 0.71875    0.75       0.77083333 0.79166667\n",
            " 0.77083333 0.76041667 0.79166667 0.82291667 0.78947368 0.77894737\n",
            " 0.70833333 0.82291667 0.80208333 0.71875    0.80208333 0.76041667\n",
            " 0.78125    0.82291667 0.76842105 0.70526316]\n",
            "Accuracy: 77.38% (4.42%)\n",
            "Execution Time: 8.26 seconds\n",
            "------------------------------\n",
            "--------- GradBoost on Tictactoe Dataset ---------\n",
            "[0.89583333 0.9375     0.9375     0.90625    0.95833333 0.92708333\n",
            " 0.92708333 0.9375     0.90526316 0.93684211 0.94791667 0.89583333\n",
            " 0.91666667 0.90625    0.91666667 0.90625    0.94791667 0.90625\n",
            " 0.92631579 0.93684211 0.95833333 0.90625    0.89583333 0.90625\n",
            " 0.91666667 0.90625    0.90625    0.89583333 0.95789474 0.97894737\n",
            " 0.89583333 0.95833333 0.89583333 0.94791667 0.94791667 0.875\n",
            " 0.89583333 0.95833333 0.91578947 0.94736842 0.92708333 0.88541667\n",
            " 0.9375     0.94791667 0.85416667 0.9375     0.94791667 0.90625\n",
            " 0.94736842 0.94736842 0.91666667 0.95833333 0.97916667 0.94791667\n",
            " 0.85416667 0.88541667 0.90625    0.94791667 0.92631579 0.87368421\n",
            " 0.97916667 0.86458333 0.85416667 0.90625    0.90625    0.92708333\n",
            " 0.94791667 0.94791667 0.94736842 0.87368421 0.9375     0.85416667\n",
            " 0.97916667 0.89583333 0.9375     0.86458333 0.90625    0.94791667\n",
            " 0.93684211 0.89473684 0.9375     0.875      0.92708333 0.91666667\n",
            " 0.89583333 0.95833333 0.90625    0.91666667 0.90526316 0.94736842\n",
            " 0.9375     0.91666667 0.95833333 0.9375     0.97916667 0.91666667\n",
            " 0.92708333 0.86458333 0.92631579 0.87368421]\n",
            "Accuracy: 92.11% (3.07%)\n",
            "Execution Time: 9.80 seconds\n",
            "------------------------------\n",
            "--------- CatBoost on Tictactoe Dataset ---------\n",
            "[1.         1.         1.         1.         1.         1.\n",
            " 0.98958333 1.         0.98947368 1.         1.         0.97916667\n",
            " 1.         1.         1.         1.         1.         1.\n",
            " 1.         1.         1.         1.         1.         1.\n",
            " 1.         1.         1.         1.         1.         0.98947368\n",
            " 1.         1.         1.         1.         1.         1.\n",
            " 1.         1.         1.         0.97894737 1.         1.\n",
            " 1.         1.         1.         1.         1.         1.\n",
            " 1.         0.98947368 1.         1.         0.96875    1.\n",
            " 1.         1.         1.         1.         1.         1.\n",
            " 1.         1.         1.         1.         1.         1.\n",
            " 1.         1.         1.         1.         1.         1.\n",
            " 1.         1.         1.         1.         1.         1.\n",
            " 1.         1.         1.         1.         1.         1.\n",
            " 1.         1.         1.         1.         1.         1.\n",
            " 1.         1.         1.         1.         1.         1.\n",
            " 1.         1.         1.         1.        ]\n",
            "Accuracy: 99.88% (0.46%)\n",
            "Execution Time: 430.68 seconds\n",
            "------------------------------\n",
            "--------- LightGBM on Tictactoe Dataset ---------\n",
            "[1.         0.98958333 1.         0.98958333 1.         0.97916667\n",
            " 0.97916667 0.98958333 0.98947368 0.98947368 1.         0.96875\n",
            " 0.98958333 1.         0.98958333 1.         1.         1.\n",
            " 0.97894737 0.98947368 0.98958333 0.97916667 0.96875    0.98958333\n",
            " 1.         1.         1.         0.98958333 0.98947368 0.97894737\n",
            " 1.         0.97916667 1.         0.98958333 1.         1.\n",
            " 1.         0.98958333 0.98947368 0.96842105 0.96875    1.\n",
            " 1.         1.         0.97916667 0.98958333 1.         1.\n",
            " 1.         0.98947368 0.98958333 1.         0.96875    0.98958333\n",
            " 0.98958333 0.98958333 0.98958333 1.         0.98947368 1.\n",
            " 1.         0.98958333 0.98958333 0.98958333 0.97916667 0.97916667\n",
            " 0.98958333 0.98958333 1.         1.         1.         1.\n",
            " 0.96875    1.         0.97916667 1.         0.97916667 1.\n",
            " 0.98947368 1.         0.98958333 0.98958333 0.97916667 1.\n",
            " 0.97916667 1.         0.98958333 1.         1.         0.98947368\n",
            " 0.98958333 0.98958333 0.97916667 0.98958333 1.         1.\n",
            " 0.98958333 0.98958333 0.98947368 1.        ]\n",
            "Accuracy: 99.11% (0.91%)\n",
            "Execution Time: 3.52 seconds\n",
            "------------------------------\n",
            "--------- XGBoost on Tictactoe Dataset ---------\n",
            "[1.         0.98958333 0.98958333 0.98958333 1.         0.97916667\n",
            " 0.97916667 0.98958333 0.98947368 0.98947368 1.         0.94791667\n",
            " 0.97916667 1.         0.98958333 1.         1.         1.\n",
            " 0.97894737 0.97894737 0.98958333 0.97916667 0.97916667 0.95833333\n",
            " 0.97916667 1.         1.         0.98958333 0.98947368 0.98947368\n",
            " 0.98958333 0.95833333 1.         0.97916667 0.98958333 1.\n",
            " 1.         0.98958333 0.98947368 0.97894737 0.96875    0.98958333\n",
            " 0.98958333 1.         0.97916667 0.98958333 0.98958333 1.\n",
            " 1.         0.98947368 1.         0.97916667 0.95833333 0.98958333\n",
            " 0.97916667 0.98958333 1.         1.         0.98947368 0.97894737\n",
            " 1.         1.         0.97916667 0.97916667 0.96875    0.98958333\n",
            " 0.98958333 0.98958333 1.         0.98947368 0.98958333 1.\n",
            " 0.97916667 1.         0.97916667 0.98958333 0.96875    1.\n",
            " 1.         0.97894737 0.97916667 0.98958333 0.97916667 0.98958333\n",
            " 0.96875    1.         0.98958333 0.98958333 1.         0.98947368\n",
            " 0.98958333 0.98958333 0.96875    0.98958333 1.         1.\n",
            " 0.98958333 0.98958333 0.98947368 1.        ]\n",
            "Accuracy: 98.81% (1.10%)\n",
            "Execution Time: 5.66 seconds\n",
            "------------------------------\n"
          ]
        }
      ],
      "source": [
        "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
        "\n",
        "tictactoe_scores = []\n",
        "tictactoe_scores_mean = []\n",
        "tictactoe_scores_std = []\n",
        "model_names = []\n",
        "execution_times = []\n",
        "\n",
        "for name, clf in zip(d_names, classi1):\n",
        "  start_time = time.time()\n",
        "  results = cross_val_score(clf, Xdummies, y, cv=rkf)\n",
        "  end_time = time.time()\n",
        "  tictactoe_scores.append(results)\n",
        "  tictactoe_scores_mean.append(results.mean()*100)\n",
        "  tictactoe_scores_std.append(results.std()*100)\n",
        "  model_names.append(name)\n",
        "  execution_time = end_time - start_time\n",
        "  execution_times.append(execution_time)\n",
        "\n",
        "  print(f'--------- {name} on Tictactoe Dataset ---------')\n",
        "  print(results)\n",
        "  print('Accuracy: %.2f%% (%.2f%%)' % (results.mean()*100, results.std()*100))\n",
        "  print(f'Execution Time: {execution_time:.2f} seconds')\n",
        "  print('------------------------------')\n",
        "\n",
        "for name, clf in zip(c_names, classi2):\n",
        "  start_time = time.time()\n",
        "  results = cross_val_score(clf, X, y, cv=rkf)\n",
        "  end_time = time.time()\n",
        "  tictactoe_scores.append(results)\n",
        "  tictactoe_scores_mean.append(results.mean()*100)\n",
        "  tictactoe_scores_std.append(results.std()*100)\n",
        "  model_names.append(name)\n",
        "  execution_time = end_time - start_time\n",
        "  execution_times.append(execution_time)\n",
        "\n",
        "  print(f'--------- {name} on Tictactoe Dataset ---------')\n",
        "  print(results)\n",
        "  print('Accuracy: %.2f%% (%.2f%%)' % (results.mean()*100, results.std()*100))\n",
        "  print(f'Execution Time: {execution_time:.2f} seconds')\n",
        "  print('------------------------------')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 158,
      "metadata": {
        "id": "7irzB5rvDpYM",
        "outputId": "760921c4-ae55-4b55-fcaf-1cfe8bb8099b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# algorithm comparison\n",
        "\n",
        "fig = plt.figure()\n",
        "fig.suptitle('Comparison between different Tictactoe scores')\n",
        "ax = fig.add_subplot(111)\n",
        "plt.ylim(0.3, 1)\n",
        "plt.boxplot(tictactoe_scores, showmeans=True)\n",
        "ax.set_xticklabels(model_names)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 159,
      "metadata": {
        "id": "hvCgpixXDpYM"
      },
      "outputs": [],
      "source": [
        "Algo_results['TicTacToe'] = tictactoe_scores_mean"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Algo_results_std['TicTacToe'] = tictactoe_scores_std"
      ],
      "metadata": {
        "id": "EbkxJRavy4Zb"
      },
      "execution_count": 160,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 161,
      "metadata": {
        "id": "a1fpYxSdDpYM"
      },
      "outputs": [],
      "source": [
        "Algo_time_results['TicTacToe'] = pd.Series(execution_times)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S8bC0IVRMCx4"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jDcirJEuMDqT"
      },
      "source": [
        "# **(Unbalanced) Liver**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gf7-6_3lMEIk"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 162,
      "metadata": {
        "id": "HOARC7vxMGYl"
      },
      "outputs": [],
      "source": [
        "liver_df = pd.read_csv('/content/drive/MyDrive/DatasetSeminario/Liver/bupa.data', header=None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 163,
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        "colab": {
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        },
        "id": "cBB4WHy9QnhI",
        "outputId": "0b37db4b-a20f-4290-f02a-0aec6a850510"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(345, 7)"
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          },
          "metadata": {},
          "execution_count": 163
        }
      ],
      "source": [
        "liver_df.shape"
      ]
    },
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          },
          "metadata": {},
          "execution_count": 164
        }
      ],
      "source": [
        "liver_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 165,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T5ek83VtQwvM",
        "outputId": "4ae5e609-2217-42ba-de31-ebc6ec391e69"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 345 entries, 0 to 344\n",
            "Data columns (total 7 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   0       345 non-null    int64  \n",
            " 1   1       345 non-null    int64  \n",
            " 2   2       345 non-null    int64  \n",
            " 3   3       345 non-null    int64  \n",
            " 4   4       345 non-null    int64  \n",
            " 5   5       345 non-null    float64\n",
            " 6   6       345 non-null    int64  \n",
            "dtypes: float64(1), int64(6)\n",
            "memory usage: 19.0 KB\n"
          ]
        }
      ],
      "source": [
        "liver_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 166,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zBkiyL0fQ2H9",
        "outputId": "6e5806d4-5c1b-4041-f221-b02b4cef28a6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    0\n",
              "2    0\n",
              "3    0\n",
              "4    0\n",
              "5    0\n",
              "6    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 166
        }
      ],
      "source": [
        "liver_df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 167,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ycqw0X7bQ7Vs",
        "outputId": "1f807521-f480-4047-8246-f43e32e7fef9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    200\n",
              "1    145\n",
              "Name: 6, dtype: int64"
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          },
          "metadata": {},
          "execution_count": 167
        }
      ],
      "source": [
        "# Unbalanced dataset\n",
        "\n",
        "liver_df[6].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 168,
      "metadata": {
        "id": "XgfkEnnnRZoT"
      },
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      "source": [
        "X = liver_df.iloc[:, :-1]\n",
        "y = liver_df.iloc[:, -1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 169,
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-565a7825-d8ec-4bc3-acd4-6fb5422ab1ba')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "\n",
              "  <style>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "    }\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",
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              "\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-565a7825-d8ec-4bc3-acd4-6fb5422ab1ba 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-565a7825-d8ec-4bc3-acd4-6fb5422ab1ba');\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",
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              "            title=\"Suggest charts.\"\n",
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              "    </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",
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              "    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-0cebf21b-00c2-4aab-b245-d191368c16ad button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ],
      "source": [
        "X.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 170,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mBjrBExWRtVn",
        "outputId": "ed36c15b-9bfb-43d9-bba7-9a5502fdef7f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    2\n",
              "2    2\n",
              "3    2\n",
              "4    2\n",
              "Name: 6, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
      ],
      "source": [
        "y.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 171,
      "metadata": {
        "id": "OdvxSN1QRSo9"
      },
      "outputs": [],
      "source": [
        "# Since version 1.3.2 XGBoost needs target columns to start with 0 value\n",
        "\n",
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 172,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XS28nqJ7Rv7D",
        "outputId": "25658825-c47f-475c-ad2a-eba6acf17e0f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0])"
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          "metadata": {},
          "execution_count": 172
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      ],
      "source": [
        "y"
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    },
    {
      "cell_type": "code",
      "execution_count": 173,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-bEnmPeDRwR9",
        "outputId": "dc4872d3-c563-4bef-c35a-a961f700d563"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------- AdaBoost on Liver Dataset ---------\n",
            "[0.8        0.8        0.65714286 0.74285714 0.71428571 0.64705882\n",
            " 0.67647059 0.79411765 0.70588235 0.64705882 0.71428571 0.77142857\n",
            " 0.71428571 0.65714286 0.65714286 0.79411765 0.55882353 0.76470588\n",
            " 0.70588235 0.82352941 0.74285714 0.62857143 0.71428571 0.74285714\n",
            " 0.68571429 0.73529412 0.79411765 0.82352941 0.82352941 0.58823529\n",
            " 0.62857143 0.74285714 0.8        0.62857143 0.71428571 0.79411765\n",
            " 0.70588235 0.67647059 0.67647059 0.55882353 0.74285714 0.77142857\n",
            " 0.74285714 0.71428571 0.77142857 0.79411765 0.70588235 0.73529412\n",
            " 0.73529412 0.70588235 0.68571429 0.8        0.62857143 0.82857143\n",
            " 0.71428571 0.67647059 0.64705882 0.70588235 0.85294118 0.73529412\n",
            " 0.62857143 0.62857143 0.77142857 0.68571429 0.8        0.61764706\n",
            " 0.61764706 0.85294118 0.76470588 0.76470588 0.71428571 0.68571429\n",
            " 0.82857143 0.74285714 0.71428571 0.76470588 0.70588235 0.85294118\n",
            " 0.61764706 0.70588235 0.88571429 0.6        0.74285714 0.71428571\n",
            " 0.74285714 0.73529412 0.70588235 0.70588235 0.67647059 0.82352941\n",
            " 0.8        0.8        0.74285714 0.65714286 0.8        0.61764706\n",
            " 0.64705882 0.61764706 0.76470588 0.76470588]\n",
            "Accuracy: 72.25% (7.03%)\n",
            "Execution Time: 7.61 seconds\n",
            "------------------------------\n",
            "--------- GradBoost on Liver Dataset ---------\n",
            "[0.68571429 0.77142857 0.71428571 0.65714286 0.68571429 0.73529412\n",
            " 0.76470588 0.73529412 0.73529412 0.67647059 0.71428571 0.68571429\n",
            " 0.71428571 0.62857143 0.71428571 0.79411765 0.64705882 0.67647059\n",
            " 0.73529412 0.88235294 0.74285714 0.65714286 0.8        0.77142857\n",
            " 0.65714286 0.73529412 0.67647059 0.70588235 0.70588235 0.73529412\n",
            " 0.71428571 0.71428571 0.77142857 0.65714286 0.74285714 0.73529412\n",
            " 0.79411765 0.64705882 0.58823529 0.5        0.71428571 0.82857143\n",
            " 0.62857143 0.74285714 0.77142857 0.79411765 0.76470588 0.64705882\n",
            " 0.73529412 0.58823529 0.71428571 0.74285714 0.62857143 0.74285714\n",
            " 0.71428571 0.79411765 0.67647059 0.73529412 0.73529412 0.79411765\n",
            " 0.74285714 0.68571429 0.82857143 0.68571429 0.8        0.73529412\n",
            " 0.70588235 0.85294118 0.73529412 0.82352941 0.65714286 0.65714286\n",
            " 0.85714286 0.77142857 0.77142857 0.76470588 0.73529412 0.73529412\n",
            " 0.70588235 0.79411765 0.8        0.65714286 0.62857143 0.8\n",
            " 0.65714286 0.70588235 0.67647059 0.79411765 0.67647059 0.82352941\n",
            " 0.71428571 0.74285714 0.71428571 0.6        0.82857143 0.58823529\n",
            " 0.70588235 0.70588235 0.79411765 0.73529412]\n",
            "Accuracy: 72.29% (6.57%)\n",
            "Execution Time: 8.47 seconds\n",
            "------------------------------\n",
            "--------- CatBoost on Liver Dataset ---------\n",
            "[0.8        0.77142857 0.71428571 0.71428571 0.68571429 0.73529412\n",
            " 0.70588235 0.76470588 0.73529412 0.64705882 0.71428571 0.74285714\n",
            " 0.85714286 0.65714286 0.74285714 0.76470588 0.67647059 0.73529412\n",
            " 0.73529412 0.88235294 0.74285714 0.71428571 0.82857143 0.77142857\n",
            " 0.68571429 0.79411765 0.70588235 0.79411765 0.76470588 0.70588235\n",
            " 0.77142857 0.8        0.8        0.71428571 0.77142857 0.82352941\n",
            " 0.67647059 0.70588235 0.64705882 0.64705882 0.74285714 0.8\n",
            " 0.65714286 0.77142857 0.85714286 0.76470588 0.76470588 0.70588235\n",
            " 0.67647059 0.67647059 0.82857143 0.71428571 0.65714286 0.77142857\n",
            " 0.65714286 0.73529412 0.76470588 0.79411765 0.79411765 0.82352941\n",
            " 0.68571429 0.65714286 0.82857143 0.62857143 0.85714286 0.79411765\n",
            " 0.70588235 0.91176471 0.70588235 0.79411765 0.6        0.68571429\n",
            " 0.82857143 0.74285714 0.77142857 0.76470588 0.76470588 0.79411765\n",
            " 0.70588235 0.76470588 0.8        0.65714286 0.8        0.82857143\n",
            " 0.65714286 0.67647059 0.73529412 0.73529412 0.76470588 0.82352941\n",
            " 0.77142857 0.85714286 0.65714286 0.65714286 0.85714286 0.64705882\n",
            " 0.70588235 0.70588235 0.79411765 0.67647059]\n",
            "Accuracy: 74.40% (6.45%)\n",
            "Execution Time: 70.59 seconds\n",
            "------------------------------\n",
            "--------- LightGBM on Liver Dataset ---------\n",
            "[0.77142857 0.77142857 0.68571429 0.71428571 0.62857143 0.70588235\n",
            " 0.64705882 0.85294118 0.76470588 0.70588235 0.6        0.68571429\n",
            " 0.77142857 0.68571429 0.71428571 0.73529412 0.61764706 0.70588235\n",
            " 0.67647059 0.76470588 0.71428571 0.65714286 0.74285714 0.71428571\n",
            " 0.77142857 0.73529412 0.61764706 0.67647059 0.76470588 0.67647059\n",
            " 0.8        0.77142857 0.77142857 0.71428571 0.65714286 0.73529412\n",
            " 0.70588235 0.67647059 0.67647059 0.61764706 0.77142857 0.8\n",
            " 0.77142857 0.74285714 0.8        0.73529412 0.73529412 0.70588235\n",
            " 0.64705882 0.67647059 0.71428571 0.65714286 0.62857143 0.74285714\n",
            " 0.71428571 0.67647059 0.76470588 0.82352941 0.73529412 0.85294118\n",
            " 0.54285714 0.71428571 0.8        0.65714286 0.68571429 0.70588235\n",
            " 0.70588235 0.73529412 0.70588235 0.79411765 0.62857143 0.65714286\n",
            " 0.88571429 0.74285714 0.65714286 0.82352941 0.67647059 0.76470588\n",
            " 0.73529412 0.73529412 0.8        0.62857143 0.68571429 0.74285714\n",
            " 0.6        0.70588235 0.67647059 0.73529412 0.73529412 0.79411765\n",
            " 0.77142857 0.82857143 0.62857143 0.65714286 0.74285714 0.67647059\n",
            " 0.70588235 0.67647059 0.79411765 0.76470588]\n",
            "Accuracy: 71.80% (6.22%)\n",
            "Execution Time: 1.40 seconds\n",
            "------------------------------\n",
            "--------- XGBoost on Liver Dataset ---------\n",
            "[0.82857143 0.77142857 0.65714286 0.74285714 0.71428571 0.70588235\n",
            " 0.70588235 0.73529412 0.76470588 0.64705882 0.6        0.68571429\n",
            " 0.74285714 0.62857143 0.71428571 0.67647059 0.64705882 0.73529412\n",
            " 0.64705882 0.70588235 0.65714286 0.68571429 0.71428571 0.77142857\n",
            " 0.71428571 0.76470588 0.61764706 0.70588235 0.64705882 0.67647059\n",
            " 0.74285714 0.71428571 0.74285714 0.74285714 0.6        0.82352941\n",
            " 0.73529412 0.58823529 0.64705882 0.61764706 0.8        0.82857143\n",
            " 0.65714286 0.71428571 0.8        0.64705882 0.70588235 0.67647059\n",
            " 0.64705882 0.67647059 0.68571429 0.62857143 0.6        0.65714286\n",
            " 0.71428571 0.76470588 0.79411765 0.76470588 0.76470588 0.76470588\n",
            " 0.65714286 0.74285714 0.8        0.62857143 0.68571429 0.70588235\n",
            " 0.70588235 0.82352941 0.61764706 0.76470588 0.51428571 0.6\n",
            " 0.85714286 0.71428571 0.65714286 0.70588235 0.70588235 0.73529412\n",
            " 0.76470588 0.73529412 0.8        0.62857143 0.71428571 0.77142857\n",
            " 0.54285714 0.73529412 0.64705882 0.76470588 0.70588235 0.79411765\n",
            " 0.71428571 0.8        0.65714286 0.68571429 0.85714286 0.58823529\n",
            " 0.70588235 0.70588235 0.64705882 0.73529412]\n",
            "Accuracy: 70.58% (6.77%)\n",
            "Execution Time: 5.49 seconds\n",
            "------------------------------\n"
          ]
        }
      ],
      "source": [
        "liver_scores = []\n",
        "liver_mean = []\n",
        "liver_std = []\n",
        "model_names = []\n",
        "execution_times = []\n",
        "\n",
        "for name, clf in zip(names, classifiers):\n",
        "  start_time = time.time()\n",
        "  results = cross_val_score(clf, X, y, cv=rkf)\n",
        "  end_time = time.time()\n",
        "\n",
        "  liver_scores.append(results)\n",
        "  liver_mean.append(results.mean()*100)\n",
        "  liver_std.append(results.std()*100)\n",
        "  model_names.append(name)\n",
        "  execution_time = end_time - start_time\n",
        "  execution_times.append(execution_time)\n",
        "\n",
        "  print(f'--------- {name} on Liver Dataset ---------')\n",
        "  print(results)\n",
        "  print('Accuracy: %.2f%% (%.2f%%)' % (results.mean()*100, results.std()*100))\n",
        "  print(f'Execution Time: {execution_time:.2f} seconds')\n",
        "  print('------------------------------')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 174,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RzaiNvbHSBqB",
        "outputId": "89854825-add1-450b-fd0a-c26771400591"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[72.25462184873949,\n",
              " 72.29327731092437,\n",
              " 74.40336134453781,\n",
              " 71.8016806722689,\n",
              " 70.57983193277309]"
            ]
          },
          "metadata": {},
          "execution_count": 174
        }
      ],
      "source": [
        "liver_mean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 175,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BulSs8uLSElJ",
        "outputId": "97ac495e-1f1f-49f3-9fcc-71a542220238"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[7.026728304847883,\n",
              " 6.574579560055363,\n",
              " 6.450640429588332,\n",
              " 6.215299205426823,\n",
              " 6.773062326505143]"
            ]
          },
          "metadata": {},
          "execution_count": 175
        }
      ],
      "source": [
        "liver_std"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 176,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "oAfu6-n13pKR",
        "outputId": "5a25e24c-8b68-42a4-f4cb-8fa0553264f0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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wazpTH/Km6HQ6s8pFxKx2ADe+pT/22GN44IEHMG/ePLi7u8PS0hJLly7Fl19+War+zWdRbmfQoEHo1q0b1q9fj82bN2PGjBmYPn064uLi8Mgjj5jdzrLWuSoZDAb07t0b48ePN/l8SXi5nZJvs9u3b8fx48cRGBgIOzs7dOvWDf/3f/+HK1euIDk5Ge+//77RcjUaDX788UeT61mvXj2lHgDMmDEDAQEBJpdfUrdEVb5WqkPJOi1fvhxubm6lnr91uLher1dC3M3zcHFxwcqVK00u49ZwWZO2yf333w9vb298/fXXGDx4ML7//nvk5+cjLCxMqVMV26gsVX2cVtSt7yMVPQYAYObMmRg2bBi+/fZbbN68GWPHjkVMTAx+/fXXCocwMh/DSA3Xr18/LFy4EImJiUY/qZji5eUFg8GAI0eOwM/PTynPzs7GpUuX4OXlVaVtMxgMOH78uNGH6OHDhwFAuXbCunXrYG1tjU2bNkGv1yv1li5desfLd3d3x6hRozBq1CicPXsWgYGBeP/99/HII48o65qenl7qG196enqVb4ujR49CRIyC4K3bonnz5rhy5YrRtTFMuV2YvO+++3Dfffdhx44dOH78uPJzwAMPPICoqCisWbMGxcXFeOCBB5RpmjdvDhFB06ZNbxt4mjdvDuBGCC6vjXeiZNsfOXLEaN9cv34dJ06cgL+/f5Utq2SdXFxcKr1OzZs3x88//4yQkJAKh+XyVPQLQ1UYNGgQ5syZg9zcXKxevRre3t64//77leerYhvdzu2O05JlHzhwoMxl33ws3yotLQ3Ozs7lDt2t6DFQol27dmjXrh3effdd7Nq1CyEhIViwYAGmTZtW7rRUOewzUsONHz8ednZ2GDFiBLKzs0s9f+zYMcyZMwcA0LdvXwDA7NmzjerMmjULwI3+ElXtk08+Uf4vIvjkk09gaWmJhx56CMCNb4kajQbFxcVKvYyMDHzzzTeVXmZxcTFycnKMylxcXNC4cWPlZ6COHTvCxcUFCxYsMPpp6Mcff0RqamqVb4szZ85g/fr1yt+5ubn44osvEBAQoHzbHDRoEBITE7Fp06ZS01+6dAlFRUUAAFtbW6XMlG7dumHLli3YvXu3EkYCAgJgb2+PDz/8EDY2NggKClLqDxw4EDqdDlOmTCn17VxEcOHCBQBAUFAQmjdvjo8++sioj0GJc+fOVXRz3FbHjh3RqFEjLFiwAIWFhUr5smXLylznygoNDYWDgwM++OADXL9+vdTzFVmnQYMGobi4GO+9916p54qKiirV5pIPz6peX1PCwsJQUFCAzz//HPHx8Rg0aJDR81WxjUypyHEaGBiIpk2bYvbs2aW2Rclr1d3dHQEBAfj888+N6hw4cACbN29W3vdup6LHQG5urnIclmjXrh20Wq3R+whVPZ4ZqeGaN2+OL7/8EmFhYfDz8zO6AuuuXbuwZs0aDBs2DADg7++P8PBwLFy4EJcuXUL37t2xe/dufP755xgwYAB69uxZpW2ztrZGfHw8wsPD0aVLF/z444/YsGED3n77beXU9aOPPopZs2ahT58+GDx4MM6ePYvY2Fj4+Pjgjz/+qNRyL1++jCZNmuCpp56Cv78/6tWrh59//hl79uzBzJkzAdz4DXj69OmIiIhA9+7d8eyzzyI7Oxtz5syBt7c3XnvttSrbDsCNn1iGDx+OPXv2wNXVFUuWLEF2drbRGaA333wT3333Hfr164dhw4YhKCgIeXl52L9/P9auXYuMjAw4OzvDxsYGrVu3xurVq9GyZUs0aNAAbdu2VX5X79atG1auXAmNRqP8bKPT6dC1a1ds2rQJPXr0MOoY2rx5c0ybNg0TJ05ERkYGBgwYAHt7e5w4cQLr16/Hiy++iDfeeANarRafffYZHnnkEbRp0wYRERHw8PDAX3/9ha1bt8LBwQHff//9HW8rS0tLTJs2DS+99BIefPBBhIWF4cSJE1i6dGmV9xlxcHDA/Pnz8fzzzyMwMBDPPPMMGjVqhFOnTmHDhg0ICQkxCtSmdO/eHS+99BJiYmKQkpKChx9+GJaWljhy5AjWrFmDOXPm4KmnnjKrXQEBAdDpdJg+fTpycnKg1+vx4IMPltkvpcSsWbOUsFpCq9Xi7bffLnOawMBA+Pj44J133kFBQYHRTzRA1WwjUypynGq1WsyfPx/9+/dHQEAAIiIi4O7ujrS0NBw8eFAJ7jNmzMAjjzyC4OBgDB8+HPn5+Zg7dy4cHR0rdM+qih4DW7ZswZgxY/D000+jZcuWKCoqwvLly6HT6fDkk0+avQ3IDGoM4SHzHT58WCIjI8Xb21usrKzE3t5eQkJCZO7cuUYXNLt+/bpMmTJFmjZtKpaWluLp6Xnbi57dCv9ceOxmJcMrZ8yYoZSZuuiZq6urREdHG12PRERk8eLF0qJFC9Hr9eLr6ytLly5VhuCVt+ybnysZ6lpQUCBvvvmm+Pv7i729vdjZ2Ym/v7/Ja4KsXr1aOnToIHq9Xho0aHDbi57dylQbTbn5omft27dX1tPU8MDLly/LxIkTxcfHR6ysrMTZ2Vm6du0qH330kdH1Knbt2iVBQUFiZWVVapjvwYMHlWuy3GzatGkmr/NSYt26dfKvf/1L7OzsxM7OTnx9fWX06NGSnp5uVC85OVkGDhwoDRs2FL1eL15eXjJo0CBJSEgotW1uHWpaMuz25ms/lGXevHnStGlT0ev10rFjxwpd9OzmZVRkaG+JrVu3SmhoqDg6Ooq1tbU0b95chg0bJr///rtSp6zXQYmFCxdKUFCQ2NjYiL29vbRr107Gjx8vZ86cUeqUdVzdul4iIosWLZJmzZqJTqer8EXPTD10Op2yjmXN55133hEA4uPjU+YyqmIb3cyc4/SXX36R3r17K/Xat29vdD0dEZGff/5ZQkJCxMbGRhwcHKR///5lXvSsrCHQ5R0Dx48flxdeeEGaN28u1tbW0qBBA+nZs6f8/PPPFVpnqjyNSA3paUa1yrBhw7B27VqTp/OJiIjMwT4jREREpCqGESIiIlIVwwgRERGpin1GiIiISFU8M0JERESqYhghIiIiVTGMEBERkaoYRoiIiEhVDCNERESkKoYRIiIiUhXDCBEREamKYYSIiIhUxTBCREREqmIYISIiIlUxjBAREZGqGEaIiIhIVQwjREREpCqGESIiIlIVwwgRERGpimGEiIiIVMUwQkRERKpiGCEiIiJVMYwQERGRqhhGiIiISFUMI0RERKQqhhEiIiJSFcMIERERqYphhIiIiFRldhjZvn07+vfvj8aNG0Oj0eCbb74pd5pt27YhMDAQer0ePj4+WLZsWSWaSkRERHWR2WEkLy8P/v7+iI2NrVD9EydO4NFHH0XPnj2RkpKCV199FSNGjMCmTZvMbiwRERHVPRoRkUpPrNFg/fr1GDBgQJl13nrrLWzYsAEHDhxQyp555hlcunQJ8fHxlV00ERER1RHV3mckMTERvXr1MioLDQ1FYmJidS+aiIiIagGL6l5AVlYWXF1djcpcXV2Rm5uL/Px82NjYlJqmoKAABQUFyt8GgwEXL15Ew4YNodFoqrvJREREVAVEBJcvX0bjxo2h1ZZ9/qPaw0hlxMTEYMqUKWo3g4iIiKrA6dOn0aRJkzKfr/Yw4ubmhuzsbKOy7OxsODg4mDwrAgATJ05EVFSU8ndOTg7uu+8+nD59Gg4ODtXaXiIiIqoaubm58PT0hL29/W3rVXsYCQ4OxsaNG43KfvrpJwQHB5c5jV6vh16vL1Xu4ODAMEJERFTLlNfFwuwOrFeuXEFKSgpSUlIA3Bi6m5KSglOnTgG4cVZj6NChSv2XX34Zx48fx/jx45GWloZ58+bh66+/xmuvvWbuoomIiKgOMjuM/P777+jQoQM6dOgAAIiKikKHDh0wadIkAEBmZqYSTACgadOm2LBhA3766Sf4+/tj5syZ+OyzzxAaGlpFq0BERES12R1dZ+Ruyc3NhaOjI3JycvgzDRERUS1R0c9v3puGiIiIVMUwQkRERKpiGCEiIiJVMYwQERGRqhhGiIiISFUMI0RERKQqhhEiIiJSFcMIERERqYphhIiIiFTFMEJERESqYhghIiIiVTGMEBERkaoYRoiIiEhVDCNERESkKoYRIiIiUhXDCBEREamKYYSIiIhUxTBCREREqmIYISIiIlUxjBAREZGqGEaIiIhIVQwjREREpCqGESIiIlIVwwgRERGpimGEiIiIVMUwQkRERKpiGCEiIiJVMYwQERGRqhhGiIiISFUMI0RERKQqhhEiIiJSFcMIERERqYphhIiIiFTFMEJERESqYhghIiIiVTGMEBERkaoYRoiIiEhVDCNERESkKoYRIiIiUpWF2g0gorrj6tWrSEtLM2ua/Px8ZGRkwNvbGzY2NhWeztfXF7a2tuY2kYhqIIYRIqoyaWlpCAoKuivLSkpKQmBg4F1ZFhFVL4YRIqoyvr6+SEpKMmua1NRUDBkyBCtWrICfn59ZyyKiuqFSYSQ2NhYzZsxAVlYW/P39MXfuXHTu3Nlk3evXryMmJgaff/45/vrrL7Rq1QrTp09Hnz597qjhRFTz2NraVvpshZ+fH890EN2jzO7Aunr1akRFRSE6Ohp79+6Fv78/QkNDcfbsWZP13333XXz66aeYO3cuDh06hJdffhlPPPEEkpOT77jxREREVPuZHUZmzZqFyMhIREREoHXr1liwYAFsbW2xZMkSk/WXL1+Ot99+G3379kWzZs0wcuRI9O3bFzNnzrzjxhMREVHtZ1YYKSwsRFJSEnr16vW/GWi16NWrFxITE01OU1BQAGtra6MyGxsb/PLLL2Uup6CgALm5uUYPIiIiqpvMCiPnz59HcXExXF1djcpdXV2RlZVlcprQ0FDMmjULR44cgcFgwE8//YS4uDhkZmaWuZyYmBg4OjoqD09PT3OaSURERLVItV/0bM6cOWjRogV8fX1hZWWFMWPGICIiAlpt2YueOHEicnJylMfp06eru5lERESkErPCiLOzM3Q6HbKzs43Ks7Oz4ebmZnKaRo0a4ZtvvkFeXh5OnjyJtLQ01KtXD82aNStzOXq9Hg4ODkYPIiIiqpvMCiNWVlYICgpCQkKCUmYwGJCQkIDg4ODbTmttbQ0PDw8UFRVh3bp1ePzxxyvXYiIiIqpTzL7OSFRUFMLDw9GxY0d07twZs2fPRl5eHiIiIgAAQ4cOhYeHB2JiYgAAv/32G/766y8EBATgr7/+wuTJk2EwGDB+/PiqXRMiIiKqlcwOI2FhYTh37hwmTZqErKwsBAQEID4+XunUeurUKaP+INeuXcO7776L48ePo169eujbty+WL18OJyenKlsJIiIiqr00IiJqN6I8ubm5cHR0RE5ODvuPENUxe/fuRVBQEO81Q1QHVfTzm/emqSRz705a2TuTArw7KRFVzt16n+J7FN0phpFK4t1Jiaimu1vvU3yPojvFMFJJ5t6dtLJ3Ji1ZFpXN3G9/AL8B0r3hbr1P8T2K7hTDSCVV9u6kvDNp1eNZKiLT+D5FtQXDCNV65n77A/gNkIioJmEYoVqvst/+AH4DJCKqCar93jREREREt8MwQkRERKpiGCEiIiJVMYwQERGRqhhGiIiISFUMI0RERKQqhhEiIiJSFcMIERERqYphhIiIiFTFK7ASERFVM3Nv6FnZm3kCtfOGngwjRERE1Yw39Lw9hhEiIqJqZu4NPSt7M8+SZdU2DCNERETVrLI39LxXbubJDqxERESkKoYRIiIiUhXDCBEREamKYYSIiIhUxTBCREREqmIYISIiIlUxjBAREZGqGEaIiIhIVQwjREREpCqGESIiIlIVwwgRERGpimGEiIiIVMUwQkRERKpiGCEiIiJVMYwQERGRqhhGiIiISFUMI0RERKQqhhEiIiJSFcMIERERqYphhIiIiFTFMEJERESqYhghIiIiVTGMEBERkaoqFUZiY2Ph7e0Na2trdOnSBbt3775t/dmzZ6NVq1awsbGBp6cnXnvtNVy7dq1SDSYiIqK6xewwsnr1akRFRSE6Ohp79+6Fv78/QkNDcfbsWZP1v/zyS0yYMAHR0dFITU3F4sWLsXr1arz99tt33HgiIiKq/cwOI7NmzUJkZCQiIiLQunVrLFiwALa2tliyZInJ+rt27UJISAgGDx4Mb29vPPzww3j22WfLPZtCRERE9wazwkhhYSGSkpLQq1ev/81Aq0WvXr2QmJhocpquXbsiKSlJCR/Hjx/Hxo0b0bdv3zKXU1BQgNzcXKMHERER1U0W5lQ+f/48iouL4erqalTu6uqKtLQ0k9MMHjwY58+fx7/+9S+ICIqKivDyyy/f9meamJgYTJkyxZymERERUS1V7aNptm3bhg8++ADz5s3D3r17ERcXhw0bNuC9994rc5qJEyciJydHeZw+fbq6m0lEREQqMevMiLOzM3Q6HbKzs43Ks7Oz4ebmZnKaf//733j++ecxYsQIAEC7du2Ql5eHF198Ee+88w602tJ5SK/XQ6/Xm9M0IqomR44cweXLl6tt/qmpqUb/Vhd7e3u0aNGiWpdBRJVjVhixsrJCUFAQEhISMGDAAACAwWBAQkICxowZY3Kaq1evlgocOp0OACAilWgyEd0tR44cQcuWLe/KsoYMGVLtyzh8+DADCVENZFYYAYCoqCiEh4ejY8eO6Ny5M2bPno28vDxEREQAAIYOHQoPDw/ExMQAAPr3749Zs2ahQ4cO6NKlC44ePYp///vf6N+/vxJKiKhmKjkjsmLFCvj5+VXLMvLz85GRkQFvb2/Y2NhUyzJSU1MxZMiQaj3DQ0SVZ3YYCQsLw7lz5zBp0iRkZWUhICAA8fHxSqfWU6dOGZ0Jeffdd6HRaPDuu+/ir7/+QqNGjdC/f3+8//77VbcWRFSt/Pz8EBgYWG3zDwkJqbZ5E1HNZ3YYAYAxY8aU+bPMtm3bjBdgYYHo6GhER0dXZlFERERUx/HeNERERKQqhhEiIiJSFcMIERERqYphhIiIiFTFMEJERESqYhghIiIiVTGMEBERkaoqdZ2Ruqo678HB+2+Ypy7cD6Wu7AsiourGMPKPu3UPDt5/o3x16X4otX1fEBHdDQwj/6jue3Dw/hsVVxfuh1JX9gUR0d3AMHKL6rwHB++/YR7eD4WI6N7AMEJEVEvUhb5UAPtTUWkMI0REtUBd6ksFsD8VGWMYISKqBepCXyqA/anINIYRIqJahH2pqC7iRc+IiIhIVQwjREREpCqGESIiIlIVwwgRERGpimGEiFSVeCYRj3/zOBLPJKrdFCJSCUfTEJFqRARz9s7B8ZzjmLN3Du53vx8ajUbtZhGVixegq1oMI0Skml1nduHghYMAgIMXDmLXmV0I8eDQUqrZeAG6qscwQkRl0hRdQwc3LWwuHQbOVO2vuiKCubunQwstDDBACy3m7p6Orp2nVPnZEZtLh9HBTQtN0bUqnS/dm3gBuqrHMEJEZbK+cgp7X6oHbH8J2F61895lY42Dbi7K3wYYcDD3BHat6IOQ/KoNDX4A9r5UD6lXTgHoWqXzpnsXL0BXdRhGiKhM1+rdh8BPr2DlypXw8/WtsvneOCsSDW3uSRhgUMq10GJuyy5VfnYkNS0Nzz33HBb3va/K5klEVYdhhIjKJBbWSM4yIN+pJdA4oMrmu+uvnTiYe6JUuXJ2BFcR0rjqvhXmZxmQnGWAWFhX2TyJqOowjFCNU539FO4W9lEom4hgbvJcaKCBQEo9r4EGc5PnomvjrhxZQ3SPYBihGqc6+yncLeyjULbrhuvIyssyGUQAQCDIysvCdcN1WOms7nLriEgNDCNU41RXP4W7iX0Uymals8Kqfqtw8drFMus0sG7AIEJ0D2EYoRqnuvop3E3so3B7bnZucLNzU7sZRFRD1M4f5GshXvK6ZuH+ICKqORhG7oJbL3ktYvq3cro7uD+IiGoWhpG7wNQlr0k93B9ERDULw0g1KxnGqNXc2NRajRZzk+fy27hKuD+IysafL0ktDCPVrORbuEFuXGXSIAZ+G1cR9weRafz5ktTEMFKNbv0WXoLfxtXB/UFUNv58SWpiGKlGt34LL8Fv4+rg/iAyjT9fktoYRqrJzZe8NqXkktc82O8O7g+isvHnS1IbL3r2j6q+H8p1w3VkXT59+0teX/4T1//6HVZayzteHsD7odwOL0FOtV113bPpxh2Up0MLbek7KO+eXuV3UOb7FJnCMPKPqr4fihWAVTodLurKftNoUPwXrNJ73fnC/sH7oZSNlyCn2q667tm0y8YaB91cSpUrd1Be0Qch+VUXHPg+VTGJZxLx4e4PMaHzBAQ3Dla7OdWOYeQf1XE/FLd/HncL74dye7wEOdVm1fEedeOsSDQ0uRll30G5ZZcqPTvC96ny3Tqy6X73++v8HawZRv7B+6EQUU1WHe9R14sLkVWYe/ufLwtzcd2tTZWdNeT7VPlMjWwK8QhRuVXVq1JhJDY2FjNmzEBWVhb8/f0xd+5cdO7c2WTdHj164L///W+p8r59+2LDhg2VWTwREVUB/nxZOdXVfwco3YenuvruADWr/47ZYWT16tWIiorCggUL0KVLF8yePRuhoaFIT0+Hi0vp3x3j4uJQWFio/H3hwgX4+/vj6aefvrOWExHRHePPl+arrv47QOk+PNXVdweoWf13zA4js2bNQmRkJCIiIgAACxYswIYNG7BkyRJMmDChVP0GDRoY/b1q1SrY2toyjBARUa1UHf13gP/14dHmniw9sqmK++4ANav/jllhpLCwEElJSZg4caJSptVq0atXLyQmVuxeBosXL8YzzzwDOzu7MusUFBSgoKBA+Ts3N9ecZhJRFbl69SoAYO/evdW2jPz8fGRkZMDb2xs2NjbVsozU1NRqmS/dm6qrj+Guv3biYO6JUuXK2RFcRUjjqus7UpP675gVRs6fP4/i4mK4uroalbu6uiItLa3c6Xfv3o0DBw5g8eLFt60XExODKVOmmNM0IqoGJcd1ZGSkyi2pGvb29mo3gcikmy/MWObIpuS56Nq4a50cWXNXR9MsXrwY7dq1K7Oza4mJEyciKipK+Ts3Nxeenp7V3TwiusWAAQMAAL6+vrC1ta2WZaSmpmLIkCFYsWIF/Pz8qmUZwI0g0qJFi2qbP9GduNcvzGhWGHF2doZOp0N2drZReXZ2Ntzcbt8BKi8vD6tWrcLUqVPLXY5er4derzenaURUDZydnTFixIi7siw/Pz8EBgbelWUR1TT3+sgms8KIlZUVgoKCkJCQoHxjMhgMSEhIwJgxY2477Zo1a1BQUIAhQ4ZUurFERER11b08ssnsn2mioqIQHh6Ojh07onPnzpg9ezby8vKU0TVDhw6Fh4cHYmJijKZbvHgxBgwYgIYNG1ZNy4mIiKhOMDuMhIWF4dy5c5g0aRKysrIQEBCA+Ph4pVPrqVOnoNUaXwQmPT0dv/zyCzZv3lw1rSYiIqI6o1IdWMeMGVPmzzLbtm0rVdaqVSvemp2IiIhMqtrr2BIRERGZiWGEiIiIVMUwQkRERKpiGCEiIiJVMYwQERGRqu7q5eBrsuq+IRhvBlZxdeHmbHVlX1DNUReOC4DHBpnGMPKPunRDsNp+MzDuC6LS6tJxAfDYIGMMI/+o7huC8WZgFVdXbs5WF/YF1Rx15bgAeGxQaQwj/7hbNwTjzcDKx5uzEZXG44LqMnZgJSIiIlUxjBAREZGqGEaIiIhIVQwjREREpCqGESIiIlIVwwgRERGpikN7iYiIzMCr4VY9hhEiIiIz8Gq4VY9hhIiIyAy8Gm7VYxghIiIyA6+GW/XYgZWIiIhUxTBCREREqmIYISIiIlUxjBAREZGqGEaIiIhIVQwjREREpCqGESIiIlIVwwgRERGpimGEiIiIVMUwQkRERKpiGCEiIiJVMYwQERGRqhhGiIiISFUMI0RERKQqhhEiIiJSFcMIERERqYphhIiIiFTFMEJERESqYhghIiIiVTGMEBERkaoYRoiIiEhVDCNERESkKoYRIiIiUlWlwkhsbCy8vb1hbW2NLl26YPfu3betf+nSJYwePRru7u7Q6/Vo2bIlNm7cWKkGExERUd1iYe4Eq1evRlRUFBYsWIAuXbpg9uzZCA0NRXp6OlxcXErVLywsRO/eveHi4oK1a9fCw8MDJ0+ehJOTU1W0n4iIiGo5s8PIrFmzEBkZiYiICADAggULsGHDBixZsgQTJkwoVX/JkiW4ePEidu3aBUtLSwCAt7f3nbWaiIiI6gyzwkhhYSGSkpIwceJEpUyr1aJXr15ITEw0Oc13332H4OBgjB49Gt9++y0aNWqEwYMH46233oJOpzM5TUFBAQoKCpS/c3NzzWkmEank6tWrSEtLM2ua1NRUo38rytfXF7a2tmZNc68xd39wX5BazAoj58+fR3FxMVxdXY3KXV1dy3zBHz9+HFu2bMFzzz2HjRs34ujRoxg1ahSuX7+O6Ohok9PExMRgypQp5jSNiGqAtLQ0BAUFVWraIUOGmFU/KSkJgYGBlVrWvaKy+4P7gu42s3+mMZfBYICLiwsWLlwInU6HoKAg/PXXX5gxY0aZYWTixImIiopS/s7NzYWnp2d1N5WI7pCvry+SkpLMmiY/Px8ZGRnw9vaGjY2NWcui2zN3f3BfkFrMCiPOzs7Q6XTIzs42Ks/Ozoabm5vJadzd3WFpaWn0k4yfnx+ysrJQWFgIKyurUtPo9Xro9XpzmkZENYCtrW2lviGHhIRUQ2uoMvuD+4LUYNbQXisrKwQFBSEhIUEpMxgMSEhIQHBwsMlpQkJCcPToURgMBqXs8OHDcHd3NxlEiIiI6N5i9nVGoqKisGjRInz++edITU3FyJEjkZeXp4yuGTp0qFEH15EjR+LixYsYN24cDh8+jA0bNuCDDz7A6NGjq24tiIiIqNYyu89IWFgYzp07h0mTJiErKwsBAQGIj49XOrWeOnUKWu3/Mo6npyc2bdqE1157De3bt4eHhwfGjRuHt956q+rWgu5pHMFBRFS7VaoD65gxYzBmzBiTz23btq1UWXBwMH799dfKLIqoXBzBQURUu1X7aBqi6sYRHEREtRvDCNV6HMFBRFS78a69REREpCqGESIiIlIVwwgRERGpimGEiIiIVMUwQkRERKpiGCEiIiJVMYwQERGRqhhGiIiISFUMI0RERKQqhhEiIiJSFcMIERERqYphhIiIiFTFG+VV0tWrV5GWllbh+qmpqUb/msPX1xe2trZmT0dERFQbMIxUUlpaGoKCgsyebsiQIWZPk5SUVKm70hIREdUGDCOV5Ovri6SkpArXz8/PR0ZGBry9vWFjY2P2soiIiOoqhpFKsrW1NftsRUhISDW1hoiIqPZiGCEiIhQXF2PHjh3IzMyEu7s7unXrBp1Op3az6B7B0TRERPe4uLg4+Pj4oGfPnhg8eDB69uwJHx8fxMXFqd00ukcwjBAR3cPi4uLw1FNPoV27dkhMTMTly5eRmJiIdu3a4amnnmIgobtCIyKidiPKk5ubC0dHR+Tk5MDBwUHt5hAR1QnFxcXw8fFBu3bt8M0330Cr/d/3U4PBgAEDBuDAgQM4cuQIf7K5y/bu3YugoKBaP5qyop/f7DNCRHSP2rFjBzIyMvDVV18ZBREA0Gq1mDhxIrp27YodO3agR48e6jSyjuC1qW6PYYSI6B6VmZkJAGjbtq3J50vKS+pR5fHaVLfHMEJEdI9yd3cHABw4cAD3339/qecPHDhgVI8qj9emuj32GSEiukexzwhVt4p+fnM0DRHRPUqn02HmzJn44YcfMGDAAKPRNAMGDMAPP/yAjz76iEGEqh1/piEiuocNHDgQa9euxeuvv46uXbsq5U2bNsXatWsxcOBAFVtH9wr+TENERLwCK1ULDu0lIqIK0+l0HL5LqmGfESIiIlIVwwgRERGpimGEiIiIVMU+I0RERDXIvdiZmGdGiIiIaoi4uDj4+PigZ8+eGDx4MHr27AkfH586f/dkhhEiIqIaIC4uDk899RTatWtndAG6du3a4amnnqrTgYTXGSEiIlJZXb00Py8HT0REVEvs2LEDGRkZePvtt42CCABotVpMnDgRJ06cwI4dO1RqYfViGCEiIlJZZmYmAKBt27Ymny8pL6lX1zCMEBERqczd3R0AcODAAZPPl5SX1KtrGEaIiIhU1q1bN3h7e+ODDz6AwWAwes5gMCAmJgZNmzZFt27dVGph9apUGImNjYW3tzesra3RpUsX7N69u8y6y5Ytg0ajMXpYW1tXusFEd6q4uBjbtm3DV199hW3btqG4uFjtJhHRPU6n02HmzJn44YcfMGDAAKPRNAMGDMAPP/yAjz76qFZ1XjWH2WFk9erViIqKQnR0NPbu3Qt/f3+Ehobi7NmzZU7j4OCAzMxM5XHy5Mk7ajRRZd2rY/iJqOYbOHAg1q5di/3796Nr165wcHBA165dceDAAaxduxYDBw5Uu4nVxuwwMmvWLERGRiIiIgKtW7fGggULYGtriyVLlpQ5jUajgZubm/JwdXW9o0YTVca9PIafiGqHgQMH4ujRo9i6dSu+/PJLbN26FUeOHKnTQQQw8zojhYWFsLW1xdq1azFgwAClPDw8HJcuXcK3335bapply5ZhxIgR8PDwgMFgQGBgID744AO0adOmwo3kdUboTtXVMfxERDVZtVxn5Pz58yguLi51ZsPV1RVZWVkmp2nVqhWWLFmCb7/9FitWrIDBYEDXrl3x559/lrmcgoIC5ObmGj2I7sS9PoafiKgmq/bRNMHBwRg6dCgCAgLQvXt3xMXFoVGjRvj000/LnCYmJgaOjo7Kw9PTs7qbSXXcvT6Gn4ioJjMrjDg7O0On0yE7O9uoPDs7G25ubhWah6WlJTp06ICjR4+WWWfixInIyclRHqdPnzanmUSl3Otj+ImIajKzwoiVlRWCgoKQkJCglBkMBiQkJCA4OLhC8yguLsb+/ftv+6av1+vh4OBg9CC6E/f6GH4ioprM7J9poqKisGjRInz++edITU3FyJEjkZeXh4iICADA0KFDMXHiRKX+1KlTsXnzZhw/fhx79+7FkCFDcPLkSYwYMaLq1oKoHPf6GH4ioprMwtwJwsLCcO7cOUyaNAlZWVkICAhAfHy80qn11KlTRh0E//77b0RGRiIrKwv169dHUFAQdu3ahdatW1fdWhBVQMkY/tdffx1du3ZVyps2bVrnx/ATEdVkZg3tVQuH9lJVKi4uxo4dO5CZmQl3d3d069aNZ0SIiKpBRT+/zT4zQlTb6XQ69OjRQ+1mEBHRP3ijPCIiIlIVwwgRERGpimGEiIiIVMUwQkRERKpiGCEiIiJVMYwQERGRqhhGiIiISFUMI0RERKQqhhEiIiJSFcMIERERqYphhIiIiFTFMEJERESqYhghIiIiVTGMEBERkaoYRoiIiEhVDCNERESkKoYRIiIiUhXDCBEREamKYYSIiIhUxTBCREREqmIYISIiIlUxjBAREZGqGEaIiIhIVQwjREREpCqGESIiIlIVwwgRERGpimGEiIiIVMUwQkRERKpiGCEiIiJVMYwQERGRqhhGiIiISFUMI0RERKQqhhEiIiJSFcMIERERqYphhIiIiFTFMEJERESqYhghIiIiVTGMEBERkaoYRoiIiEhVDCNERESkKoYRIiIiUlWlwkhsbCy8vb1hbW2NLl26YPfu3RWabtWqVdBoNBgwYEBlFktERER1kNlhZPXq1YiKikJ0dDT27t0Lf39/hIaG4uzZs7edLiMjA2+88Qa6detW6cYSERFR3WN2GJk1axYiIyMRERGB1q1bY8GCBbC1tcWSJUvKnKa4uBjPPfccpkyZgmbNmt1Rg4mIiKhuMSuMFBYWIikpCb169frfDLRa9OrVC4mJiWVON3XqVLi4uGD48OEVWk5BQQFyc3ONHkRERFQ3mRVGzp8/j+LiYri6uhqVu7q6Iisry+Q0v/zyCxYvXoxFixZVeDkxMTFwdHRUHp6enuY0k4iIiGqRah1Nc/nyZTz//PNYtGgRnJ2dKzzdxIkTkZOTozxOnz5dja0kIiIiNVmYU9nZ2Rk6nQ7Z2dlG5dnZ2XBzcytV/9ixY8jIyED//v2VMoPBcGPBFhZIT09H8+bNS02n1+uh1+vNaRoRERHVUmadGbGyskJQUBASEhKUMoPBgISEBAQHB5eq7+vri/379yMlJUV5PPbYY+jZsydSUlL48wsRERGZd2YEAKKiohAeHo6OHTuic+fOmD17NvLy8hAREQEAGDp0KDw8PBATEwNra2u0bdvWaHonJycAKFVORERE9yazw0hYWBjOnTuHSZMmISsrCwEBAYiPj1c6tZ46dQpaLS/sSkRERBWjERFRuxHlyc3NhaOjI3JycuDg4KB2c4iIiKgCKvr5zVMYREREpCqGESIiIlIVwwgRERGpimGEiIiIVMUwQkRERKpiGCEiIiJVMYwQERGRqhhGiIiISFUMI0RERKQqhhEiIiJSFcMIERERqYphhIiIiFTFMEJERESqYhghIiIiVTGMEBERkaoYRoiIiEhVDCNERESkKoYRIiIiUhXDCBEREamKYYSIiIhUxTBCREREqmIYISIiIlUxjBAREZGqGEaIiIhIVQwjREREpCqGESIiIlIVwwgRERGpimGEiIiIVMUwQkRERKpiGCEiIiJVMYwQERGRqhhGiIiISFUMI0RERKQqhhEiIiJSFcMIERERqYphhIiIiFTFMEJERESqYhghIiIiVTGMEBERkaoYRoiIiEhVDCNERESkKoYRIiIiUlWlwkhsbCy8vb1hbW2NLl26YPfu3WXWjYuLQ8eOHeHk5AQ7OzsEBARg+fLllW4wERER1S1mh5HVq1cjKioK0dHR2Lt3L/z9/REaGoqzZ8+arN+gQQO88847SExMxB9//IGIiAhERERg06ZNd9x4IiIiqv00IiLmTNClSxd06tQJn3zyCQDAYDDA09MTr7zyCiZMmFCheQQGBuLRRx/Fe++9V6H6ubm5cHR0RE5ODhwcHMxpLhEREamkop/fFubMtLCwEElJSZg4caJSptVq0atXLyQmJpY7vYhgy5YtSE9Px/Tp08usV1BQgIKCAuXvnJwcADdWioiIiGqHks/t8s57mBVGzp8/j+LiYri6uhqVu7q6Ii0trczpcnJy4OHhgYKCAuh0OsybNw+9e/cus35MTAymTJlSqtzT09Oc5hIREVENcPnyZTg6Opb5vFlhpLLs7e2RkpKCK1euICEhAVFRUWjWrBl69Ohhsv7EiRMRFRWl/G0wGHDx4kU0bNgQGo3mbjS5yuXm5sLT0xOnT5/mT001APdHzcF9UXNwX9QcdWVfiAguX76Mxo0b37aeWWHE2dkZOp0O2dnZRuXZ2dlwc3MrczqtVgsfHx8AQEBAAFJTUxETE1NmGNHr9dDr9UZlTk5O5jS1xnJwcKjVL6y6hvuj5uC+qDm4L2qOurAvbndGpIRZo2msrKwQFBSEhIQEpcxgMCAhIQHBwcEVno/BYDDqE0JERET3LrN/pomKikJ4eDg6duyIzp07Y/bs2cjLy0NERAQAYOjQofDw8EBMTAyAG/0/OnbsiObNm6OgoAAbN27E8uXLMX/+/KpdEyIiIqqVzA4jYWFhOHfuHCZNmoSsrCwEBAQgPj5e6dR66tQpaLX/O+GSl5eHUaNG4c8//4SNjQ18fX2xYsUKhIWFVd1a1AJ6vR7R0dGlfn4idXB/1BzcFzUH90XNca/tC7OvM0JERERUlXhvGiIiIlIVwwgRERGpimGEiIiIVMUwUo7JkycjICBA7WbQHRg2bBgGDBigdjOI7phGo8E333xT4frbtm2DRqPBpUuXqq1NRFXhngwjiYmJ0Ol0ePTRR6tl/t7e3tBoNNBoNNDpdGjcuDGGDx+Ov//+u1qWZ0pNfhPKysrCuHHj4OPjA2tra7i6uiIkJATz58/H1atXq335w4YNU/aPRqNBw4YN0adPH/zxxx/VvuybmfvBcrdkZWXhlVdeQbNmzaDX6+Hp6Yn+/fsbXV/odpYtW2byIoU9evQw2u6urq54+umncfLkySpeg7JlZGRAo9EgJSXlri3TXLcLz5mZmXjkkUeqdHm3+8KVnJyMsLAwuLu7Q6/Xw8vLC/369cP333+v3GukZJuWPKysrODj44Np06YZ3Y9k8uTJ0Gg06NOnT6nlzJgxAxqNpswLYdYExcXF6Nq1KwYOHGhUnpOTA09PT7zzzjtK2bp16/Dggw+ifv36sLGxQatWrfDCCy8gOTlZqbNs2TKj7VavXj0EBQUhLi7urq0TcOO4fPXVV+/qMk25J8PI4sWL8corr2D79u04c+ZMtSxj6tSpyMzMxKlTp7By5Ups374dY8eOrZZl1SbHjx9Hhw4dsHnzZnzwwQdITk5GYmIixo8fjx9++AE///yzyemuX79epe3o06cPMjMzkZmZiYSEBFhYWKBfv35VuozaKCMjA0FBQdiyZQtmzJiB/fv3Iz4+Hj179sTo0aPveP6RkZHIzMzEmTNn8O233+L06dMYMmRIFbT83uDm5nbXhnp+++23uP/++3HlyhV8/vnnSE1NRXx8PJ544gm8++67yg1MS/z888/IzMzEkSNHMGXKFLz//vtYsmSJUR13d3ds3boVf/75p1H5kiVLcN9991X7Ot0JnU6HZcuWIT4+HitXrlTKX3nlFTRo0ADR0dEAgLfeegthYWEICAjAd999h/T0dHz55Zdo1qyZ0U1mgRtXVy15H0pOTkZoaCgGDRqE9PT0u7puNYLcYy5fviz16tWTtLQ0CQsLk/fff9/o+ZiYGHFxcZF69erJCy+8IG+99Zb4+/srz+/evVt69eolDRs2FAcHB3nggQckKSnJaB5eXl7y8ccfG5W999570rp1a6OytWvXSuvWrcXKykq8vLzko48+Mnr+4sWL8vzzz4uTk5PY2NhInz595PDhw8rzGRkZ0q9fP3FychJbW1tp3bq1bNiwQU6cOCEAjB7h4eGV32hVKDQ0VJo0aSJXrlwx+bzBYBAREQAyb9486d+/v9ja2kp0dLQUFRXJCy+8IN7e3mJtbS0tW7aU2bNnG01fVFQkr732mjg6OkqDBg3kzTfflKFDh8rjjz+u1AkPDzf6W0Rkx44dAkDOnj2rlP3xxx/Ss2dPsba2lgYNGkhkZKRcvnxZeb64uFimTJkiHh4eYmVlJf7+/vLjjz8qzxcUFMjo0aPFzc1N9Hq93HffffLBBx+IyI3XyM37x8vLqzKbs8o98sgj4uHhYXL//P333yIiMnPmTGnbtq3Y2tpKkyZNZOTIkcp22bp1a6nXXnR0tIiIdO/eXcaNG2c0z+XLl4utra1R2bZt26RTp05iZWUlbm5u8tZbb8n169eV569duyavvPKKNGrUSPR6vYSEhMju3buV5y9evCiDBw8WZ2dnsba2Fh8fH1myZImISKm2de/e/Q63WNUz9fosAUDWr1+v/L1z507x9/cXvV4vQUFBsn79egEgycnJIvK//fHzzz9LUFCQ2NjYSHBwsKSlpYmIyNKlS0ttk6VLl8qVK1ekYcOG8sQTT5TZzpJjteT9pmSZJR566CEZNWqU8nd0dLT4+/tLv379ZNq0aUbr4OzsLCNHjqyR++NWc+bMkfr168uZM2fkm2++EUtLS0lJSRERkcTERAEgc+bMMTltyTYTubHtHR0djZ4vLi4WS0tL+frrr5Wy8j4HRMr/LImNjRUfHx/R6/Xi4uIiTz75pIjceK3duv9PnDhR2U1zR+65MLJ48WLp2LGjiIh8//330rx5c+UFsnr1atHr9fLZZ59JWlqavPPOO2Jvb28URhISEmT58uWSmpoqhw4dkuHDh4urq6vk5uYqdW4NI3/++ad07txZIiIilLLff/9dtFqtTJ06VdLT02Xp0qViY2MjS5cuVeo89thj4ufnJ9u3b5eUlBQJDQ0VHx8fKSwsFBGRRx99VHr37i1//PGHHDt2TL7//nv573//K0VFRbJu3ToBIOnp6ZKZmSmXLl2qhq1pnvPnz4tGo5GYmJhy6wIQFxcXWbJkiRw7dkxOnjwphYWFMmnSJNmzZ48cP35cVqxYIba2trJ69WpluunTp0v9+vVl3bp1yv6xt7e/bRi5fPmyvPTSS+Lj4yPFxcUiInLlyhVxd3eXgQMHyv79+yUhIUGaNm1qFOpmzZolDg4O8tVXX0laWpqMHz9eLC0tlTeKGTNmiKenp2zfvl0yMjJkx44d8uWXX4qIyNmzZ5U3/szMTKMQpJYLFy6IRqNRAlNZPv74Y9myZYucOHFCEhISpFWrVjJy5EgRuRHAZs+eLQ4ODpKZmSmZmZlKULk1jFy4cEH69+8vPXv2VMr+/PNPsbW1lVGjRklqaqqsX79enJ2dlUAjIjJ27Fhp3LixbNy4UQ4ePCjh4eFSv359uXDhgoiIjB49WgICAmTPnj1y4sQJ+emnn+S7774TkRtfJko+nDMzM5VpapKKhpGcnBxp0KCBDBkyRA4ePCgbN26Uli1bmgwjXbp0kW3btsnBgwelW7du0rVrVxERuXr1qrz++uvSpk0bZX9dvXpV4uLiBIAkJiaW215TYWTPnj3i5OQkn3/+uVJWEkbi4uLEx8dHKR8+fLiMGzdOxo0bVyvCiMFgkB49eshDDz0kLi4u8t577ynPjR07VurVq2cUnstyaxgpKiqSJUuWiKWlpRw9elQpL+9zoLzPkj179ohOp5Mvv/xSMjIyZO/evUpYunTpkgQHB0tkZKSy/4uKiqpgK5nvngsjXbt2Vb5NX79+XZydnWXr1q0iIhIcHGyU5EVEunTpYhRGblVcXCz29vby/fffK2VeXl5iZWUldnZ2Ym1trbwZlHyzFBEZPHiw9O7d22heb775pnL25PDhwwJAdu7cqTx//vx5sbGxUVJzu3btZPLkySbbVfImdPMy1fbrr78KAImLizMqb9iwodjZ2YmdnZ2MHz9eRG686b766qvlznP06NFKyhcRcXd3l//85z/K39evX5cmTZqUCiM6nU5ZJgBxd3c3OsO1cOFCqV+/vtEZgg0bNohWq5WsrCwREWncuHGpM2udOnVSXkOvvPKKPPjgg0bfhm5267dctf32228m90951qxZIw0bNlT+NvWNT+RGGLG0tBQ7OzuxtbUVANKyZUujb2Jvv/22tGrVymibxcbGSr169aS4uFiuXLkilpaWsnLlSuX5wsJCady4sbLf+/fvbxT8b1bWt/iapKJhZP78+dKwYUPJz89Xnl+0aFGZZ0ZKbNiwQQAo05WEhJt9+OGHAkAuXryolO3evVs5Zuzs7JT3vJJtamNjI3Z2dmJpaSkA5MUXXzSaZ8lyCgsLxcXFRf773//KlStXxN7eXvbt21drwoiISGpqqgCQdu3aGQWPPn36SPv27Y3qzpw502i7lXwxLDkrVVKu1WpFr9cbfSGtyOdAeZ8l69atEwcHB6MvzDczdcZSDfdUn5H09HTs3r0bzz77LADAwsICYWFhWLx4MQAgNTUVXbp0MZrm1hsAZmdnIzIyEi1atICjoyMcHBxw5coVnDp1yqjem2++iZSUFPzxxx9Kx79HH30UxcXFyrJCQkKMpgkJCcGRI0dQXFyM1NRUWFhYGLWnYcOGaNWqFVJTUwEAY8eOxbRp0xASEoLo6Oi73gGzquzevRspKSlo06aN0Q0UO3bsWKpubGwsgoKC0KhRI9SrVw8LFy5Utn1OTg4yMzONtpmFhYXJ+fTs2RMpKSlISUnB7t27ERoaikceeUTpTJmamgp/f3/Y2dkp04SEhMBgMCA9PR25ubk4c+aMyX1Ysn+GDRuGlJQUtGrVCmPHjsXmzZvvYCtVP6ngxZh//vlnPPTQQ/Dw8IC9vT2ef/55XLhwoUKdj5977jmkpKRg3759+OWXX+Dj44OHH34Yly9fBnBjuwcHB0Oj0SjThISE4MqVK/jzzz9x7NgxXL9+3Wi7W1paonPnzsp2HzlyJFatWoWAgACMHz8eu3btMmcz1Brp6elo3749rK2tlbLOnTubrNu+fXvl/+7u7gCAs2fPmrW89u3bK8dMXl4eioqKjJ5fvXq1sm+//vprfPvtt5gwYUKp+VhaWmLIkCFYunQp1qxZg5YtWxq1rzZYsmQJbG1tceLEiVL9X271wgsvICUlBZ9++iny8vKMjjN7e3tlmyYnJ+ODDz7Ayy+/jO+//x4AKvQ5UN5nSe/eveHl5YVmzZrh+eefx8qVK+/KQAFz3VNhZPHixSgqKkLjxo1hYWEBCwsLzJ8/H+vWrSvVGass4eHhSElJwZw5c7Br1y6kpKSgYcOGKCwsNKrn7OwMHx8ftGjRAg8++CBmz56NXbt2YevWrVW2PiNGjMDx48fx/PPPY//+/ejYsSPmzp1bZfOvaj4+PtBoNKU6ZzVr1gw+Pj6wsbExKr85CADAqlWr8MYbb2D48OHYvHkzUlJSEBERUWrbV4SdnR18fHzg4+ODTp064bPPPkNeXh4WLVpk/oqVITAwECdOnMB7772H/Px8DBo0CE899VSVzb+qtWjRAhqNBmlpaWXWycjIQL9+/dC+fXusW7cOSUlJiI2NBYAK7QdHR0dlu4eEhGDx4sU4cuQIVq9eXWXrURIqX3vtNZw5cwYPPfQQ3njjjSqbf21kaWmp/L8k6BkMhjLrt2jRAgCMjlW9Xq/sO1M8PT3h4+MDPz8/PP3003j11Vcxc+ZMXLt2rVTdF154AWvWrEFsbCxeeOGFSq2TWnbt2oWPP/4YP/zwAzp37ozhw4crAaNFixY4fvy4UYd7Jycn+Pj4wMPDo9S8tFqtsk3bt2+PqKgo9OjRA9OnT6+y9trb22Pv3r346quv4O7ujkmTJsHf37/GjbS8Z8JIUVERvvjiC8ycOVNJoiUpvnHjxvjqq6/g5+eH3377zWi6X3/91ejvnTt3YuzYsejbty/atGkDvV6P8+fPl7t8nU4HAMjPzwcA+Pn5YefOnaXm3bJlS+h0Ovj5+aGoqMioPRcuXEB6ejpat26tlHl6euLll19GXFwcXn/9deXD1MrKCgCUMzE1QcOGDdG7d2988sknyMvLM3v6nTt3omvXrhg1ahQ6dOgAHx8fHDt2THne0dER7u7uRtusqKgISUlJ5c5bo9FAq9Ua7Z99+/YZtXPnzp3QarVo1aoVHBwc0LhxY5P78Ob94+DggLCwMCxatAirV6/GunXrcPHiRQA3PiBq0v5p0KABQkNDERsba3L/XLp0CUlJSTAYDJg5cybuv/9+tGzZstSINCsrqwqvl6njIjEx0ejb486dO2Fvb48mTZqgefPmsLKyMtru169fx549e4y2e6NGjRAeHo4VK1Zg9uzZWLhwodI2oGYdF5XVqlUr7N+/3+hs4p49e8yej6n99fDDD6NBgwZ39KGo0+lQVFRkMqS2adMGbdq0wYEDBzB48OBKL+Nuu3r1KoYNG4aRI0eiZ8+eWLx4MXbv3o0FCxYAAJ599llcuXIF8+bNq/QydDqd0fFQ3udAeZ8lwI0zxL169cJ//vMf/PHHH8jIyMCWLVsAmHe8Vit1fyW6e9avXy9WVlYmO3KOHz9eOnbsKKtWrRJra2tZsmSJpKeny6RJk0p1YO3QoYP07t1bDh06JL/++qt069ZNbGxsjDqsenl5ydSpUyUzM1POnDkjv/32m3Tv3l0aNWok58+fFxGRpKQko05Hy5YtK9WB9fHHH5fWrVvLjh07JCUlRfr06WPUcWncuHESHx8vx48fl6SkJOnSpYsMGjRIRG50BNRoNLJs2TI5e/as0SgQNR09elRcXV3F19dXVq1aJYcOHZK0tDRZvny5uLq6SlRUlIiY7k8xZ84ccXBwkPj4eElPT5d3331XHBwcjPbPhx9+KA0aNJD169dLamqqREZGmuzA2qdPH6XD1qFDh2TUqFGi0WiU/kN5eXni7u4uTz75pOzfv1+2bNkizZo1M+rA+vHHH4uDg4OsWrVK0tLS5K233jLqwDpz5kz58ssvJTU1VdLT02X48OHi5uamdJJt0aKFjBw5UjIzM41+m1fTsWPHxM3NTVq3bi1r166Vw4cPy6FDh2TOnDni6+srKSkpAkBmz54tx44dky+++EI8PDyM+ift3LlT6adw7tw5ycvLE5Ebv03f3FEuJSVFnnzySbG2tlZGd5R0YB09erSkpqbKN998U6oD67hx46Rx48by448/GnVgLdmG//73v+Wbb76RI0eOyIEDB6Rfv37SuXNnEbnRh8jGxkamTZsmWVlZNaJj963Cw8OlR48ekpycbPQ4deqUyQ6sQ4cOlUOHDkl8fLz4+voKAGV0h6m+Y8nJyUajJlauXCl2dnaSnJws586dk2vXromISFxcnFhaWkrfvn0lPj5ejh07Jvv27ZPp06cLAKVTcEmfkZJOwadPn5aNGzeKh4eHUefkW/umXLlyxahdtaHPyNixY8XHx0d5TYuILFiwQOrVq6dsz9dff110Op289tprsmPHDsnIyJDExEQZMmSIaDQaycnJEZEbfUZu7uh9/Phx+fTTT0Wn08mUKVOU+Zf3OVDeZ8n3338vc+bMkeTkZMnIyJB58+aJVquVAwcOiIhIZGSkdOrUSU6cOCHnzp1T3p/utnsmjPTr10/69u1r8rmSjnv79u2T999/X5ydnaVevXoSHh4u48ePNzqA9u7dKx07dhRra2tp0aKFrFmzptTomVuHbTZq1Ej69u1bqtNcyXAsS0tLue+++2TGjBlGz5cM6XJ0dBQbGxsJDQ01GtI1ZswYad68uej1emnUqJE8//zzStgREZk6daq4ubmJRqOpMUN7RUTOnDkjY8aMkaZNm4qlpaXUq1dPOnfuLDNmzFAOclNh5Nq1azJs2DBxdHQUJycnGTlypEyYMMFo/1y/fl3GjRsnDg4O4uTkJFFRUSaH9t68f+zt7aVTp06ydu1ao+VVZGjv5MmTxcPDQywtLUsN7V24cKEEBASInZ2dODg4yEMPPSR79+5Vnv/uu+/Ex8dHLCwsaszQXpEb+2f06NFKR2wPDw957LHHlKA2a9YscXd3V16TX3zxRakPvJdfflkaNmxYamjvzdu9fv360r17d9myZYvR8ssb2pufny+vvPKKODs7mxza+95774mfn5/Y2NhIgwYN5PHHH5fjx48rzy9atEg8PT1Fq9XWyA8/U8MtAcjw4cNNDu1t3769WFlZSVBQkHz55ZcCQAl3FQkj165dkyeffFKcnJyUEV4l9uzZI0899ZS4uLiIhYWFNGzYUEJDQ2XVqlWlhvaWPHQ6nTRp0kQiIyONRomZ6ih7s5oeRrZt2yY6nU527NhR6rmHH37YqLP66tWrpUePHuLo6CiWlpbSpEkTGTx4sPz666/KNLcOq9br9dKyZUt5//33jUa0lPc5IHL7z5IdO3ZI9+7dpX79+mJjYyPt27c3GoGYnp4u999/v9jY2Kg6tFcjUsFea0REVKOtXLkSERERyMnJKdUHi6gms1C7AUREVDlffPEFmjVrBg8PD+zbtw9vvfUWBg0axCBCtQ7DCBFRLZWVlYVJkyYhKysL7u7uePrpp/H++++r3Swis/FnGiIiIlLVPTO0l4iIiGomhhEiIiJSFcMIERERqYphhIiIiFTFMEJERESqYhghIiIiVTGMEBERkaoYRoiIiEhVDCNERESkqv8HJfNd9PTsvEYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# algorithm comparison\n",
        "\n",
        "fig = plt.figure()\n",
        "fig.suptitle('Comparison between different Liver scores')\n",
        "ax = fig.add_subplot(111)\n",
        "plt.ylim(0.3, 1)\n",
        "plt.boxplot(liver_scores, showmeans=True)\n",
        "ax.set_xticklabels(model_names)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 177,
      "metadata": {
        "id": "Bj67LjWXSJ-e"
      },
      "outputs": [],
      "source": [
        "Algo_results['Liver'] = liver_mean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 256,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "3TSUzfSsoHS3",
        "outputId": "01800512-d571-4bb7-c69f-6851784ce658"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Names       Wine  Breast_Cancer      Sonar  Ionosphere       Bupa  \\\n",
              "0   AdaBoost  89.830065      95.800512  83.140476   93.027778  72.254622   \n",
              "1  GradBoost  95.071895      96.515772  84.185714   93.055556  72.410924   \n",
              "2   CatBoost  97.977124      97.087170  87.152381   93.453175  74.403361   \n",
              "3   LightGBM  97.421569      96.632992  88.123810   93.706349  71.801681   \n",
              "4    XGBoost  96.676471      96.429028  85.007143   92.430159  70.579832   \n",
              "\n",
              "        Pima      Heart  TicTacToe      Liver  \n",
              "0  75.118592  79.814815  77.381469  72.254622  \n",
              "1  76.183869  80.074074  92.109430  72.293277  \n",
              "2  76.249316  81.888889  99.884868  74.403361  \n",
              "3  73.985133  80.888889  99.112719  71.801681  \n",
              "4  73.842447  80.370370  98.810307  70.579832  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-957f1c8d-9338-44b5-aca0-20563293cf5c\" 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>Names</th>\n",
              "      <th>Wine</th>\n",
              "      <th>Breast_Cancer</th>\n",
              "      <th>Sonar</th>\n",
              "      <th>Ionosphere</th>\n",
              "      <th>Bupa</th>\n",
              "      <th>Pima</th>\n",
              "      <th>Heart</th>\n",
              "      <th>TicTacToe</th>\n",
              "      <th>Liver</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AdaBoost</td>\n",
              "      <td>89.830065</td>\n",
              "      <td>95.800512</td>\n",
              "      <td>83.140476</td>\n",
              "      <td>93.027778</td>\n",
              "      <td>72.254622</td>\n",
              "      <td>75.118592</td>\n",
              "      <td>79.814815</td>\n",
              "      <td>77.381469</td>\n",
              "      <td>72.254622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GradBoost</td>\n",
              "      <td>95.071895</td>\n",
              "      <td>96.515772</td>\n",
              "      <td>84.185714</td>\n",
              "      <td>93.055556</td>\n",
              "      <td>72.410924</td>\n",
              "      <td>76.183869</td>\n",
              "      <td>80.074074</td>\n",
              "      <td>92.109430</td>\n",
              "      <td>72.293277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CatBoost</td>\n",
              "      <td>97.977124</td>\n",
              "      <td>97.087170</td>\n",
              "      <td>87.152381</td>\n",
              "      <td>93.453175</td>\n",
              "      <td>74.403361</td>\n",
              "      <td>76.249316</td>\n",
              "      <td>81.888889</td>\n",
              "      <td>99.884868</td>\n",
              "      <td>74.403361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LightGBM</td>\n",
              "      <td>97.421569</td>\n",
              "      <td>96.632992</td>\n",
              "      <td>88.123810</td>\n",
              "      <td>93.706349</td>\n",
              "      <td>71.801681</td>\n",
              "      <td>73.985133</td>\n",
              "      <td>80.888889</td>\n",
              "      <td>99.112719</td>\n",
              "      <td>71.801681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>96.676471</td>\n",
              "      <td>96.429028</td>\n",
              "      <td>85.007143</td>\n",
              "      <td>92.430159</td>\n",
              "      <td>70.579832</td>\n",
              "      <td>73.842447</td>\n",
              "      <td>80.370370</td>\n",
              "      <td>98.810307</td>\n",
              "      <td>70.579832</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-957f1c8d-9338-44b5-aca0-20563293cf5c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-957f1c8d-9338-44b5-aca0-20563293cf5c 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-957f1c8d-9338-44b5-aca0-20563293cf5c');\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-4f6f2993-d2fa-48d3-89ca-42da33566f72\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4f6f2993-d2fa-48d3-89ca-42da33566f72')\"\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-4f6f2993-d2fa-48d3-89ca-42da33566f72 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 256
        }
      ],
      "source": [
        "Algo_results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 257,
      "metadata": {
        "id": "tZqkWBmLr1tu"
      },
      "outputs": [],
      "source": [
        "Algo_results_tr = Algo_results.set_index('Names').T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 258,
      "metadata": {
        "id": "SMnbVxHmvY2z"
      },
      "outputs": [],
      "source": [
        "Algo_results_tr.to_csv('/content/drive/MyDrive/DatasetSeminario/Algo_results/AlgoResults.csv', index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 259,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "4P4vCHsKxCss",
        "outputId": "2bbb61af-1d32-4631-f55f-e9e85c7c6ebf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Names           AdaBoost  GradBoost   CatBoost   LightGBM    XGBoost\n",
              "Wine           89.830065  95.071895  97.977124  97.421569  96.676471\n",
              "Breast_Cancer  95.800512  96.515772  97.087170  96.632992  96.429028\n",
              "Sonar          83.140476  84.185714  87.152381  88.123810  85.007143\n",
              "Ionosphere     93.027778  93.055556  93.453175  93.706349  92.430159\n",
              "Bupa           72.254622  72.410924  74.403361  71.801681  70.579832\n",
              "Pima           75.118592  76.183869  76.249316  73.985133  73.842447\n",
              "Heart          79.814815  80.074074  81.888889  80.888889  80.370370\n",
              "TicTacToe      77.381469  92.109430  99.884868  99.112719  98.810307\n",
              "Liver          72.254622  72.293277  74.403361  71.801681  70.579832"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-63ae200a-5cd8-40db-9ddc-3c82fe7b91f2\" 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>Names</th>\n",
              "      <th>AdaBoost</th>\n",
              "      <th>GradBoost</th>\n",
              "      <th>CatBoost</th>\n",
              "      <th>LightGBM</th>\n",
              "      <th>XGBoost</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Wine</th>\n",
              "      <td>89.830065</td>\n",
              "      <td>95.071895</td>\n",
              "      <td>97.977124</td>\n",
              "      <td>97.421569</td>\n",
              "      <td>96.676471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Breast_Cancer</th>\n",
              "      <td>95.800512</td>\n",
              "      <td>96.515772</td>\n",
              "      <td>97.087170</td>\n",
              "      <td>96.632992</td>\n",
              "      <td>96.429028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sonar</th>\n",
              "      <td>83.140476</td>\n",
              "      <td>84.185714</td>\n",
              "      <td>87.152381</td>\n",
              "      <td>88.123810</td>\n",
              "      <td>85.007143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ionosphere</th>\n",
              "      <td>93.027778</td>\n",
              "      <td>93.055556</td>\n",
              "      <td>93.453175</td>\n",
              "      <td>93.706349</td>\n",
              "      <td>92.430159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bupa</th>\n",
              "      <td>72.254622</td>\n",
              "      <td>72.410924</td>\n",
              "      <td>74.403361</td>\n",
              "      <td>71.801681</td>\n",
              "      <td>70.579832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pima</th>\n",
              "      <td>75.118592</td>\n",
              "      <td>76.183869</td>\n",
              "      <td>76.249316</td>\n",
              "      <td>73.985133</td>\n",
              "      <td>73.842447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Heart</th>\n",
              "      <td>79.814815</td>\n",
              "      <td>80.074074</td>\n",
              "      <td>81.888889</td>\n",
              "      <td>80.888889</td>\n",
              "      <td>80.370370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TicTacToe</th>\n",
              "      <td>77.381469</td>\n",
              "      <td>92.109430</td>\n",
              "      <td>99.884868</td>\n",
              "      <td>99.112719</td>\n",
              "      <td>98.810307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Liver</th>\n",
              "      <td>72.254622</td>\n",
              "      <td>72.293277</td>\n",
              "      <td>74.403361</td>\n",
              "      <td>71.801681</td>\n",
              "      <td>70.579832</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-63ae200a-5cd8-40db-9ddc-3c82fe7b91f2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      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-63ae200a-5cd8-40db-9ddc-3c82fe7b91f2 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-63ae200a-5cd8-40db-9ddc-3c82fe7b91f2');\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-bc7ec689-5a25-46ef-b1c3-47a7c7b1115c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bc7ec689-5a25-46ef-b1c3-47a7c7b1115c')\"\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-bc7ec689-5a25-46ef-b1c3-47a7c7b1115c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 259
        }
      ],
      "source": [
        "Algo_results_tr"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Algo_results_std['Liver'] = liver_std"
      ],
      "metadata": {
        "id": "5SlB-Sm6zBK4"
      },
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Algo_results_std"
      ],
      "metadata": {
        "id": "0U8Di7Sgzk5Z",
        "outputId": "c6f4bfd8-28d6-4cbf-b0e5-b24dd8b9e1a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Names      Wine  Breast_Cancer     Sonar  Ionosphere      Bupa  \\\n",
              "0   AdaBoost  8.935178       2.542428  7.844412    4.476252  7.026728   \n",
              "1  GradBoost  4.689441       2.515754  8.340078    4.859172  6.671652   \n",
              "2   CatBoost  3.231395       2.223046  8.016771    4.589217  6.450640   \n",
              "3   LightGBM  3.206245       2.373965  7.246884    4.414604  6.215299   \n",
              "4    XGBoost  4.235288       2.256039  8.247342    5.314318  6.773062   \n",
              "\n",
              "       Pima     Heart  TicTacToe     Liver  \n",
              "0  5.077771  6.139105   4.415847  7.026728  \n",
              "1  4.805912  7.177545   3.069869  6.574580  \n",
              "2  4.987578  6.807061   0.464991  6.450640  \n",
              "3  5.301429  7.252453   0.914159  6.215299  \n",
              "4  4.741009  6.594257   1.103353  6.773062  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ef6a6aa6-7744-47e8-bad3-6f2bb82096de\" 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>Names</th>\n",
              "      <th>Wine</th>\n",
              "      <th>Breast_Cancer</th>\n",
              "      <th>Sonar</th>\n",
              "      <th>Ionosphere</th>\n",
              "      <th>Bupa</th>\n",
              "      <th>Pima</th>\n",
              "      <th>Heart</th>\n",
              "      <th>TicTacToe</th>\n",
              "      <th>Liver</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AdaBoost</td>\n",
              "      <td>8.935178</td>\n",
              "      <td>2.542428</td>\n",
              "      <td>7.844412</td>\n",
              "      <td>4.476252</td>\n",
              "      <td>7.026728</td>\n",
              "      <td>5.077771</td>\n",
              "      <td>6.139105</td>\n",
              "      <td>4.415847</td>\n",
              "      <td>7.026728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GradBoost</td>\n",
              "      <td>4.689441</td>\n",
              "      <td>2.515754</td>\n",
              "      <td>8.340078</td>\n",
              "      <td>4.859172</td>\n",
              "      <td>6.671652</td>\n",
              "      <td>4.805912</td>\n",
              "      <td>7.177545</td>\n",
              "      <td>3.069869</td>\n",
              "      <td>6.574580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CatBoost</td>\n",
              "      <td>3.231395</td>\n",
              "      <td>2.223046</td>\n",
              "      <td>8.016771</td>\n",
              "      <td>4.589217</td>\n",
              "      <td>6.450640</td>\n",
              "      <td>4.987578</td>\n",
              "      <td>6.807061</td>\n",
              "      <td>0.464991</td>\n",
              "      <td>6.450640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LightGBM</td>\n",
              "      <td>3.206245</td>\n",
              "      <td>2.373965</td>\n",
              "      <td>7.246884</td>\n",
              "      <td>4.414604</td>\n",
              "      <td>6.215299</td>\n",
              "      <td>5.301429</td>\n",
              "      <td>7.252453</td>\n",
              "      <td>0.914159</td>\n",
              "      <td>6.215299</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>4.235288</td>\n",
              "      <td>2.256039</td>\n",
              "      <td>8.247342</td>\n",
              "      <td>5.314318</td>\n",
              "      <td>6.773062</td>\n",
              "      <td>4.741009</td>\n",
              "      <td>6.594257</td>\n",
              "      <td>1.103353</td>\n",
              "      <td>6.773062</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ef6a6aa6-7744-47e8-bad3-6f2bb82096de')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ef6a6aa6-7744-47e8-bad3-6f2bb82096de 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-ef6a6aa6-7744-47e8-bad3-6f2bb82096de');\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-9404a8c5-a18f-43a1-978c-9644f025b9b6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9404a8c5-a18f-43a1-978c-9644f025b9b6')\"\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-9404a8c5-a18f-43a1-978c-9644f025b9b6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 183
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Algo_results_std_tr = Algo_results_std.set_index('Names').T"
      ],
      "metadata": {
        "id": "eVsH1SJXzPDE"
      },
      "execution_count": 184,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Algo_results_std_tr.to_csv('/content/drive/MyDrive/DatasetSeminario/Algo_results/AlgoResultsStd.csv', index=False)"
      ],
      "metadata": {
        "id": "ywDDhi6xzXNc"
      },
      "execution_count": 235,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 186,
      "metadata": {
        "id": "gDYyrTbdDpYP"
      },
      "outputs": [],
      "source": [
        "Algo_time_results['Liver'] = pd.Series(execution_times)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 223,
      "metadata": {
        "id": "FBentUcKDpYP",
        "outputId": "612e66fd-e809-4ddc-e62b-c03add23aa4c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Names        Wine  Breast_Cancer       Sonar  Ionosphere       Bupa  \\\n",
              "0   AdaBoost   12.676296       8.138952   12.050752   11.089048   7.651754   \n",
              "1  GradBoost   35.455523      14.576946   27.724694   23.873473   7.717140   \n",
              "2   CatBoost  252.544651      65.529124  745.911721  545.454163  71.475763   \n",
              "3   LightGBM    3.015574       2.534867    2.904376    5.442254   1.480027   \n",
              "4    XGBoost    3.691690       5.288792    8.706552    8.399888   3.574911   \n",
              "\n",
              "         Pima      Heart   TicTacToe      Liver  \n",
              "0    9.096246   7.598711    8.260242   7.612116  \n",
              "1   14.597989   7.698237    9.802947   8.473189  \n",
              "2  123.902024  79.368919  430.675700  70.586465  \n",
              "3    3.410988   1.339058    3.518559   1.402952  \n",
              "4    8.648128   3.369851    5.656618   5.488013  "
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              "      <th></th>\n",
              "      <th>Names</th>\n",
              "      <th>Wine</th>\n",
              "      <th>Breast_Cancer</th>\n",
              "      <th>Sonar</th>\n",
              "      <th>Ionosphere</th>\n",
              "      <th>Bupa</th>\n",
              "      <th>Pima</th>\n",
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              "      <th>0</th>\n",
              "      <td>AdaBoost</td>\n",
              "      <td>12.676296</td>\n",
              "      <td>8.138952</td>\n",
              "      <td>12.050752</td>\n",
              "      <td>11.089048</td>\n",
              "      <td>7.651754</td>\n",
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              "      <td>7.612116</td>\n",
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              "      <th>1</th>\n",
              "      <td>GradBoost</td>\n",
              "      <td>35.455523</td>\n",
              "      <td>14.576946</td>\n",
              "      <td>27.724694</td>\n",
              "      <td>23.873473</td>\n",
              "      <td>7.717140</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CatBoost</td>\n",
              "      <td>252.544651</td>\n",
              "      <td>65.529124</td>\n",
              "      <td>745.911721</td>\n",
              "      <td>545.454163</td>\n",
              "      <td>71.475763</td>\n",
              "      <td>123.902024</td>\n",
              "      <td>79.368919</td>\n",
              "      <td>430.675700</td>\n",
              "      <td>70.586465</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LightGBM</td>\n",
              "      <td>3.015574</td>\n",
              "      <td>2.534867</td>\n",
              "      <td>2.904376</td>\n",
              "      <td>5.442254</td>\n",
              "      <td>1.480027</td>\n",
              "      <td>3.410988</td>\n",
              "      <td>1.339058</td>\n",
              "      <td>3.518559</td>\n",
              "      <td>1.402952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>3.691690</td>\n",
              "      <td>5.288792</td>\n",
              "      <td>8.706552</td>\n",
              "      <td>8.399888</td>\n",
              "      <td>3.574911</td>\n",
              "      <td>8.648128</td>\n",
              "      <td>3.369851</td>\n",
              "      <td>5.656618</td>\n",
              "      <td>5.488013</td>\n",
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              "  @keyframes spin {\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        document.querySelector('#df-bb423e4f-fc0e-490d-a74a-1f5068ce4280 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 223
        }
      ],
      "source": [
        "Algo_time_results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 188,
      "metadata": {
        "id": "qZa5td0fDpYP"
      },
      "outputs": [],
      "source": [
        "Algo_time_results_tr = Algo_results.set_index('Names').T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 189,
      "metadata": {
        "id": "utQkOOvbDpYP"
      },
      "outputs": [],
      "source": [
        "Algo_time_results_tr.to_csv('/content/drive/MyDrive/DatasetSeminario/Algo_results/AlgoTimeResults.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tbGLU51qj2Pz"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "su-CfOlHj7d2"
      },
      "source": [
        "# **Friedman's Test**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pa59ipyPoLri"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 190,
      "metadata": {
        "id": "4rYO6n391rtL",
        "outputId": "f0f6d434-5f54-4e3c-d7c0-9bd8ad7d8e6e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting scikit-posthocs\n",
            "  Downloading scikit_posthocs-0.7.0-py3-none-any.whl (38 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from scikit-posthocs) (1.23.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from scikit-posthocs) (1.11.2)\n",
            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.10/dist-packages (from scikit-posthocs) (0.14.0)\n",
            "Requirement already satisfied: pandas>=0.20.0 in /usr/local/lib/python3.10/dist-packages (from scikit-posthocs) (1.5.3)\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.10/dist-packages (from scikit-posthocs) (0.12.2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from scikit-posthocs) (3.7.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.20.0->scikit-posthocs) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.20.0->scikit-posthocs) (2023.3.post1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit-posthocs) (1.1.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit-posthocs) (0.11.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit-posthocs) (4.42.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit-posthocs) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit-posthocs) (23.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit-posthocs) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit-posthocs) (3.1.1)\n",
            "Requirement already satisfied: patsy>=0.5.2 in /usr/local/lib/python3.10/dist-packages (from statsmodels->scikit-posthocs) (0.5.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from patsy>=0.5.2->statsmodels->scikit-posthocs) (1.16.0)\n",
            "Installing collected packages: scikit-posthocs\n",
            "Successfully installed scikit-posthocs-0.7.0\n"
          ]
        }
      ],
      "source": [
        "!pip install scikit-posthocs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 191,
      "metadata": {
        "id": "4VNr0Yir1i4K"
      },
      "outputs": [],
      "source": [
        "import scikit_posthocs as sp\n",
        "from scipy.stats import friedmanchisquare"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 260,
      "metadata": {
        "id": "cJxO315dpo9G"
      },
      "outputs": [],
      "source": [
        "Algo_results_tr = pd.read_csv('/content/drive/MyDrive/DatasetSeminario/Algo_results/AlgoResults.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 261,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "vUmCXG1FrkFk",
        "outputId": "dc0bde27-2986-425f-86c8-a30b8e11fb77"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    AdaBoost  GradBoost   CatBoost   LightGBM    XGBoost\n",
              "0  89.830065  95.071895  97.977124  97.421569  96.676471\n",
              "1  95.800512  96.515772  97.087170  96.632992  96.429028\n",
              "2  83.140476  84.185714  87.152381  88.123810  85.007143\n",
              "3  93.027778  93.055556  93.453175  93.706349  92.430159\n",
              "4  72.254622  72.410924  74.403361  71.801681  70.579832\n",
              "5  75.118592  76.183869  76.249316  73.985133  73.842447\n",
              "6  79.814815  80.074074  81.888889  80.888889  80.370370\n",
              "7  77.381469  92.109430  99.884868  99.112719  98.810307\n",
              "8  72.254622  72.293277  74.403361  71.801681  70.579832"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-223deb75-2a0e-47bb-81b0-877220ba2e77\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AdaBoost</th>\n",
              "      <th>GradBoost</th>\n",
              "      <th>CatBoost</th>\n",
              "      <th>LightGBM</th>\n",
              "      <th>XGBoost</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>89.830065</td>\n",
              "      <td>95.071895</td>\n",
              "      <td>97.977124</td>\n",
              "      <td>97.421569</td>\n",
              "      <td>96.676471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>95.800512</td>\n",
              "      <td>96.515772</td>\n",
              "      <td>97.087170</td>\n",
              "      <td>96.632992</td>\n",
              "      <td>96.429028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>83.140476</td>\n",
              "      <td>84.185714</td>\n",
              "      <td>87.152381</td>\n",
              "      <td>88.123810</td>\n",
              "      <td>85.007143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>93.027778</td>\n",
              "      <td>93.055556</td>\n",
              "      <td>93.453175</td>\n",
              "      <td>93.706349</td>\n",
              "      <td>92.430159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>72.254622</td>\n",
              "      <td>72.410924</td>\n",
              "      <td>74.403361</td>\n",
              "      <td>71.801681</td>\n",
              "      <td>70.579832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>75.118592</td>\n",
              "      <td>76.183869</td>\n",
              "      <td>76.249316</td>\n",
              "      <td>73.985133</td>\n",
              "      <td>73.842447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>79.814815</td>\n",
              "      <td>80.074074</td>\n",
              "      <td>81.888889</td>\n",
              "      <td>80.888889</td>\n",
              "      <td>80.370370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>77.381469</td>\n",
              "      <td>92.109430</td>\n",
              "      <td>99.884868</td>\n",
              "      <td>99.112719</td>\n",
              "      <td>98.810307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>72.254622</td>\n",
              "      <td>72.293277</td>\n",
              "      <td>74.403361</td>\n",
              "      <td>71.801681</td>\n",
              "      <td>70.579832</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-223deb75-2a0e-47bb-81b0-877220ba2e77')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-223deb75-2a0e-47bb-81b0-877220ba2e77 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-223deb75-2a0e-47bb-81b0-877220ba2e77');\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-deac283c-05fd-4dbc-a64c-b29e37ab7e08\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-deac283c-05fd-4dbc-a64c-b29e37ab7e08')\"\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-deac283c-05fd-4dbc-a64c-b29e37ab7e08 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 261
        }
      ],
      "source": [
        "Algo_results_tr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 194,
      "metadata": {
        "id": "wUqD-hCsj3In"
      },
      "outputs": [],
      "source": [
        "stat, p_value = friedmanchisquare(Algo_results_tr['AdaBoost'], Algo_results_tr['GradBoost'], Algo_results_tr['CatBoost'], Algo_results_tr['LightGBM'], Algo_results_tr['XGBoost'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 195,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JfHUN2tlxVTY",
        "outputId": "e78f38bf-ff22-4041-c596-25366b955114"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00025070600166248206"
            ]
          },
          "metadata": {},
          "execution_count": 195
        }
      ],
      "source": [
        "p_value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 196,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "reprvZdFuqyR",
        "outputId": "f33684c3-ecb0-4c09-f925-9b1775124613"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are significant differences among the models.\n"
          ]
        }
      ],
      "source": [
        "alpha = 0.05\n",
        "if p_value < alpha:\n",
        "    print('There are significant differences among the models.')\n",
        "else:\n",
        "    print('There are no significant differences among the models.')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PhdzNCb-x2I9"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FjYBbvR7yAQl"
      },
      "source": [
        "# **Nemenyi test**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uXIQ9unSyDtG"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 197,
      "metadata": {
        "id": "TXAePdgP6bsk"
      },
      "outputs": [],
      "source": [
        "nemenyi_results = sp.posthoc_nemenyi_friedman(a=Algo_results_tr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 198,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "5RR8Z-7K0qtg",
        "outputId": "0ccb6fe7-19b9-4fa4-98d3-76f1c91ad858"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           AdaBoost  GradBoost  CatBoost  LightGBM   XGBoost\n",
              "AdaBoost   1.000000   0.559331  0.001000  0.119516  0.900000\n",
              "GradBoost  0.559331   1.000000  0.083366  0.897740  0.728536\n",
              "CatBoost   0.001000   0.083366  1.000000  0.472825  0.001816\n",
              "LightGBM   0.119516   0.897740  0.472825  1.000000  0.225673\n",
              "XGBoost    0.900000   0.728536  0.001816  0.225673  1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c1dcb7e8-ac25-4c29-802c-ed3c6fd3f653\" 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>AdaBoost</th>\n",
              "      <th>GradBoost</th>\n",
              "      <th>CatBoost</th>\n",
              "      <th>LightGBM</th>\n",
              "      <th>XGBoost</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>AdaBoost</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.559331</td>\n",
              "      <td>0.001000</td>\n",
              "      <td>0.119516</td>\n",
              "      <td>0.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GradBoost</th>\n",
              "      <td>0.559331</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.083366</td>\n",
              "      <td>0.897740</td>\n",
              "      <td>0.728536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CatBoost</th>\n",
              "      <td>0.001000</td>\n",
              "      <td>0.083366</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.472825</td>\n",
              "      <td>0.001816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LightGBM</th>\n",
              "      <td>0.119516</td>\n",
              "      <td>0.897740</td>\n",
              "      <td>0.472825</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.225673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGBoost</th>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.728536</td>\n",
              "      <td>0.001816</td>\n",
              "      <td>0.225673</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c1dcb7e8-ac25-4c29-802c-ed3c6fd3f653')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c1dcb7e8-ac25-4c29-802c-ed3c6fd3f653 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-c1dcb7e8-ac25-4c29-802c-ed3c6fd3f653');\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-38c07113-62b0-41d6-81ca-0b9e9122fd81\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-38c07113-62b0-41d6-81ca-0b9e9122fd81')\"\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-38c07113-62b0-41d6-81ca-0b9e9122fd81 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 198
        }
      ],
      "source": [
        "nemenyi_results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 199,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I0FsDJGc3i3Y",
        "outputId": "9bdfe2c9-d4fa-43fd-a6bd-aa59a5bc8f72"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Models 1 and 2 are not significantly different (p-value = 0.5593).\n",
            "Models 1 and 3 are significantly different (p-value = 0.0010).\n",
            "Models 1 and 4 are not significantly different (p-value = 0.1195).\n",
            "Models 1 and 5 are not significantly different (p-value = 0.9000).\n",
            "Models 2 and 3 are not significantly different (p-value = 0.0834).\n",
            "Models 2 and 4 are not significantly different (p-value = 0.8977).\n",
            "Models 2 and 5 are not significantly different (p-value = 0.7285).\n",
            "Models 3 and 4 are not significantly different (p-value = 0.4728).\n",
            "Models 3 and 5 are significantly different (p-value = 0.0018).\n",
            "Models 4 and 5 are not significantly different (p-value = 0.2257).\n"
          ]
        }
      ],
      "source": [
        "alpha = 0.05\n",
        "\n",
        "# Print p-values for all pairs of models\n",
        "for i in range(nemenyi_results.shape[0]):\n",
        "    for j in range(i + 1, nemenyi_results.shape[1]):\n",
        "        model1 = i + 1\n",
        "        model2 = j + 1\n",
        "        p_value = nemenyi_results.iloc[i, j]\n",
        "\n",
        "        if p_value < alpha:\n",
        "            print(f\"Models {model1} and {model2} are significantly different (p-value = {p_value:.4f}).\")\n",
        "        else:\n",
        "            print(f\"Models {model1} and {model2} are not significantly different (p-value = {p_value:.4f}).\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8xh-kfjfJ4rY"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.17"
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  "nbformat_minor": 0
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