PyCaret_Heart_Disease-Cleveland-Hungarian_Cleveland.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# SETUP"
      ],
      "metadata": {
        "id": "x2H5QiCQ6Brv"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zXvBMa-r57wc"
      },
      "outputs": [],
      "source": [
        "# import libraries\n",
        "from google.colab import files\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn import metrics\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import StratifiedShuffleSplit"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hungarian Data"
      ],
      "metadata": {
        "id": "-pB6vjqcZnRs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# load hungarian datafile\n",
        "hungarian_csv_path = \"/reprocessed.hungarian.data\"\n",
        "hungarian_data = pd.read_csv(hungarian_csv_path, delimiter = ' ', header = None)\n",
        "# assign column names\n",
        "hungarian_data.set_axis(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs',\n",
        "                  'restecg', 'thalach', 'exang', 'oldpeak', 'slope',\n",
        "                  'ca', 'thal', 'num'], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "XCVoL6th6RgI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cleveland Data"
      ],
      "metadata": {
        "id": "k8dtXzyRZoz9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# load Cleveland datafile\n",
        "cleveland_csv_path = \"/processed.cleveland.data\"\n",
        "cleveland_data = pd.read_csv(cleveland_csv_path, header = None)\n",
        "# assign column names\n",
        "cleveland_data.set_axis(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs',\n",
        "                  'restecg', 'thalach', 'exang', 'oldpeak', 'slope',\n",
        "                  'ca', 'thal', 'num'], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "KYxg2KxwY7Dt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "vFo54vY5o1G2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define label column\n",
        "heart_label = 'num'"
      ],
      "metadata": {
        "id": "uk7ZN8fO78WW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# SLIGHTLY BETTER MODEL\n",
        "no missing values & stratified sampling"
      ],
      "metadata": {
        "id": "3Dio3HabHu6p"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DATA PREPROCESSING"
      ],
      "metadata": {
        "id": "VuYocTRhqHz5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### MISSING VALUES"
      ],
      "metadata": {
        "id": "4NZ7lFaCIL2W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# make copy of original dataframe\n",
        "cleveland = cleveland_data.copy()"
      ],
      "metadata": {
        "id": "ShywUowp4shy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check for missing values\n",
        "cleveland.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Ge1KJ0t4-3o",
        "outputId": "247bd20d-bd60-48d2-ad3b-5d1fcbcf99aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       303 non-null    float64\n",
            " 1   sex       303 non-null    float64\n",
            " 2   cp        303 non-null    float64\n",
            " 3   trestbps  303 non-null    float64\n",
            " 4   chol      303 non-null    float64\n",
            " 5   fbs       303 non-null    float64\n",
            " 6   restecg   303 non-null    float64\n",
            " 7   thalach   303 non-null    float64\n",
            " 8   exang     303 non-null    float64\n",
            " 9   oldpeak   303 non-null    float64\n",
            " 10  slope     303 non-null    float64\n",
            " 11  ca        303 non-null    object \n",
            " 12  thal      303 non-null    object \n",
            " 13  num       303 non-null    int64  \n",
            "dtypes: float64(11), int64(1), object(2)\n",
            "memory usage: 33.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# replace '?' values with NaN so can impute\n",
        "cleveland['thal'].replace('?', np.NaN, inplace=True)\n",
        "cleveland['thal'] = cleveland['thal'].astype(float)\n",
        "cleveland['ca'].replace('?', np.NaN, inplace=True)\n",
        "cleveland['ca'] = cleveland['ca'].astype(float)\n",
        "cleveland.tail() # to verify didn't mess up dataframe IDs"
      ],
      "metadata": {
        "id": "9-K5ouSs5Oju",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "40dde7a1-a5f5-485b-8f45-d94073fc5878"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "298  45.0  1.0  1.0     110.0  264.0  0.0      0.0    132.0    0.0      1.2   \n",
              "299  68.0  1.0  4.0     144.0  193.0  1.0      0.0    141.0    0.0      3.4   \n",
              "300  57.0  1.0  4.0     130.0  131.0  0.0      0.0    115.0    1.0      1.2   \n",
              "301  57.0  0.0  2.0     130.0  236.0  0.0      2.0    174.0    0.0      0.0   \n",
              "302  38.0  1.0  3.0     138.0  175.0  0.0      0.0    173.0    0.0      0.0   \n",
              "\n",
              "     slope   ca  thal  num  \n",
              "298    2.0  0.0   7.0    1  \n",
              "299    2.0  2.0   7.0    2  \n",
              "300    2.0  1.0   7.0    3  \n",
              "301    2.0  1.0   3.0    1  \n",
              "302    1.0  NaN   3.0    0  "
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "# impute with mode as 'thal' and 'ca' (attributes w/ missing values) are discrete\n",
        "imputeMode = SimpleImputer(strategy=\"most_frequent\") # create mode imputer\n",
        "imputeMode.fit(cleveland) # fit - learns the data\n",
        "imputed = imputeMode.transform(cleveland) # transform - imputes with chosen strategy\n",
        "cleveland = pd.DataFrame(imputed, columns=cleveland.columns, index=cleveland['thal'].index) # back to pandas DataFrame\n",
        "cleveland.info() # check for missing values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UrCeHciD8m9e",
        "outputId": "70c495f8-3fc5-41d2-a5ff-2a4332098c55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       303 non-null    float64\n",
            " 1   sex       303 non-null    float64\n",
            " 2   cp        303 non-null    float64\n",
            " 3   trestbps  303 non-null    float64\n",
            " 4   chol      303 non-null    float64\n",
            " 5   fbs       303 non-null    float64\n",
            " 6   restecg   303 non-null    float64\n",
            " 7   thalach   303 non-null    float64\n",
            " 8   exang     303 non-null    float64\n",
            " 9   oldpeak   303 non-null    float64\n",
            " 10  slope     303 non-null    float64\n",
            " 11  ca        303 non-null    float64\n",
            " 12  thal      303 non-null    float64\n",
            " 13  num       303 non-null    float64\n",
            "dtypes: float64(14)\n",
            "memory usage: 33.3 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cleveland.head() # check for anything obviously wonky"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "53PYROVrEW7Z",
        "outputId": "7d090656-926c-4948-a509-43bb60af94c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "0  63.0  1.0  1.0     145.0  233.0  1.0      2.0    150.0    0.0      2.3   \n",
              "1  67.0  1.0  4.0     160.0  286.0  0.0      2.0    108.0    1.0      1.5   \n",
              "2  67.0  1.0  4.0     120.0  229.0  0.0      2.0    129.0    1.0      2.6   \n",
              "3  37.0  1.0  3.0     130.0  250.0  0.0      0.0    187.0    0.0      3.5   \n",
              "4  41.0  0.0  2.0     130.0  204.0  0.0      2.0    172.0    0.0      1.4   \n",
              "\n",
              "   slope   ca  thal  num  \n",
              "0    3.0  0.0   6.0  0.0  \n",
              "1    2.0  3.0   3.0  2.0  \n",
              "2    2.0  2.0   7.0  1.0  \n",
              "3    3.0  0.0   3.0  0.0  \n",
              "4    1.0  0.0   3.0  0.0  "
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              "      <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>num</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>63.0</td>\n",
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              "      <td>6.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>67.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
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              "      <td>108.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.5</td>\n",
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              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>67.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>229.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>129.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>37.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>130.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>187.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>41.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>204.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>172.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8e007488-f85f-4484-a081-af8268e186d3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\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",
              "    [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-8e007488-f85f-4484-a081-af8268e186d3 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-8e007488-f85f-4484-a081-af8268e186d3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### BINARY LABEL\n",
        "(multiclass for \"future research\")"
      ],
      "metadata": {
        "id": "RqiWdhsTIQ3H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cleveland['num'][cleveland['num'] > 0] = 1\n",
        "cleveland.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "zP1I2-0ZISki",
        "outputId": "c201963e-f0ba-4833-99cf-2995278f2c79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "0  63.0  1.0  1.0     145.0  233.0  1.0      2.0    150.0    0.0      2.3   \n",
              "1  67.0  1.0  4.0     160.0  286.0  0.0      2.0    108.0    1.0      1.5   \n",
              "2  67.0  1.0  4.0     120.0  229.0  0.0      2.0    129.0    1.0      2.6   \n",
              "3  37.0  1.0  3.0     130.0  250.0  0.0      0.0    187.0    0.0      3.5   \n",
              "4  41.0  0.0  2.0     130.0  204.0  0.0      2.0    172.0    0.0      1.4   \n",
              "\n",
              "   slope   ca  thal  num  \n",
              "0    3.0  0.0   6.0  0.0  \n",
              "1    2.0  3.0   3.0  1.0  \n",
              "2    2.0  2.0   7.0  1.0  \n",
              "3    3.0  0.0   3.0  0.0  \n",
              "4    1.0  0.0   3.0  0.0  "
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              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
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              "      <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>num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63.0</td>\n",
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              "      <td>233.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>67.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>286.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>108.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>67.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>229.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>129.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>37.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>250.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>187.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>41.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>204.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>172.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3231e7a7-4749-4892-8911-2d7203377c57')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3231e7a7-4749-4892-8911-2d7203377c57 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-3231e7a7-4749-4892-8911-2d7203377c57');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### STRATIFIED SAMPLING\n",
        "\n",
        "(due to clear significance of sex)"
      ],
      "metadata": {
        "id": "6oc55538M-Yz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cleveland_strat = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
        "for train_index, test_index in cleveland_strat.split(cleveland, cleveland[\"num\"]):\n",
        "  cleveland_strat_train = cleveland.loc[train_index]\n",
        "  cleveland_strat_test = cleveland.loc[test_index]"
      ],
      "metadata": {
        "id": "eWIXOA1-NRYE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cleveland_strat_test[\"num\"].value_counts()/len(cleveland_strat_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_pdyTPieQRK-",
        "outputId": "f14ae701-5ade-4ce1-b74b-3baddd5392b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    0.540984\n",
              "1.0    0.180328\n",
              "3.0    0.114754\n",
              "2.0    0.114754\n",
              "4.0    0.049180\n",
              "Name: num, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cleveland_strat_train[\"num\"].value_counts()/len(cleveland_strat_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vx2mSr8RQce9",
        "outputId": "99bc21f7-9348-4323-e9ee-bb4e87e98977"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    0.541322\n",
              "1.0    0.458678\n",
              "Name: num, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Less significant for Cleveland and Statlog datasets, which have less dramatic difference in sex representation in data, but different for other three datasets. "
      ],
      "metadata": {
        "id": "xWi_wP6iQ3HI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# split stratified data\n",
        "cleveland_strat_train_X = cleveland_strat_train.drop([heart_label], axis=1)\n",
        "cleveland_strat_train_y = cleveland_strat_train[heart_label]\n",
        "cleveland_strat_test_X = cleveland_strat_test.drop([heart_label], axis=1)\n",
        "cleveland_strat_test_y = cleveland_strat_test[heart_label]\n",
        "cleveland_strat_train_X.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "QxckdahgRjW8",
        "outputId": "322694c5-6870-4fe1-dc5b-469b772e0eae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "45   58.0  1.0  3.0     112.0  230.0  0.0      2.0    165.0    0.0      2.5   \n",
              "77   51.0  0.0  3.0     140.0  308.0  0.0      2.0    142.0    0.0      1.5   \n",
              "64   54.0  1.0  4.0     120.0  188.0  0.0      0.0    113.0    0.0      1.4   \n",
              "20   64.0  1.0  1.0     110.0  211.0  0.0      2.0    144.0    1.0      1.8   \n",
              "273  71.0  0.0  4.0     112.0  149.0  0.0      0.0    125.0    0.0      1.6   \n",
              "\n",
              "     slope   ca  thal  \n",
              "45     2.0  1.0   7.0  \n",
              "77     1.0  1.0   3.0  \n",
              "64     2.0  1.0   7.0  \n",
              "20     2.0  0.0   3.0  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Bustin down Pycaret style"
      ],
      "metadata": {
        "id": "C3PqLMT4rULR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install pycaret"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xm9iwid0rTSf",
        "outputId": "87fd62d1-f3b9-4dba-90cb-9d42d378503f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pycaret\n",
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            "Building wheels for collected packages: plotly-resampler, pyod\n",
            "  Building wheel for plotly-resampler (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for plotly-resampler: filename=plotly_resampler-0.8.3.2-cp39-cp39-manylinux_2_31_x86_64.whl size=75058 sha256=4a5945be10ad81057e47876f8c7be5366e309dda33485c46231348967b7fd4b3\n",
            "  Stored in directory: /root/.cache/pip/wheels/9e/36/06/4c11e300918011376af149098621ec7ebe06d8256566d43d51\n",
            "  Building wheel for pyod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyod: filename=pyod-1.0.9-py3-none-any.whl size=184112 sha256=7f0fb6acf55eb858244ce8d873fb5b613c6f458c8460f2eb3231566f642b03dc\n",
            "  Stored in directory: /root/.cache/pip/wheels/1b/9c/b8/9759d7cc64a1e01bb9872ade80cb7db445ccf506e083325106\n",
            "Successfully built plotly-resampler pyod\n",
            "Installing collected packages: trace-updater, kaleido, dash-table, dash-html-components, dash-core-components, xxhash, wurlitzer, schemdraw, retrying, orjson, joblib, jedi, deprecation, deprecated, ansi2html, sktime, scikit-plot, pyod, dash, pmdarima, jupyter-dash, category-encoders, tbats, plotly-resampler, pycaret\n",
            "  Attempting uninstall: joblib\n",
            "    Found existing installation: joblib 1.1.1\n",
            "    Uninstalling joblib-1.1.1:\n",
            "      Successfully uninstalled joblib-1.1.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "pandas-profiling 3.2.0 requires joblib~=1.1.0, but you have joblib 1.2.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed ansi2html-1.8.0 category-encoders-2.6.0 dash-2.9.1 dash-core-components-2.0.0 dash-html-components-2.0.0 dash-table-5.0.0 deprecated-1.2.13 deprecation-2.1.0 jedi-0.18.2 joblib-1.2.0 jupyter-dash-0.4.2 kaleido-0.2.1 orjson-3.8.9 plotly-resampler-0.8.3.2 pmdarima-2.0.3 pycaret-3.0.0 pyod-1.0.9 retrying-1.3.4 schemdraw-0.16 scikit-plot-0.3.7 sktime-0.16.1 tbats-1.1.2 trace-updater-0.0.9 wurlitzer-3.0.3 xxhash-3.2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret import classification"
      ],
      "metadata": {
        "id": "Sqs0pkFqrr9k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Strat Tests..."
      ],
      "metadata": {
        "id": "JiV7eCP9ibJF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cleveClass = classification.setup(data = cleveland_strat_train, target = 'num',  normalize = True, transformation = True, remove_multicollinearity = True, multicollinearity_threshold = 0.95)"
      ],
      "metadata": {
        "id": "X-U1nmMPiPRj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "outputId": "ac691edb-64a8-4efd-c8da-59aa780b69a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f2b8f4ee6d0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_6bea0_row8_col1, #T_6bea0_row12_col1, #T_6bea0_row14_col1, #T_6bea0_row16_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_6bea0\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_6bea0_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_6bea0_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_6bea0_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_6bea0_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_6bea0_row0_col1\" class=\"data row0 col1\" >8677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6bea0_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_6bea0_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
              "      <td id=\"T_6bea0_row1_col1\" class=\"data row1 col1\" >num</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6bea0_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_6bea0_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
              "      <td id=\"T_6bea0_row2_col1\" class=\"data row2 col1\" >Multiclass</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6bea0_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_6bea0_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
              "      <td id=\"T_6bea0_row3_col1\" class=\"data row3 col1\" >(242, 14)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6bea0_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_6bea0_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_6bea0_row4_col1\" class=\"data row4 col1\" >(242, 14)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6bea0_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_6bea0_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
              "      <td id=\"T_6bea0_row5_col1\" class=\"data row5 col1\" >(169, 14)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6bea0_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_6bea0_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
              "      <td id=\"T_6bea0_row6_col1\" class=\"data row6 col1\" >(73, 14)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6bea0_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_6bea0_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
              "      <td id=\"T_6bea0_row7_col1\" class=\"data row7 col1\" >13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6bea0_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_6bea0_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
              "      <td id=\"T_6bea0_row8_col1\" class=\"data row8 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6bea0_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_6bea0_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
              "      <td id=\"T_6bea0_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6bea0_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_6bea0_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_6bea0_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6bea0_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_6bea0_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_6bea0_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6bea0_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_6bea0_row12_col0\" class=\"data row12 col0\" >Remove multicollinearity</td>\n",
              "      <td id=\"T_6bea0_row12_col1\" class=\"data row12 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6bea0_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_6bea0_row13_col0\" class=\"data row13 col0\" >Multicollinearity threshold</td>\n",
              "      <td id=\"T_6bea0_row13_col1\" class=\"data row13 col1\" >0.950000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6bea0_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_6bea0_row14_col0\" class=\"data row14 col0\" >Transformation</td>\n",
              "      <td id=\"T_6bea0_row14_col1\" class=\"data row14 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6bea0_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_6bea0_row15_col0\" class=\"data row15 col0\" >Transformation method</td>\n",
              "      <td id=\"T_6bea0_row15_col1\" class=\"data row15 col1\" >yeo-johnson</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6bea0_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_6bea0_row16_col0\" class=\"data row16 col0\" >Normalize</td>\n",
              "      <td id=\"T_6bea0_row16_col1\" class=\"data row16 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6bea0_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_6bea0_row17_col0\" class=\"data row17 col0\" >Normalize method</td>\n",
              "      <td id=\"T_6bea0_row17_col1\" class=\"data row17 col1\" >zscore</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6bea0_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "      <td id=\"T_6bea0_row18_col0\" class=\"data row18 col0\" >Fold Generator</td>\n",
              "      <td id=\"T_6bea0_row18_col1\" class=\"data row18 col1\" >StratifiedKFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6bea0_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
              "      <td id=\"T_6bea0_row19_col0\" class=\"data row19 col0\" >Fold Number</td>\n",
              "      <td id=\"T_6bea0_row19_col1\" class=\"data row19 col1\" >10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6bea0_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
              "      <td id=\"T_6bea0_row20_col0\" class=\"data row20 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_6bea0_row20_col1\" class=\"data row20 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6bea0_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
              "      <td id=\"T_6bea0_row21_col0\" class=\"data row21 col0\" >Use GPU</td>\n",
              "      <td id=\"T_6bea0_row21_col1\" class=\"data row21 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6bea0_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
              "      <td id=\"T_6bea0_row22_col0\" class=\"data row22 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_6bea0_row22_col1\" class=\"data row22 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6bea0_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
              "      <td id=\"T_6bea0_row23_col0\" class=\"data row23 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_6bea0_row23_col1\" class=\"data row23 col1\" >clf-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6bea0_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
              "      <td id=\"T_6bea0_row24_col0\" class=\"data row24 col0\" >USI</td>\n",
              "      <td id=\"T_6bea0_row24_col1\" class=\"data row24 col1\" >5a72</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cleveClass = classification.setup(data = cleveland_strat_train, target = 'num')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "oP9x5Ss2mMz8",
        "outputId": "58d555c4-1998-4725-bb9f-4ffec46becde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f2b8beb7d00>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_f67ce_row8_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_f67ce\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_f67ce_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_f67ce_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_f67ce_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_f67ce_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_f67ce_row0_col1\" class=\"data row0 col1\" >4111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f67ce_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_f67ce_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
              "      <td id=\"T_f67ce_row1_col1\" class=\"data row1 col1\" >num</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f67ce_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_f67ce_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
              "      <td id=\"T_f67ce_row2_col1\" class=\"data row2 col1\" >Multiclass</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f67ce_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_f67ce_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
              "      <td id=\"T_f67ce_row3_col1\" class=\"data row3 col1\" >(242, 14)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f67ce_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_f67ce_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_f67ce_row4_col1\" class=\"data row4 col1\" >(242, 14)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f67ce_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_f67ce_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
              "      <td id=\"T_f67ce_row5_col1\" class=\"data row5 col1\" >(169, 14)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f67ce_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_f67ce_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
              "      <td id=\"T_f67ce_row6_col1\" class=\"data row6 col1\" >(73, 14)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f67ce_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_f67ce_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
              "      <td id=\"T_f67ce_row7_col1\" class=\"data row7 col1\" >13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f67ce_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_f67ce_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
              "      <td id=\"T_f67ce_row8_col1\" class=\"data row8 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f67ce_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_f67ce_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
              "      <td id=\"T_f67ce_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f67ce_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_f67ce_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_f67ce_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f67ce_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_f67ce_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_f67ce_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f67ce_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_f67ce_row12_col0\" class=\"data row12 col0\" >Fold Generator</td>\n",
              "      <td id=\"T_f67ce_row12_col1\" class=\"data row12 col1\" >StratifiedKFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f67ce_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_f67ce_row13_col0\" class=\"data row13 col0\" >Fold Number</td>\n",
              "      <td id=\"T_f67ce_row13_col1\" class=\"data row13 col1\" >10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f67ce_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_f67ce_row14_col0\" class=\"data row14 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_f67ce_row14_col1\" class=\"data row14 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f67ce_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_f67ce_row15_col0\" class=\"data row15 col0\" >Use GPU</td>\n",
              "      <td id=\"T_f67ce_row15_col1\" class=\"data row15 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f67ce_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_f67ce_row16_col0\" class=\"data row16 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_f67ce_row16_col1\" class=\"data row16 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f67ce_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_f67ce_row17_col0\" class=\"data row17 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_f67ce_row17_col1\" class=\"data row17 col1\" >clf-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f67ce_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "      <td id=\"T_f67ce_row18_col0\" class=\"data row18 col0\" >USI</td>\n",
              "      <td id=\"T_f67ce_row18_col1\" class=\"data row18 col1\" >bc58</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cleveClass.compare_models()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 647,
          "referenced_widgets": [
            "38c92a49c82d443daf8b3bdb0e870e3e",
            "0a9a09a3ee9e481fb5155f780c2da046",
            "6c66125e1e164458925100708237eb3b",
            "7e4bc763db4b412fac3953a731f1310c",
            "e4350a0c7d344facbaa0ddf2126f965f",
            "1c92eae2223942d89b4a548aec702299",
            "bf13fbcfe066470ab90124df740dbcef",
            "c1d1d01e192e4347aa051b6f1c2ce8d2",
            "90e6bd60fac84472add4a7c4c45393c2",
            "20bc9e89e89e433eb18c940893c07bcb",
            "5e9d17e3acc143d89774e4585ab2b9ba"
          ]
        },
        "id": "wEAq-cOulvCp",
        "outputId": "431b839a-a996-4301-bfd4-817de3abb85d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "<pandas.io.formats.style.Styler at 0x7f2b8d067d60>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_fbf9a th {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_fbf9a_row0_col0, #T_fbf9a_row0_col2, #T_fbf9a_row0_col4, #T_fbf9a_row0_col5, #T_fbf9a_row1_col0, #T_fbf9a_row1_col1, #T_fbf9a_row1_col2, #T_fbf9a_row1_col3, #T_fbf9a_row1_col4, #T_fbf9a_row1_col5, #T_fbf9a_row1_col6, #T_fbf9a_row1_col7, #T_fbf9a_row2_col0, #T_fbf9a_row2_col1, #T_fbf9a_row2_col3, #T_fbf9a_row2_col6, #T_fbf9a_row2_col7, #T_fbf9a_row3_col0, #T_fbf9a_row3_col1, #T_fbf9a_row3_col2, #T_fbf9a_row3_col3, #T_fbf9a_row3_col4, #T_fbf9a_row3_col5, #T_fbf9a_row3_col6, #T_fbf9a_row3_col7, #T_fbf9a_row4_col0, #T_fbf9a_row4_col1, #T_fbf9a_row4_col2, #T_fbf9a_row4_col3, #T_fbf9a_row4_col4, #T_fbf9a_row4_col5, #T_fbf9a_row4_col6, #T_fbf9a_row4_col7, #T_fbf9a_row5_col0, #T_fbf9a_row5_col1, #T_fbf9a_row5_col2, #T_fbf9a_row5_col3, #T_fbf9a_row5_col4, #T_fbf9a_row5_col5, #T_fbf9a_row5_col6, #T_fbf9a_row5_col7, #T_fbf9a_row6_col0, #T_fbf9a_row6_col1, #T_fbf9a_row6_col2, #T_fbf9a_row6_col3, #T_fbf9a_row6_col4, #T_fbf9a_row6_col5, #T_fbf9a_row6_col6, #T_fbf9a_row6_col7, #T_fbf9a_row7_col0, #T_fbf9a_row7_col1, #T_fbf9a_row7_col2, #T_fbf9a_row7_col3, #T_fbf9a_row7_col4, #T_fbf9a_row7_col5, #T_fbf9a_row7_col6, #T_fbf9a_row7_col7, #T_fbf9a_row8_col0, #T_fbf9a_row8_col1, #T_fbf9a_row8_col2, #T_fbf9a_row8_col3, #T_fbf9a_row8_col4, #T_fbf9a_row8_col5, #T_fbf9a_row8_col6, #T_fbf9a_row8_col7, #T_fbf9a_row9_col0, #T_fbf9a_row9_col1, #T_fbf9a_row9_col2, #T_fbf9a_row9_col3, #T_fbf9a_row9_col4, #T_fbf9a_row9_col5, #T_fbf9a_row9_col6, #T_fbf9a_row9_col7, #T_fbf9a_row10_col0, #T_fbf9a_row10_col1, #T_fbf9a_row10_col2, #T_fbf9a_row10_col3, #T_fbf9a_row10_col4, #T_fbf9a_row10_col5, #T_fbf9a_row10_col6, #T_fbf9a_row10_col7, #T_fbf9a_row11_col0, #T_fbf9a_row11_col1, #T_fbf9a_row11_col2, #T_fbf9a_row11_col3, #T_fbf9a_row11_col4, #T_fbf9a_row11_col5, #T_fbf9a_row11_col6, #T_fbf9a_row11_col7, #T_fbf9a_row12_col0, #T_fbf9a_row12_col1, #T_fbf9a_row12_col2, #T_fbf9a_row12_col3, #T_fbf9a_row12_col4, #T_fbf9a_row12_col5, #T_fbf9a_row12_col6, #T_fbf9a_row12_col7, #T_fbf9a_row13_col0, #T_fbf9a_row13_col1, #T_fbf9a_row13_col2, #T_fbf9a_row13_col3, #T_fbf9a_row13_col4, #T_fbf9a_row13_col5, #T_fbf9a_row13_col6, #T_fbf9a_row13_col7, #T_fbf9a_row14_col0, #T_fbf9a_row14_col1, #T_fbf9a_row14_col2, #T_fbf9a_row14_col3, #T_fbf9a_row14_col4, #T_fbf9a_row14_col5, #T_fbf9a_row14_col6, #T_fbf9a_row14_col7 {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_fbf9a_row0_col1, #T_fbf9a_row0_col3, #T_fbf9a_row0_col6, #T_fbf9a_row0_col7, #T_fbf9a_row2_col2, #T_fbf9a_row2_col4, #T_fbf9a_row2_col5 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "}\n",
              "#T_fbf9a_row0_col8, #T_fbf9a_row1_col8, #T_fbf9a_row2_col8, #T_fbf9a_row3_col8, #T_fbf9a_row4_col8, #T_fbf9a_row5_col8, #T_fbf9a_row6_col8, #T_fbf9a_row7_col8, #T_fbf9a_row8_col8, #T_fbf9a_row10_col8, #T_fbf9a_row11_col8, #T_fbf9a_row12_col8, #T_fbf9a_row13_col8, #T_fbf9a_row14_col8 {\n",
              "  text-align: left;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "#T_fbf9a_row9_col8 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_fbf9a\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_fbf9a_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_fbf9a_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th id=\"T_fbf9a_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
              "      <th id=\"T_fbf9a_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
              "      <th id=\"T_fbf9a_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
              "      <th id=\"T_fbf9a_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
              "      <th id=\"T_fbf9a_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th id=\"T_fbf9a_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
              "      <th id=\"T_fbf9a_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_fbf9a_level0_row0\" class=\"row_heading level0 row0\" >lr</th>\n",
              "      <td id=\"T_fbf9a_row0_col0\" class=\"data row0 col0\" >Logistic Regression</td>\n",
              "      <td id=\"T_fbf9a_row0_col1\" class=\"data row0 col1\" >0.6221</td>\n",
              "      <td id=\"T_fbf9a_row0_col2\" class=\"data row0 col2\" >0.5837</td>\n",
              "      <td id=\"T_fbf9a_row0_col3\" class=\"data row0 col3\" >0.6221</td>\n",
              "      <td id=\"T_fbf9a_row0_col4\" class=\"data row0 col4\" >0.5738</td>\n",
              "      <td id=\"T_fbf9a_row0_col5\" class=\"data row0 col5\" >0.5810</td>\n",
              "      <td id=\"T_fbf9a_row0_col6\" class=\"data row0 col6\" >0.3837</td>\n",
              "      <td id=\"T_fbf9a_row0_col7\" class=\"data row0 col7\" >0.4052</td>\n",
              "      <td id=\"T_fbf9a_row0_col8\" class=\"data row0 col8\" >0.3980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_fbf9a_level0_row1\" class=\"row_heading level0 row1\" >ridge</th>\n",
              "      <td id=\"T_fbf9a_row1_col0\" class=\"data row1 col0\" >Ridge Classifier</td>\n",
              "      <td id=\"T_fbf9a_row1_col1\" class=\"data row1 col1\" >0.6154</td>\n",
              "      <td id=\"T_fbf9a_row1_col2\" class=\"data row1 col2\" >0.0000</td>\n",
              "      <td id=\"T_fbf9a_row1_col3\" class=\"data row1 col3\" >0.6154</td>\n",
              "      <td id=\"T_fbf9a_row1_col4\" class=\"data row1 col4\" >0.5337</td>\n",
              "      <td id=\"T_fbf9a_row1_col5\" class=\"data row1 col5\" >0.5548</td>\n",
              "      <td id=\"T_fbf9a_row1_col6\" class=\"data row1 col6\" >0.3342</td>\n",
              "      <td id=\"T_fbf9a_row1_col7\" class=\"data row1 col7\" >0.3605</td>\n",
              "      <td id=\"T_fbf9a_row1_col8\" class=\"data row1 col8\" >0.0890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_fbf9a_level0_row2\" class=\"row_heading level0 row2\" >lda</th>\n",
              "      <td id=\"T_fbf9a_row2_col0\" class=\"data row2 col0\" >Linear Discriminant Analysis</td>\n",
              "      <td id=\"T_fbf9a_row2_col1\" class=\"data row2 col1\" >0.6044</td>\n",
              "      <td id=\"T_fbf9a_row2_col2\" class=\"data row2 col2\" >0.5961</td>\n",
              "      <td id=\"T_fbf9a_row2_col3\" class=\"data row2 col3\" >0.6044</td>\n",
              "      <td id=\"T_fbf9a_row2_col4\" class=\"data row2 col4\" >0.5832</td>\n",
              "      <td id=\"T_fbf9a_row2_col5\" class=\"data row2 col5\" >0.5833</td>\n",
              "      <td id=\"T_fbf9a_row2_col6\" class=\"data row2 col6\" >0.3725</td>\n",
              "      <td id=\"T_fbf9a_row2_col7\" class=\"data row2 col7\" >0.3841</td>\n",
              "      <td id=\"T_fbf9a_row2_col8\" class=\"data row2 col8\" >0.1170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_fbf9a_level0_row3\" class=\"row_heading level0 row3\" >lightgbm</th>\n",
              "      <td id=\"T_fbf9a_row3_col0\" class=\"data row3 col0\" >Light Gradient Boosting Machine</td>\n",
              "      <td id=\"T_fbf9a_row3_col1\" class=\"data row3 col1\" >0.6037</td>\n",
              "      <td id=\"T_fbf9a_row3_col2\" class=\"data row3 col2\" >0.5668</td>\n",
              "      <td id=\"T_fbf9a_row3_col3\" class=\"data row3 col3\" >0.6037</td>\n",
              "      <td id=\"T_fbf9a_row3_col4\" class=\"data row3 col4\" >0.5447</td>\n",
              "      <td id=\"T_fbf9a_row3_col5\" class=\"data row3 col5\" >0.5613</td>\n",
              "      <td id=\"T_fbf9a_row3_col6\" class=\"data row3 col6\" >0.3430</td>\n",
              "      <td id=\"T_fbf9a_row3_col7\" class=\"data row3 col7\" >0.3562</td>\n",
              "      <td id=\"T_fbf9a_row3_col8\" class=\"data row3 col8\" >0.1750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_fbf9a_level0_row4\" class=\"row_heading level0 row4\" >rf</th>\n",
              "      <td id=\"T_fbf9a_row4_col0\" class=\"data row4 col0\" >Random Forest Classifier</td>\n",
              "      <td id=\"T_fbf9a_row4_col1\" class=\"data row4 col1\" >0.6033</td>\n",
              "      <td id=\"T_fbf9a_row4_col2\" class=\"data row4 col2\" >0.5957</td>\n",
              "      <td id=\"T_fbf9a_row4_col3\" class=\"data row4 col3\" >0.6033</td>\n",
              "      <td id=\"T_fbf9a_row4_col4\" class=\"data row4 col4\" >0.5214</td>\n",
              "      <td id=\"T_fbf9a_row4_col5\" class=\"data row4 col5\" >0.5446</td>\n",
              "      <td id=\"T_fbf9a_row4_col6\" class=\"data row4 col6\" >0.3174</td>\n",
              "      <td id=\"T_fbf9a_row4_col7\" class=\"data row4 col7\" >0.3379</td>\n",
              "      <td id=\"T_fbf9a_row4_col8\" class=\"data row4 col8\" >0.4270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_fbf9a_level0_row5\" class=\"row_heading level0 row5\" >gbc</th>\n",
              "      <td id=\"T_fbf9a_row5_col0\" class=\"data row5 col0\" >Gradient Boosting Classifier</td>\n",
              "      <td id=\"T_fbf9a_row5_col1\" class=\"data row5 col1\" >0.5982</td>\n",
              "      <td id=\"T_fbf9a_row5_col2\" class=\"data row5 col2\" >0.5795</td>\n",
              "      <td id=\"T_fbf9a_row5_col3\" class=\"data row5 col3\" >0.5982</td>\n",
              "      <td id=\"T_fbf9a_row5_col4\" class=\"data row5 col4\" >0.5367</td>\n",
              "      <td id=\"T_fbf9a_row5_col5\" class=\"data row5 col5\" >0.5598</td>\n",
              "      <td id=\"T_fbf9a_row5_col6\" class=\"data row5 col6\" >0.3421</td>\n",
              "      <td id=\"T_fbf9a_row5_col7\" class=\"data row5 col7\" >0.3541</td>\n",
              "      <td id=\"T_fbf9a_row5_col8\" class=\"data row5 col8\" >1.6740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_fbf9a_level0_row6\" class=\"row_heading level0 row6\" >xgboost</th>\n",
              "      <td id=\"T_fbf9a_row6_col0\" class=\"data row6 col0\" >Extreme Gradient Boosting</td>\n",
              "      <td id=\"T_fbf9a_row6_col1\" class=\"data row6 col1\" >0.5923</td>\n",
              "      <td id=\"T_fbf9a_row6_col2\" class=\"data row6 col2\" >0.5868</td>\n",
              "      <td id=\"T_fbf9a_row6_col3\" class=\"data row6 col3\" >0.5923</td>\n",
              "      <td id=\"T_fbf9a_row6_col4\" class=\"data row6 col4\" >0.5571</td>\n",
              "      <td id=\"T_fbf9a_row6_col5\" class=\"data row6 col5\" >0.5640</td>\n",
              "      <td id=\"T_fbf9a_row6_col6\" class=\"data row6 col6\" >0.3395</td>\n",
              "      <td id=\"T_fbf9a_row6_col7\" class=\"data row6 col7\" >0.3512</td>\n",
              "      <td id=\"T_fbf9a_row6_col8\" class=\"data row6 col8\" >0.2890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_fbf9a_level0_row7\" class=\"row_heading level0 row7\" >et</th>\n",
              "      <td id=\"T_fbf9a_row7_col0\" class=\"data row7 col0\" >Extra Trees Classifier</td>\n",
              "      <td id=\"T_fbf9a_row7_col1\" class=\"data row7 col1\" >0.5919</td>\n",
              "      <td id=\"T_fbf9a_row7_col2\" class=\"data row7 col2\" >0.5815</td>\n",
              "      <td id=\"T_fbf9a_row7_col3\" class=\"data row7 col3\" >0.5919</td>\n",
              "      <td id=\"T_fbf9a_row7_col4\" class=\"data row7 col4\" >0.5431</td>\n",
              "      <td id=\"T_fbf9a_row7_col5\" class=\"data row7 col5\" >0.5576</td>\n",
              "      <td id=\"T_fbf9a_row7_col6\" class=\"data row7 col6\" >0.3310</td>\n",
              "      <td id=\"T_fbf9a_row7_col7\" class=\"data row7 col7\" >0.3414</td>\n",
              "      <td id=\"T_fbf9a_row7_col8\" class=\"data row7 col8\" >0.5300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_fbf9a_level0_row8\" class=\"row_heading level0 row8\" >qda</th>\n",
              "      <td id=\"T_fbf9a_row8_col0\" class=\"data row8 col0\" >Quadratic Discriminant Analysis</td>\n",
              "      <td id=\"T_fbf9a_row8_col1\" class=\"data row8 col1\" >0.5563</td>\n",
              "      <td id=\"T_fbf9a_row8_col2\" class=\"data row8 col2\" >0.5171</td>\n",
              "      <td id=\"T_fbf9a_row8_col3\" class=\"data row8 col3\" >0.5563</td>\n",
              "      <td id=\"T_fbf9a_row8_col4\" class=\"data row8 col4\" >0.4822</td>\n",
              "      <td id=\"T_fbf9a_row8_col5\" class=\"data row8 col5\" >0.4976</td>\n",
              "      <td id=\"T_fbf9a_row8_col6\" class=\"data row8 col6\" >0.3012</td>\n",
              "      <td id=\"T_fbf9a_row8_col7\" class=\"data row8 col7\" >0.3238</td>\n",
              "      <td id=\"T_fbf9a_row8_col8\" class=\"data row8 col8\" >0.0760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_fbf9a_level0_row9\" class=\"row_heading level0 row9\" >dummy</th>\n",
              "      <td id=\"T_fbf9a_row9_col0\" class=\"data row9 col0\" >Dummy Classifier</td>\n",
              "      <td id=\"T_fbf9a_row9_col1\" class=\"data row9 col1\" >0.5386</td>\n",
              "      <td id=\"T_fbf9a_row9_col2\" class=\"data row9 col2\" >0.3500</td>\n",
              "      <td id=\"T_fbf9a_row9_col3\" class=\"data row9 col3\" >0.5386</td>\n",
              "      <td id=\"T_fbf9a_row9_col4\" class=\"data row9 col4\" >0.2905</td>\n",
              "      <td id=\"T_fbf9a_row9_col5\" class=\"data row9 col5\" >0.3773</td>\n",
              "      <td id=\"T_fbf9a_row9_col6\" class=\"data row9 col6\" >0.0000</td>\n",
              "      <td id=\"T_fbf9a_row9_col7\" class=\"data row9 col7\" >0.0000</td>\n",
              "      <td id=\"T_fbf9a_row9_col8\" class=\"data row9 col8\" >0.0710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_fbf9a_level0_row10\" class=\"row_heading level0 row10\" >ada</th>\n",
              "      <td id=\"T_fbf9a_row10_col0\" class=\"data row10 col0\" >Ada Boost Classifier</td>\n",
              "      <td id=\"T_fbf9a_row10_col1\" class=\"data row10 col1\" >0.5279</td>\n",
              "      <td id=\"T_fbf9a_row10_col2\" class=\"data row10 col2\" >0.5406</td>\n",
              "      <td id=\"T_fbf9a_row10_col3\" class=\"data row10 col3\" >0.5279</td>\n",
              "      <td id=\"T_fbf9a_row10_col4\" class=\"data row10 col4\" >0.5573</td>\n",
              "      <td id=\"T_fbf9a_row10_col5\" class=\"data row10 col5\" >0.5218</td>\n",
              "      <td id=\"T_fbf9a_row10_col6\" class=\"data row10 col6\" >0.2715</td>\n",
              "      <td id=\"T_fbf9a_row10_col7\" class=\"data row10 col7\" >0.2816</td>\n",
              "      <td id=\"T_fbf9a_row10_col8\" class=\"data row10 col8\" >0.2590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_fbf9a_level0_row11\" class=\"row_heading level0 row11\" >dt</th>\n",
              "      <td id=\"T_fbf9a_row11_col0\" class=\"data row11 col0\" >Decision Tree Classifier</td>\n",
              "      <td id=\"T_fbf9a_row11_col1\" class=\"data row11 col1\" >0.5272</td>\n",
              "      <td id=\"T_fbf9a_row11_col2\" class=\"data row11 col2\" >0.4643</td>\n",
              "      <td id=\"T_fbf9a_row11_col3\" class=\"data row11 col3\" >0.5272</td>\n",
              "      <td id=\"T_fbf9a_row11_col4\" class=\"data row11 col4\" >0.5408</td>\n",
              "      <td id=\"T_fbf9a_row11_col5\" class=\"data row11 col5\" >0.5168</td>\n",
              "      <td id=\"T_fbf9a_row11_col6\" class=\"data row11 col6\" >0.2784</td>\n",
              "      <td id=\"T_fbf9a_row11_col7\" class=\"data row11 col7\" >0.2897</td>\n",
              "      <td id=\"T_fbf9a_row11_col8\" class=\"data row11 col8\" >0.1240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_fbf9a_level0_row12\" class=\"row_heading level0 row12\" >nb</th>\n",
              "      <td id=\"T_fbf9a_row12_col0\" class=\"data row12 col0\" >Naive Bayes</td>\n",
              "      <td id=\"T_fbf9a_row12_col1\" class=\"data row12 col1\" >0.5151</td>\n",
              "      <td id=\"T_fbf9a_row12_col2\" class=\"data row12 col2\" >0.5729</td>\n",
              "      <td id=\"T_fbf9a_row12_col3\" class=\"data row12 col3\" >0.5151</td>\n",
              "      <td id=\"T_fbf9a_row12_col4\" class=\"data row12 col4\" >0.5813</td>\n",
              "      <td id=\"T_fbf9a_row12_col5\" class=\"data row12 col5\" >0.5245</td>\n",
              "      <td id=\"T_fbf9a_row12_col6\" class=\"data row12 col6\" >0.3002</td>\n",
              "      <td id=\"T_fbf9a_row12_col7\" class=\"data row12 col7\" >0.3248</td>\n",
              "      <td id=\"T_fbf9a_row12_col8\" class=\"data row12 col8\" >0.0880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_fbf9a_level0_row13\" class=\"row_heading level0 row13\" >knn</th>\n",
              "      <td id=\"T_fbf9a_row13_col0\" class=\"data row13 col0\" >K Neighbors Classifier</td>\n",
              "      <td id=\"T_fbf9a_row13_col1\" class=\"data row13 col1\" >0.4974</td>\n",
              "      <td id=\"T_fbf9a_row13_col2\" class=\"data row13 col2\" >0.3694</td>\n",
              "      <td id=\"T_fbf9a_row13_col3\" class=\"data row13 col3\" >0.4974</td>\n",
              "      <td id=\"T_fbf9a_row13_col4\" class=\"data row13 col4\" >0.3315</td>\n",
              "      <td id=\"T_fbf9a_row13_col5\" class=\"data row13 col5\" >0.3934</td>\n",
              "      <td id=\"T_fbf9a_row13_col6\" class=\"data row13 col6\" >0.0437</td>\n",
              "      <td id=\"T_fbf9a_row13_col7\" class=\"data row13 col7\" >0.0570</td>\n",
              "      <td id=\"T_fbf9a_row13_col8\" class=\"data row13 col8\" >0.0730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_fbf9a_level0_row14\" class=\"row_heading level0 row14\" >svm</th>\n",
              "      <td id=\"T_fbf9a_row14_col0\" class=\"data row14 col0\" >SVM - Linear Kernel</td>\n",
              "      <td id=\"T_fbf9a_row14_col1\" class=\"data row14 col1\" >0.4195</td>\n",
              "      <td id=\"T_fbf9a_row14_col2\" class=\"data row14 col2\" >0.0000</td>\n",
              "      <td id=\"T_fbf9a_row14_col3\" class=\"data row14 col3\" >0.4195</td>\n",
              "      <td id=\"T_fbf9a_row14_col4\" class=\"data row14 col4\" >0.3503</td>\n",
              "      <td id=\"T_fbf9a_row14_col5\" class=\"data row14 col5\" >0.3416</td>\n",
              "      <td id=\"T_fbf9a_row14_col6\" class=\"data row14 col6\" >0.1016</td>\n",
              "      <td id=\"T_fbf9a_row14_col7\" class=\"data row14 col7\" >0.1329</td>\n",
              "      <td id=\"T_fbf9a_row14_col8\" class=\"data row14 col8\" >0.1020</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
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        },
        {
          "output_type": "display_data",
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              "Processing:   0%|          | 0/65 [00:00<?, ?it/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          "output_type": "execute_result",
          "data": {
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              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=4111, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: /~https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
              "                   multi_class=&#x27;auto&#x27;, n_jobs=None, penalty=&#x27;l2&#x27;,\n",
              "                   random_state=4111, solver=&#x27;lbfgs&#x27;, tol=0.0001, verbose=0,\n",
              "                   warm_start=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
              "                   multi_class=&#x27;auto&#x27;, n_jobs=None, penalty=&#x27;l2&#x27;,\n",
              "                   random_state=4111, solver=&#x27;lbfgs&#x27;, tol=0.0001, verbose=0,\n",
              "                   warm_start=False)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#cleveClass2 = classification.setup(data = cleveland_strat_train, target = 'num',  normalize = True, transformation = True, remove_multicollinearity = True, multicollinearity_threshold = 0.95, train_size = 0.7, test_data =cleveland_strat_test)\n",
        "cleveClass2 = classification.setup(data = cleveland_strat_train, target = 'num', train_size = 1, test_data =cleveland_strat_test)\n"
      ],
      "metadata": {
        "id": "WONbLyvPiddv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "outputId": "4ba88bbb-16b8-4514-b090-2aa47804f987"
      },
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      "outputs": [
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              "<pandas.io.formats.style.Styler at 0x7f2b8b24cc70>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_dd1e6_row8_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_dd1e6\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_dd1e6_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_dd1e6_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_dd1e6_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_dd1e6_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_dd1e6_row0_col1\" class=\"data row0 col1\" >8970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_dd1e6_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_dd1e6_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
              "      <td id=\"T_dd1e6_row1_col1\" class=\"data row1 col1\" >num</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_dd1e6_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_dd1e6_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
              "      <td id=\"T_dd1e6_row2_col1\" class=\"data row2 col1\" >Multiclass</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_dd1e6_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_dd1e6_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
              "      <td id=\"T_dd1e6_row3_col1\" class=\"data row3 col1\" >(303, 14)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_dd1e6_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_dd1e6_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_dd1e6_row4_col1\" class=\"data row4 col1\" >(303, 14)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_dd1e6_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_dd1e6_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
              "      <td id=\"T_dd1e6_row5_col1\" class=\"data row5 col1\" >(242, 14)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_dd1e6_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_dd1e6_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
              "      <td id=\"T_dd1e6_row6_col1\" class=\"data row6 col1\" >(61, 14)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_dd1e6_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_dd1e6_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
              "      <td id=\"T_dd1e6_row7_col1\" class=\"data row7 col1\" >13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_dd1e6_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_dd1e6_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
              "      <td id=\"T_dd1e6_row8_col1\" class=\"data row8 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_dd1e6_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_dd1e6_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
              "      <td id=\"T_dd1e6_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_dd1e6_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_dd1e6_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_dd1e6_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_dd1e6_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_dd1e6_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_dd1e6_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_dd1e6_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_dd1e6_row12_col0\" class=\"data row12 col0\" >Fold Generator</td>\n",
              "      <td id=\"T_dd1e6_row12_col1\" class=\"data row12 col1\" >StratifiedKFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_dd1e6_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_dd1e6_row13_col0\" class=\"data row13 col0\" >Fold Number</td>\n",
              "      <td id=\"T_dd1e6_row13_col1\" class=\"data row13 col1\" >10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_dd1e6_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_dd1e6_row14_col0\" class=\"data row14 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_dd1e6_row14_col1\" class=\"data row14 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_dd1e6_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_dd1e6_row15_col0\" class=\"data row15 col0\" >Use GPU</td>\n",
              "      <td id=\"T_dd1e6_row15_col1\" class=\"data row15 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_dd1e6_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_dd1e6_row16_col0\" class=\"data row16 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_dd1e6_row16_col1\" class=\"data row16 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_dd1e6_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_dd1e6_row17_col0\" class=\"data row17 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_dd1e6_row17_col1\" class=\"data row17 col1\" >clf-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_dd1e6_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "      <td id=\"T_dd1e6_row18_col0\" class=\"data row18 col0\" >USI</td>\n",
              "      <td id=\"T_dd1e6_row18_col1\" class=\"data row18 col1\" >448a</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cleveClass2.compare_models()"
      ],
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          "height": 595,
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              "<IPython.core.display.HTML object>"
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            "text/html": []
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            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_40ed8 th {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_40ed8_row0_col0, #T_40ed8_row0_col2, #T_40ed8_row1_col0, #T_40ed8_row1_col1, #T_40ed8_row1_col2, #T_40ed8_row1_col3, #T_40ed8_row1_col4, #T_40ed8_row1_col5, #T_40ed8_row1_col6, #T_40ed8_row1_col7, #T_40ed8_row2_col0, #T_40ed8_row2_col1, #T_40ed8_row2_col2, #T_40ed8_row2_col3, #T_40ed8_row2_col4, #T_40ed8_row2_col5, #T_40ed8_row2_col6, #T_40ed8_row2_col7, #T_40ed8_row3_col0, #T_40ed8_row3_col1, #T_40ed8_row3_col2, #T_40ed8_row3_col3, #T_40ed8_row3_col4, #T_40ed8_row3_col5, #T_40ed8_row3_col6, #T_40ed8_row3_col7, #T_40ed8_row4_col0, #T_40ed8_row4_col1, #T_40ed8_row4_col3, #T_40ed8_row4_col4, #T_40ed8_row4_col5, #T_40ed8_row4_col6, #T_40ed8_row4_col7, #T_40ed8_row5_col0, #T_40ed8_row5_col1, #T_40ed8_row5_col2, #T_40ed8_row5_col3, #T_40ed8_row5_col4, #T_40ed8_row5_col5, #T_40ed8_row5_col6, #T_40ed8_row5_col7, #T_40ed8_row6_col0, #T_40ed8_row6_col1, #T_40ed8_row6_col2, #T_40ed8_row6_col3, #T_40ed8_row6_col4, #T_40ed8_row6_col5, #T_40ed8_row6_col6, #T_40ed8_row6_col7, #T_40ed8_row7_col0, #T_40ed8_row7_col1, #T_40ed8_row7_col2, #T_40ed8_row7_col3, #T_40ed8_row7_col4, #T_40ed8_row7_col5, #T_40ed8_row7_col6, #T_40ed8_row7_col7, #T_40ed8_row8_col0, #T_40ed8_row8_col1, #T_40ed8_row8_col2, #T_40ed8_row8_col3, #T_40ed8_row8_col4, #T_40ed8_row8_col5, #T_40ed8_row8_col6, #T_40ed8_row8_col7, #T_40ed8_row9_col0, #T_40ed8_row9_col1, #T_40ed8_row9_col2, #T_40ed8_row9_col3, #T_40ed8_row9_col4, #T_40ed8_row9_col5, #T_40ed8_row9_col6, #T_40ed8_row9_col7, #T_40ed8_row10_col0, #T_40ed8_row10_col1, #T_40ed8_row10_col2, #T_40ed8_row10_col3, #T_40ed8_row10_col4, #T_40ed8_row10_col5, #T_40ed8_row10_col6, #T_40ed8_row10_col7, #T_40ed8_row11_col0, #T_40ed8_row11_col1, #T_40ed8_row11_col2, #T_40ed8_row11_col3, #T_40ed8_row11_col4, #T_40ed8_row11_col5, #T_40ed8_row11_col6, #T_40ed8_row11_col7, #T_40ed8_row12_col0, #T_40ed8_row12_col1, #T_40ed8_row12_col2, #T_40ed8_row12_col3, #T_40ed8_row12_col4, #T_40ed8_row12_col5, #T_40ed8_row12_col6, #T_40ed8_row12_col7, #T_40ed8_row13_col0, #T_40ed8_row13_col1, #T_40ed8_row13_col2, #T_40ed8_row13_col3, #T_40ed8_row13_col4, #T_40ed8_row13_col5, #T_40ed8_row13_col6, #T_40ed8_row13_col7, #T_40ed8_row14_col0, #T_40ed8_row14_col1, #T_40ed8_row14_col2, #T_40ed8_row14_col3, #T_40ed8_row14_col4, #T_40ed8_row14_col5, #T_40ed8_row14_col6, #T_40ed8_row14_col7 {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_40ed8_row0_col1, #T_40ed8_row0_col3, #T_40ed8_row0_col4, #T_40ed8_row0_col5, #T_40ed8_row0_col6, #T_40ed8_row0_col7, #T_40ed8_row4_col2 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "}\n",
              "#T_40ed8_row0_col8, #T_40ed8_row2_col8, #T_40ed8_row3_col8, #T_40ed8_row4_col8, #T_40ed8_row5_col8, #T_40ed8_row6_col8, #T_40ed8_row7_col8, #T_40ed8_row8_col8, #T_40ed8_row9_col8, #T_40ed8_row10_col8, #T_40ed8_row11_col8, #T_40ed8_row12_col8, #T_40ed8_row13_col8, #T_40ed8_row14_col8 {\n",
              "  text-align: left;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "#T_40ed8_row1_col8 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_40ed8\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_40ed8_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_40ed8_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th id=\"T_40ed8_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
              "      <th id=\"T_40ed8_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
              "      <th id=\"T_40ed8_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
              "      <th id=\"T_40ed8_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
              "      <th id=\"T_40ed8_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th id=\"T_40ed8_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
              "      <th id=\"T_40ed8_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_40ed8_level0_row0\" class=\"row_heading level0 row0\" >qda</th>\n",
              "      <td id=\"T_40ed8_row0_col0\" class=\"data row0 col0\" >Quadratic Discriminant Analysis</td>\n",
              "      <td id=\"T_40ed8_row0_col1\" class=\"data row0 col1\" >0.6197</td>\n",
              "      <td id=\"T_40ed8_row0_col2\" class=\"data row0 col2\" >0.7679</td>\n",
              "      <td id=\"T_40ed8_row0_col3\" class=\"data row0 col3\" >0.6197</td>\n",
              "      <td id=\"T_40ed8_row0_col4\" class=\"data row0 col4\" >0.5641</td>\n",
              "      <td id=\"T_40ed8_row0_col5\" class=\"data row0 col5\" >0.5832</td>\n",
              "      <td id=\"T_40ed8_row0_col6\" class=\"data row0 col6\" >0.3777</td>\n",
              "      <td id=\"T_40ed8_row0_col7\" class=\"data row0 col7\" >0.3859</td>\n",
              "      <td id=\"T_40ed8_row0_col8\" class=\"data row0 col8\" >0.1590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40ed8_level0_row1\" class=\"row_heading level0 row1\" >ridge</th>\n",
              "      <td id=\"T_40ed8_row1_col0\" class=\"data row1 col0\" >Ridge Classifier</td>\n",
              "      <td id=\"T_40ed8_row1_col1\" class=\"data row1 col1\" >0.5990</td>\n",
              "      <td id=\"T_40ed8_row1_col2\" class=\"data row1 col2\" >0.0000</td>\n",
              "      <td id=\"T_40ed8_row1_col3\" class=\"data row1 col3\" >0.5990</td>\n",
              "      <td id=\"T_40ed8_row1_col4\" class=\"data row1 col4\" >0.5134</td>\n",
              "      <td id=\"T_40ed8_row1_col5\" class=\"data row1 col5\" >0.5420</td>\n",
              "      <td id=\"T_40ed8_row1_col6\" class=\"data row1 col6\" >0.3068</td>\n",
              "      <td id=\"T_40ed8_row1_col7\" class=\"data row1 col7\" >0.3222</td>\n",
              "      <td id=\"T_40ed8_row1_col8\" class=\"data row1 col8\" >0.0720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40ed8_level0_row2\" class=\"row_heading level0 row2\" >lr</th>\n",
              "      <td id=\"T_40ed8_row2_col0\" class=\"data row2 col0\" >Logistic Regression</td>\n",
              "      <td id=\"T_40ed8_row2_col1\" class=\"data row2 col1\" >0.5952</td>\n",
              "      <td id=\"T_40ed8_row2_col2\" class=\"data row2 col2\" >0.8138</td>\n",
              "      <td id=\"T_40ed8_row2_col3\" class=\"data row2 col3\" >0.5952</td>\n",
              "      <td id=\"T_40ed8_row2_col4\" class=\"data row2 col4\" >0.5483</td>\n",
              "      <td id=\"T_40ed8_row2_col5\" class=\"data row2 col5\" >0.5579</td>\n",
              "      <td id=\"T_40ed8_row2_col6\" class=\"data row2 col6\" >0.3340</td>\n",
              "      <td id=\"T_40ed8_row2_col7\" class=\"data row2 col7\" >0.3432</td>\n",
              "      <td id=\"T_40ed8_row2_col8\" class=\"data row2 col8\" >0.4570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40ed8_level0_row3\" class=\"row_heading level0 row3\" >rf</th>\n",
              "      <td id=\"T_40ed8_row3_col0\" class=\"data row3 col0\" >Random Forest Classifier</td>\n",
              "      <td id=\"T_40ed8_row3_col1\" class=\"data row3 col1\" >0.5867</td>\n",
              "      <td id=\"T_40ed8_row3_col2\" class=\"data row3 col2\" >0.8061</td>\n",
              "      <td id=\"T_40ed8_row3_col3\" class=\"data row3 col3\" >0.5867</td>\n",
              "      <td id=\"T_40ed8_row3_col4\" class=\"data row3 col4\" >0.5064</td>\n",
              "      <td id=\"T_40ed8_row3_col5\" class=\"data row3 col5\" >0.5253</td>\n",
              "      <td id=\"T_40ed8_row3_col6\" class=\"data row3 col6\" >0.2807</td>\n",
              "      <td id=\"T_40ed8_row3_col7\" class=\"data row3 col7\" >0.2999</td>\n",
              "      <td id=\"T_40ed8_row3_col8\" class=\"data row3 col8\" >0.5370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40ed8_level0_row4\" class=\"row_heading level0 row4\" >lda</th>\n",
              "      <td id=\"T_40ed8_row4_col0\" class=\"data row4 col0\" >Linear Discriminant Analysis</td>\n",
              "      <td id=\"T_40ed8_row4_col1\" class=\"data row4 col1\" >0.5867</td>\n",
              "      <td id=\"T_40ed8_row4_col2\" class=\"data row4 col2\" >0.8169</td>\n",
              "      <td id=\"T_40ed8_row4_col3\" class=\"data row4 col3\" >0.5867</td>\n",
              "      <td id=\"T_40ed8_row4_col4\" class=\"data row4 col4\" >0.5560</td>\n",
              "      <td id=\"T_40ed8_row4_col5\" class=\"data row4 col5\" >0.5618</td>\n",
              "      <td id=\"T_40ed8_row4_col6\" class=\"data row4 col6\" >0.3296</td>\n",
              "      <td id=\"T_40ed8_row4_col7\" class=\"data row4 col7\" >0.3362</td>\n",
              "      <td id=\"T_40ed8_row4_col8\" class=\"data row4 col8\" >0.0880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40ed8_level0_row5\" class=\"row_heading level0 row5\" >et</th>\n",
              "      <td id=\"T_40ed8_row5_col0\" class=\"data row5 col0\" >Extra Trees Classifier</td>\n",
              "      <td id=\"T_40ed8_row5_col1\" class=\"data row5 col1\" >0.5660</td>\n",
              "      <td id=\"T_40ed8_row5_col2\" class=\"data row5 col2\" >0.8087</td>\n",
              "      <td id=\"T_40ed8_row5_col3\" class=\"data row5 col3\" >0.5660</td>\n",
              "      <td id=\"T_40ed8_row5_col4\" class=\"data row5 col4\" >0.5116</td>\n",
              "      <td id=\"T_40ed8_row5_col5\" class=\"data row5 col5\" >0.5287</td>\n",
              "      <td id=\"T_40ed8_row5_col6\" class=\"data row5 col6\" >0.2730</td>\n",
              "      <td id=\"T_40ed8_row5_col7\" class=\"data row5 col7\" >0.2808</td>\n",
              "      <td id=\"T_40ed8_row5_col8\" class=\"data row5 col8\" >0.4250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40ed8_level0_row6\" class=\"row_heading level0 row6\" >nb</th>\n",
              "      <td id=\"T_40ed8_row6_col0\" class=\"data row6 col0\" >Naive Bayes</td>\n",
              "      <td id=\"T_40ed8_row6_col1\" class=\"data row6 col1\" >0.5582</td>\n",
              "      <td id=\"T_40ed8_row6_col2\" class=\"data row6 col2\" >0.8096</td>\n",
              "      <td id=\"T_40ed8_row6_col3\" class=\"data row6 col3\" >0.5582</td>\n",
              "      <td id=\"T_40ed8_row6_col4\" class=\"data row6 col4\" >0.5556</td>\n",
              "      <td id=\"T_40ed8_row6_col5\" class=\"data row6 col5\" >0.5516</td>\n",
              "      <td id=\"T_40ed8_row6_col6\" class=\"data row6 col6\" >0.3204</td>\n",
              "      <td id=\"T_40ed8_row6_col7\" class=\"data row6 col7\" >0.3299</td>\n",
              "      <td id=\"T_40ed8_row6_col8\" class=\"data row6 col8\" >0.0900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40ed8_level0_row7\" class=\"row_heading level0 row7\" >gbc</th>\n",
              "      <td id=\"T_40ed8_row7_col0\" class=\"data row7 col0\" >Gradient Boosting Classifier</td>\n",
              "      <td id=\"T_40ed8_row7_col1\" class=\"data row7 col1\" >0.5495</td>\n",
              "      <td id=\"T_40ed8_row7_col2\" class=\"data row7 col2\" >0.7836</td>\n",
              "      <td id=\"T_40ed8_row7_col3\" class=\"data row7 col3\" >0.5495</td>\n",
              "      <td id=\"T_40ed8_row7_col4\" class=\"data row7 col4\" >0.5057</td>\n",
              "      <td id=\"T_40ed8_row7_col5\" class=\"data row7 col5\" >0.5218</td>\n",
              "      <td id=\"T_40ed8_row7_col6\" class=\"data row7 col6\" >0.2730</td>\n",
              "      <td id=\"T_40ed8_row7_col7\" class=\"data row7 col7\" >0.2778</td>\n",
              "      <td id=\"T_40ed8_row7_col8\" class=\"data row7 col8\" >1.3370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40ed8_level0_row8\" class=\"row_heading level0 row8\" >xgboost</th>\n",
              "      <td id=\"T_40ed8_row8_col0\" class=\"data row8 col0\" >Extreme Gradient Boosting</td>\n",
              "      <td id=\"T_40ed8_row8_col1\" class=\"data row8 col1\" >0.5493</td>\n",
              "      <td id=\"T_40ed8_row8_col2\" class=\"data row8 col2\" >0.7845</td>\n",
              "      <td id=\"T_40ed8_row8_col3\" class=\"data row8 col3\" >0.5493</td>\n",
              "      <td id=\"T_40ed8_row8_col4\" class=\"data row8 col4\" >0.4941</td>\n",
              "      <td id=\"T_40ed8_row8_col5\" class=\"data row8 col5\" >0.5150</td>\n",
              "      <td id=\"T_40ed8_row8_col6\" class=\"data row8 col6\" >0.2693</td>\n",
              "      <td id=\"T_40ed8_row8_col7\" class=\"data row8 col7\" >0.2767</td>\n",
              "      <td id=\"T_40ed8_row8_col8\" class=\"data row8 col8\" >0.2540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40ed8_level0_row9\" class=\"row_heading level0 row9\" >dummy</th>\n",
              "      <td id=\"T_40ed8_row9_col0\" class=\"data row9 col0\" >Dummy Classifier</td>\n",
              "      <td id=\"T_40ed8_row9_col1\" class=\"data row9 col1\" >0.5415</td>\n",
              "      <td id=\"T_40ed8_row9_col2\" class=\"data row9 col2\" >0.5000</td>\n",
              "      <td id=\"T_40ed8_row9_col3\" class=\"data row9 col3\" >0.5415</td>\n",
              "      <td id=\"T_40ed8_row9_col4\" class=\"data row9 col4\" >0.2935</td>\n",
              "      <td id=\"T_40ed8_row9_col5\" class=\"data row9 col5\" >0.3806</td>\n",
              "      <td id=\"T_40ed8_row9_col6\" class=\"data row9 col6\" >0.0000</td>\n",
              "      <td id=\"T_40ed8_row9_col7\" class=\"data row9 col7\" >0.0000</td>\n",
              "      <td id=\"T_40ed8_row9_col8\" class=\"data row9 col8\" >0.1120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40ed8_level0_row10\" class=\"row_heading level0 row10\" >lightgbm</th>\n",
              "      <td id=\"T_40ed8_row10_col0\" class=\"data row10 col0\" >Light Gradient Boosting Machine</td>\n",
              "      <td id=\"T_40ed8_row10_col1\" class=\"data row10 col1\" >0.5412</td>\n",
              "      <td id=\"T_40ed8_row10_col2\" class=\"data row10 col2\" >0.7884</td>\n",
              "      <td id=\"T_40ed8_row10_col3\" class=\"data row10 col3\" >0.5412</td>\n",
              "      <td id=\"T_40ed8_row10_col4\" class=\"data row10 col4\" >0.4863</td>\n",
              "      <td id=\"T_40ed8_row10_col5\" class=\"data row10 col5\" >0.5085</td>\n",
              "      <td id=\"T_40ed8_row10_col6\" class=\"data row10 col6\" >0.2553</td>\n",
              "      <td id=\"T_40ed8_row10_col7\" class=\"data row10 col7\" >0.2622</td>\n",
              "      <td id=\"T_40ed8_row10_col8\" class=\"data row10 col8\" >0.4370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40ed8_level0_row11\" class=\"row_heading level0 row11\" >ada</th>\n",
              "      <td id=\"T_40ed8_row11_col0\" class=\"data row11 col0\" >Ada Boost Classifier</td>\n",
              "      <td id=\"T_40ed8_row11_col1\" class=\"data row11 col1\" >0.5373</td>\n",
              "      <td id=\"T_40ed8_row11_col2\" class=\"data row11 col2\" >0.7177</td>\n",
              "      <td id=\"T_40ed8_row11_col3\" class=\"data row11 col3\" >0.5373</td>\n",
              "      <td id=\"T_40ed8_row11_col4\" class=\"data row11 col4\" >0.5032</td>\n",
              "      <td id=\"T_40ed8_row11_col5\" class=\"data row11 col5\" >0.5135</td>\n",
              "      <td id=\"T_40ed8_row11_col6\" class=\"data row11 col6\" >0.2685</td>\n",
              "      <td id=\"T_40ed8_row11_col7\" class=\"data row11 col7\" >0.2756</td>\n",
              "      <td id=\"T_40ed8_row11_col8\" class=\"data row11 col8\" >0.2900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40ed8_level0_row12\" class=\"row_heading level0 row12\" >knn</th>\n",
              "      <td id=\"T_40ed8_row12_col0\" class=\"data row12 col0\" >K Neighbors Classifier</td>\n",
              "      <td id=\"T_40ed8_row12_col1\" class=\"data row12 col1\" >0.5213</td>\n",
              "      <td id=\"T_40ed8_row12_col2\" class=\"data row12 col2\" >0.5830</td>\n",
              "      <td id=\"T_40ed8_row12_col3\" class=\"data row12 col3\" >0.5213</td>\n",
              "      <td id=\"T_40ed8_row12_col4\" class=\"data row12 col4\" >0.3759</td>\n",
              "      <td id=\"T_40ed8_row12_col5\" class=\"data row12 col5\" >0.4225</td>\n",
              "      <td id=\"T_40ed8_row12_col6\" class=\"data row12 col6\" >0.0853</td>\n",
              "      <td id=\"T_40ed8_row12_col7\" class=\"data row12 col7\" >0.1077</td>\n",
              "      <td id=\"T_40ed8_row12_col8\" class=\"data row12 col8\" >0.0980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40ed8_level0_row13\" class=\"row_heading level0 row13\" >dt</th>\n",
              "      <td id=\"T_40ed8_row13_col0\" class=\"data row13 col0\" >Decision Tree Classifier</td>\n",
              "      <td id=\"T_40ed8_row13_col1\" class=\"data row13 col1\" >0.4920</td>\n",
              "      <td id=\"T_40ed8_row13_col2\" class=\"data row13 col2\" >0.6418</td>\n",
              "      <td id=\"T_40ed8_row13_col3\" class=\"data row13 col3\" >0.4920</td>\n",
              "      <td id=\"T_40ed8_row13_col4\" class=\"data row13 col4\" >0.4854</td>\n",
              "      <td id=\"T_40ed8_row13_col5\" class=\"data row13 col5\" >0.4817</td>\n",
              "      <td id=\"T_40ed8_row13_col6\" class=\"data row13 col6\" >0.2148</td>\n",
              "      <td id=\"T_40ed8_row13_col7\" class=\"data row13 col7\" >0.2202</td>\n",
              "      <td id=\"T_40ed8_row13_col8\" class=\"data row13 col8\" >0.0800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40ed8_level0_row14\" class=\"row_heading level0 row14\" >svm</th>\n",
              "      <td id=\"T_40ed8_row14_col0\" class=\"data row14 col0\" >SVM - Linear Kernel</td>\n",
              "      <td id=\"T_40ed8_row14_col1\" class=\"data row14 col1\" >0.4182</td>\n",
              "      <td id=\"T_40ed8_row14_col2\" class=\"data row14 col2\" >0.0000</td>\n",
              "      <td id=\"T_40ed8_row14_col3\" class=\"data row14 col3\" >0.4182</td>\n",
              "      <td id=\"T_40ed8_row14_col4\" class=\"data row14 col4\" >0.3043</td>\n",
              "      <td id=\"T_40ed8_row14_col5\" class=\"data row14 col5\" >0.3340</td>\n",
              "      <td id=\"T_40ed8_row14_col6\" class=\"data row14 col6\" >0.1093</td>\n",
              "      <td id=\"T_40ed8_row14_col7\" class=\"data row14 col7\" >0.1428</td>\n",
              "      <td id=\"T_40ed8_row14_col8\" class=\"data row14 col8\" >0.0750</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0,\n",
              "                              store_covariance=False, tol=0.0001)"
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              "                              store_covariance=False, tol=0.0001)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">QuadraticDiscriminantAnalysis</label><div class=\"sk-toggleable__content\"><pre>QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0,\n",
              "                              store_covariance=False, tol=0.0001)</pre></div></div></div></div></div>"
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      "cell_type": "code",
      "source": [
        "cleveClass3 = classification.setup(data = cleveland, target = 'num',  normalize = True, transformation = True, remove_multicollinearity = True, multicollinearity_threshold = 0.95)"
      ],
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      "outputs": [
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              "<pandas.io.formats.style.Styler at 0x7f2b8f8bc5b0>"
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              "<style type=\"text/css\">\n",
              "#T_21080_row8_col1, #T_21080_row12_col1, #T_21080_row14_col1, #T_21080_row16_col1 {\n",
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              "</style>\n",
              "<table id=\"T_21080\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_21080_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_21080_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_21080_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_21080_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_21080_row0_col1\" class=\"data row0 col1\" >4905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21080_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_21080_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
              "      <td id=\"T_21080_row1_col1\" class=\"data row1 col1\" >num</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21080_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_21080_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
              "      <td id=\"T_21080_row2_col1\" class=\"data row2 col1\" >Multiclass</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21080_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_21080_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
              "      <td id=\"T_21080_row3_col1\" class=\"data row3 col1\" >(303, 14)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21080_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_21080_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_21080_row4_col1\" class=\"data row4 col1\" >(303, 14)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21080_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_21080_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
              "      <td id=\"T_21080_row5_col1\" class=\"data row5 col1\" >(212, 14)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21080_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_21080_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
              "      <td id=\"T_21080_row6_col1\" class=\"data row6 col1\" >(91, 14)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21080_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_21080_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
              "      <td id=\"T_21080_row7_col1\" class=\"data row7 col1\" >13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21080_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_21080_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
              "      <td id=\"T_21080_row8_col1\" class=\"data row8 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21080_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_21080_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
              "      <td id=\"T_21080_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21080_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_21080_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_21080_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21080_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_21080_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_21080_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21080_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_21080_row12_col0\" class=\"data row12 col0\" >Remove multicollinearity</td>\n",
              "      <td id=\"T_21080_row12_col1\" class=\"data row12 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21080_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_21080_row13_col0\" class=\"data row13 col0\" >Multicollinearity threshold</td>\n",
              "      <td id=\"T_21080_row13_col1\" class=\"data row13 col1\" >0.950000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21080_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_21080_row14_col0\" class=\"data row14 col0\" >Transformation</td>\n",
              "      <td id=\"T_21080_row14_col1\" class=\"data row14 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21080_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_21080_row15_col0\" class=\"data row15 col0\" >Transformation method</td>\n",
              "      <td id=\"T_21080_row15_col1\" class=\"data row15 col1\" >yeo-johnson</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21080_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_21080_row16_col0\" class=\"data row16 col0\" >Normalize</td>\n",
              "      <td id=\"T_21080_row16_col1\" class=\"data row16 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21080_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_21080_row17_col0\" class=\"data row17 col0\" >Normalize method</td>\n",
              "      <td id=\"T_21080_row17_col1\" class=\"data row17 col1\" >zscore</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21080_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "      <td id=\"T_21080_row18_col0\" class=\"data row18 col0\" >Fold Generator</td>\n",
              "      <td id=\"T_21080_row18_col1\" class=\"data row18 col1\" >StratifiedKFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21080_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
              "      <td id=\"T_21080_row19_col0\" class=\"data row19 col0\" >Fold Number</td>\n",
              "      <td id=\"T_21080_row19_col1\" class=\"data row19 col1\" >10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21080_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
              "      <td id=\"T_21080_row20_col0\" class=\"data row20 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_21080_row20_col1\" class=\"data row20 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21080_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
              "      <td id=\"T_21080_row21_col0\" class=\"data row21 col0\" >Use GPU</td>\n",
              "      <td id=\"T_21080_row21_col1\" class=\"data row21 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21080_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
              "      <td id=\"T_21080_row22_col0\" class=\"data row22 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_21080_row22_col1\" class=\"data row22 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21080_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
              "      <td id=\"T_21080_row23_col0\" class=\"data row23 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_21080_row23_col1\" class=\"data row23 col1\" >clf-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21080_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
              "      <td id=\"T_21080_row24_col0\" class=\"data row24 col0\" >USI</td>\n",
              "      <td id=\"T_21080_row24_col1\" class=\"data row24 col1\" >123d</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "<table id=\"T_042fd\" class=\"dataframe\">\n",
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              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_042fd_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_042fd_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th id=\"T_042fd_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
              "      <th id=\"T_042fd_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
              "      <th id=\"T_042fd_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
              "      <th id=\"T_042fd_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
              "      <th id=\"T_042fd_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th id=\"T_042fd_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
              "      <th id=\"T_042fd_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_042fd_level0_row0\" class=\"row_heading level0 row0\" >ridge</th>\n",
              "      <td id=\"T_042fd_row0_col0\" class=\"data row0 col0\" >Ridge Classifier</td>\n",
              "      <td id=\"T_042fd_row0_col1\" class=\"data row0 col1\" >0.6072</td>\n",
              "      <td id=\"T_042fd_row0_col2\" class=\"data row0 col2\" >0.0000</td>\n",
              "      <td id=\"T_042fd_row0_col3\" class=\"data row0 col3\" >0.6072</td>\n",
              "      <td id=\"T_042fd_row0_col4\" class=\"data row0 col4\" >0.5197</td>\n",
              "      <td id=\"T_042fd_row0_col5\" class=\"data row0 col5\" >0.5505</td>\n",
              "      <td id=\"T_042fd_row0_col6\" class=\"data row0 col6\" >0.3182</td>\n",
              "      <td id=\"T_042fd_row0_col7\" class=\"data row0 col7\" >0.3330</td>\n",
              "      <td id=\"T_042fd_row0_col8\" class=\"data row0 col8\" >0.3930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_042fd_level0_row1\" class=\"row_heading level0 row1\" >rf</th>\n",
              "      <td id=\"T_042fd_row1_col0\" class=\"data row1 col0\" >Random Forest Classifier</td>\n",
              "      <td id=\"T_042fd_row1_col1\" class=\"data row1 col1\" >0.5865</td>\n",
              "      <td id=\"T_042fd_row1_col2\" class=\"data row1 col2\" >0.8179</td>\n",
              "      <td id=\"T_042fd_row1_col3\" class=\"data row1 col3\" >0.5865</td>\n",
              "      <td id=\"T_042fd_row1_col4\" class=\"data row1 col4\" >0.5233</td>\n",
              "      <td id=\"T_042fd_row1_col5\" class=\"data row1 col5\" >0.5381</td>\n",
              "      <td id=\"T_042fd_row1_col6\" class=\"data row1 col6\" >0.2940</td>\n",
              "      <td id=\"T_042fd_row1_col7\" class=\"data row1 col7\" >0.3073</td>\n",
              "      <td id=\"T_042fd_row1_col8\" class=\"data row1 col8\" >0.8460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_042fd_level0_row2\" class=\"row_heading level0 row2\" >knn</th>\n",
              "      <td id=\"T_042fd_row2_col0\" class=\"data row2 col0\" >K Neighbors Classifier</td>\n",
              "      <td id=\"T_042fd_row2_col1\" class=\"data row2 col1\" >0.5742</td>\n",
              "      <td id=\"T_042fd_row2_col2\" class=\"data row2 col2\" >0.7800</td>\n",
              "      <td id=\"T_042fd_row2_col3\" class=\"data row2 col3\" >0.5742</td>\n",
              "      <td id=\"T_042fd_row2_col4\" class=\"data row2 col4\" >0.4916</td>\n",
              "      <td id=\"T_042fd_row2_col5\" class=\"data row2 col5\" >0.5255</td>\n",
              "      <td id=\"T_042fd_row2_col6\" class=\"data row2 col6\" >0.2827</td>\n",
              "      <td id=\"T_042fd_row2_col7\" class=\"data row2 col7\" >0.2906</td>\n",
              "      <td id=\"T_042fd_row2_col8\" class=\"data row2 col8\" >0.2150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_042fd_level0_row3\" class=\"row_heading level0 row3\" >qda</th>\n",
              "      <td id=\"T_042fd_row3_col0\" class=\"data row3 col0\" >Quadratic Discriminant Analysis</td>\n",
              "      <td id=\"T_042fd_row3_col1\" class=\"data row3 col1\" >0.5740</td>\n",
              "      <td id=\"T_042fd_row3_col2\" class=\"data row3 col2\" >0.7678</td>\n",
              "      <td id=\"T_042fd_row3_col3\" class=\"data row3 col3\" >0.5740</td>\n",
              "      <td id=\"T_042fd_row3_col4\" class=\"data row3 col4\" >0.5287</td>\n",
              "      <td id=\"T_042fd_row3_col5\" class=\"data row3 col5\" >0.5378</td>\n",
              "      <td id=\"T_042fd_row3_col6\" class=\"data row3 col6\" >0.2888</td>\n",
              "      <td id=\"T_042fd_row3_col7\" class=\"data row3 col7\" >0.2969</td>\n",
              "      <td id=\"T_042fd_row3_col8\" class=\"data row3 col8\" >0.3660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_042fd_level0_row4\" class=\"row_heading level0 row4\" >lr</th>\n",
              "      <td id=\"T_042fd_row4_col0\" class=\"data row4 col0\" >Logistic Regression</td>\n",
              "      <td id=\"T_042fd_row4_col1\" class=\"data row4 col1\" >0.5618</td>\n",
              "      <td id=\"T_042fd_row4_col2\" class=\"data row4 col2\" >0.8158</td>\n",
              "      <td id=\"T_042fd_row4_col3\" class=\"data row4 col3\" >0.5618</td>\n",
              "      <td id=\"T_042fd_row4_col4\" class=\"data row4 col4\" >0.5423</td>\n",
              "      <td id=\"T_042fd_row4_col5\" class=\"data row4 col5\" >0.5377</td>\n",
              "      <td id=\"T_042fd_row4_col6\" class=\"data row4 col6\" >0.2883</td>\n",
              "      <td id=\"T_042fd_row4_col7\" class=\"data row4 col7\" >0.2946</td>\n",
              "      <td id=\"T_042fd_row4_col8\" class=\"data row4 col8\" >0.5180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_042fd_level0_row5\" class=\"row_heading level0 row5\" >ada</th>\n",
              "      <td id=\"T_042fd_row5_col0\" class=\"data row5 col0\" >Ada Boost Classifier</td>\n",
              "      <td id=\"T_042fd_row5_col1\" class=\"data row5 col1\" >0.5373</td>\n",
              "      <td id=\"T_042fd_row5_col2\" class=\"data row5 col2\" >0.7165</td>\n",
              "      <td id=\"T_042fd_row5_col3\" class=\"data row5 col3\" >0.5373</td>\n",
              "      <td id=\"T_042fd_row5_col4\" class=\"data row5 col4\" >0.5043</td>\n",
              "      <td id=\"T_042fd_row5_col5\" class=\"data row5 col5\" >0.5142</td>\n",
              "      <td id=\"T_042fd_row5_col6\" class=\"data row5 col6\" >0.2687</td>\n",
              "      <td id=\"T_042fd_row5_col7\" class=\"data row5 col7\" >0.2755</td>\n",
              "      <td id=\"T_042fd_row5_col8\" class=\"data row5 col8\" >0.3930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_042fd_level0_row6\" class=\"row_heading level0 row6\" >svm</th>\n",
              "      <td id=\"T_042fd_row6_col0\" class=\"data row6 col0\" >SVM - Linear Kernel</td>\n",
              "      <td id=\"T_042fd_row6_col1\" class=\"data row6 col1\" >0.5337</td>\n",
              "      <td id=\"T_042fd_row6_col2\" class=\"data row6 col2\" >0.0000</td>\n",
              "      <td id=\"T_042fd_row6_col3\" class=\"data row6 col3\" >0.5337</td>\n",
              "      <td id=\"T_042fd_row6_col4\" class=\"data row6 col4\" >0.5110</td>\n",
              "      <td id=\"T_042fd_row6_col5\" class=\"data row6 col5\" >0.5131</td>\n",
              "      <td id=\"T_042fd_row6_col6\" class=\"data row6 col6\" >0.2589</td>\n",
              "      <td id=\"T_042fd_row6_col7\" class=\"data row6 col7\" >0.2674</td>\n",
              "      <td id=\"T_042fd_row6_col8\" class=\"data row6 col8\" >0.2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_042fd_level0_row7\" class=\"row_heading level0 row7\" >nb</th>\n",
              "      <td id=\"T_042fd_row7_col0\" class=\"data row7 col0\" >Naive Bayes</td>\n",
              "      <td id=\"T_042fd_row7_col1\" class=\"data row7 col1\" >0.5248</td>\n",
              "      <td id=\"T_042fd_row7_col2\" class=\"data row7 col2\" >0.7980</td>\n",
              "      <td id=\"T_042fd_row7_col3\" class=\"data row7 col3\" >0.5248</td>\n",
              "      <td id=\"T_042fd_row7_col4\" class=\"data row7 col4\" >0.5565</td>\n",
              "      <td id=\"T_042fd_row7_col5\" class=\"data row7 col5\" >0.5287</td>\n",
              "      <td id=\"T_042fd_row7_col6\" class=\"data row7 col6\" >0.2802</td>\n",
              "      <td id=\"T_042fd_row7_col7\" class=\"data row7 col7\" >0.2903</td>\n",
              "      <td id=\"T_042fd_row7_col8\" class=\"data row7 col8\" >0.1920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_042fd_level0_row8\" class=\"row_heading level0 row8\" >dt</th>\n",
              "      <td id=\"T_042fd_row8_col0\" class=\"data row8 col0\" >Decision Tree Classifier</td>\n",
              "      <td id=\"T_042fd_row8_col1\" class=\"data row8 col1\" >0.5040</td>\n",
              "      <td id=\"T_042fd_row8_col2\" class=\"data row8 col2\" >0.6662</td>\n",
              "      <td id=\"T_042fd_row8_col3\" class=\"data row8 col3\" >0.5040</td>\n",
              "      <td id=\"T_042fd_row8_col4\" class=\"data row8 col4\" >0.5240</td>\n",
              "      <td id=\"T_042fd_row8_col5\" class=\"data row8 col5\" >0.5039</td>\n",
              "      <td id=\"T_042fd_row8_col6\" class=\"data row8 col6\" >0.2465</td>\n",
              "      <td id=\"T_042fd_row8_col7\" class=\"data row8 col7\" >0.2529</td>\n",
              "      <td id=\"T_042fd_row8_col8\" class=\"data row8 col8\" >0.1010</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
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              "Processing:   0%|          | 0/65 [00:00<?, ?it/s]"
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        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-20-633eb0ca4316>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcleveClass\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompare_models\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pycaret/classification/oop.py\u001b[0m in \u001b[0;36mcompare_models\u001b[0;34m(self, include, exclude, fold, round, cross_validation, sort, n_select, budget_time, turbo, errors, fit_kwargs, groups, experiment_custom_tags, probability_threshold, engine, verbose, parallel)\u001b[0m\n\u001b[1;32m   1190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1191\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1192\u001b[0;31m             return_values = super().compare_models(\n\u001b[0m\u001b[1;32m   1193\u001b[0m                 \u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1194\u001b[0m                 \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pycaret/internal/pycaret_experiment/supervised_experiment.py\u001b[0m in \u001b[0;36mcompare_models\u001b[0;34m(self, include, exclude, fold, round, cross_validation, sort, n_select, budget_time, turbo, errors, fit_kwargs, groups, experiment_custom_tags, probability_threshold, verbose, parallel, caller_params)\u001b[0m\n\u001b[1;32m    792\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    793\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 794\u001b[0;31m                     \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_fit_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mcreate_model_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    795\u001b[0m                     \u001b[0mmodel_results\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    796\u001b[0m                     assert (\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pycaret/internal/pycaret_experiment/supervised_experiment.py\u001b[0m in \u001b[0;36m_create_model\u001b[0;34m(self, estimator, fold, round, cross_validation, predict, fit_kwargs, groups, refit, probability_threshold, experiment_custom_tags, verbose, system, add_to_model_list, X_train_data, y_train_data, metrics, display, model_only, return_train_score, **kwargs)\u001b[0m\n\u001b[1;32m   1521\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1523\u001b[0;31m         model, model_fit_time, model_results, _ = self._create_model_with_cv(\n\u001b[0m\u001b[1;32m   1524\u001b[0m             \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1525\u001b[0m             \u001b[0mdata_X\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pycaret/internal/pycaret_experiment/supervised_experiment.py\u001b[0m in \u001b[0;36m_create_model_with_cv\u001b[0;34m(self, model, data_X, data_y, fit_kwargs, round, cv, groups, metrics, refit, system, display, return_train_score)\u001b[0m\n\u001b[1;32m   1115\u001b[0m             \u001b[0mmodel_fit_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1116\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mredirect_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1117\u001b[0;31m                 scores = cross_validate(\n\u001b[0m\u001b[1;32m   1118\u001b[0m                     \u001b[0mpipeline_with_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1119\u001b[0m                     \u001b[0mdata_X\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36mcross_validate\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001b[0m\n\u001b[1;32m    264\u001b[0m     \u001b[0;31m# independent, and that it is pickle-able.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    265\u001b[0m     \u001b[0mparallel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParallel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpre_dispatch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpre_dispatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 266\u001b[0;31m     results = parallel(\n\u001b[0m\u001b[1;32m    267\u001b[0m         delayed(_fit_and_score)(\n\u001b[1;32m    268\u001b[0m             \u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/sklearn/utils/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mdelayed_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         )\n\u001b[0;32m---> 63\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable_with_config\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1059\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1060\u001b[0m             \u001b[0;31m# The main thread will consume the first pre_dispatch items and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1061\u001b[0;31m             \u001b[0;31m# the remaining items will later be lazily dispatched by async\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1062\u001b[0m             \u001b[0;31m# callbacks upon task completions.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1063\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    936\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    937\u001b[0m             \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_completed_tasks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 938\u001b[0;31m             \u001b[0;31m# We are finished dispatching\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    939\u001b[0m             \u001b[0mtotal_tasks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_dispatched_tasks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    940\u001b[0m             \u001b[0;31m# We always display the first loop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m    540\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    541\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m         \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0min_main_thread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnesting_level\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    543\u001b[0m             \u001b[0;31m# Prevent posix fork inside in non-main posix threads\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn_jobs\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.9/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    439\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 441\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    442\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    443\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mCANCELLED\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCANCELLED_AND_NOTIFIED\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.9/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    310\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    311\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 312\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    313\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    314\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.classification import *\n",
        "\n",
        "ridgeModel = create_model('ridge', fold = 10)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457,
          "referenced_widgets": [
            "0f415b9e879e4e428262ce87f0f3f921",
            "61d92b993b99416392b7182f4d9e3fe9",
            "a7ec6120932d4f6e8c8b3065212e7dad",
            "138afd217cee41c4ae1bebdcd2033fff",
            "b2b3fc7b3564483ea1588b2792e053eb",
            "480880d6e8cb4681a6325120c612ca37",
            "25efb7a3adae4c06ab31a4a35a2f0856",
            "8810b68f990a4f2fb168689a217c9fe5",
            "213c9cc422614833ab9cbd194bc26870",
            "a036fe1df9354d70a9b61c69ce744864",
            "e2dddd8c85664ac9a068a4170afbf4d0"
          ]
        },
        "id": "mGnENsRku1Px",
        "outputId": "8b8515d4-2c67-4163-c977-0e968121468d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f124c3c6c10>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_bbbe4_row10_col0, #T_bbbe4_row10_col1, #T_bbbe4_row10_col2, #T_bbbe4_row10_col3, #T_bbbe4_row10_col4, #T_bbbe4_row10_col5, #T_bbbe4_row10_col6 {\n",
              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_bbbe4\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_bbbe4_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
              "      <th id=\"T_bbbe4_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
              "      <th id=\"T_bbbe4_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
              "      <th id=\"T_bbbe4_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
              "      <th id=\"T_bbbe4_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
              "      <th id=\"T_bbbe4_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
              "      <th id=\"T_bbbe4_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >Fold</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "      <th class=\"blank col5\" >&nbsp;</th>\n",
              "      <th class=\"blank col6\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_bbbe4_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_bbbe4_row0_col0\" class=\"data row0 col0\" >0.5000</td>\n",
              "      <td id=\"T_bbbe4_row0_col1\" class=\"data row0 col1\" >0.0000</td>\n",
              "      <td id=\"T_bbbe4_row0_col2\" class=\"data row0 col2\" >0.5000</td>\n",
              "      <td id=\"T_bbbe4_row0_col3\" class=\"data row0 col3\" >0.3235</td>\n",
              "      <td id=\"T_bbbe4_row0_col4\" class=\"data row0 col4\" >0.3929</td>\n",
              "      <td id=\"T_bbbe4_row0_col5\" class=\"data row0 col5\" >0.1326</td>\n",
              "      <td id=\"T_bbbe4_row0_col6\" class=\"data row0 col6\" >0.1514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bbbe4_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_bbbe4_row1_col0\" class=\"data row1 col0\" >0.5909</td>\n",
              "      <td id=\"T_bbbe4_row1_col1\" class=\"data row1 col1\" >0.0000</td>\n",
              "      <td id=\"T_bbbe4_row1_col2\" class=\"data row1 col2\" >0.5909</td>\n",
              "      <td id=\"T_bbbe4_row1_col3\" class=\"data row1 col3\" >0.5367</td>\n",
              "      <td id=\"T_bbbe4_row1_col4\" class=\"data row1 col4\" >0.5583</td>\n",
              "      <td id=\"T_bbbe4_row1_col5\" class=\"data row1 col5\" >0.3613</td>\n",
              "      <td id=\"T_bbbe4_row1_col6\" class=\"data row1 col6\" >0.3657</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bbbe4_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_bbbe4_row2_col0\" class=\"data row2 col0\" >0.5714</td>\n",
              "      <td id=\"T_bbbe4_row2_col1\" class=\"data row2 col1\" >0.0000</td>\n",
              "      <td id=\"T_bbbe4_row2_col2\" class=\"data row2 col2\" >0.5714</td>\n",
              "      <td id=\"T_bbbe4_row2_col3\" class=\"data row2 col3\" >0.4789</td>\n",
              "      <td id=\"T_bbbe4_row2_col4\" class=\"data row2 col4\" >0.5210</td>\n",
              "      <td id=\"T_bbbe4_row2_col5\" class=\"data row2 col5\" >0.3152</td>\n",
              "      <td id=\"T_bbbe4_row2_col6\" class=\"data row2 col6\" >0.3220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bbbe4_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_bbbe4_row3_col0\" class=\"data row3 col0\" >0.5238</td>\n",
              "      <td id=\"T_bbbe4_row3_col1\" class=\"data row3 col1\" >0.0000</td>\n",
              "      <td id=\"T_bbbe4_row3_col2\" class=\"data row3 col2\" >0.5238</td>\n",
              "      <td id=\"T_bbbe4_row3_col3\" class=\"data row3 col3\" >0.4386</td>\n",
              "      <td id=\"T_bbbe4_row3_col4\" class=\"data row3 col4\" >0.4773</td>\n",
              "      <td id=\"T_bbbe4_row3_col5\" class=\"data row3 col5\" >0.2336</td>\n",
              "      <td id=\"T_bbbe4_row3_col6\" class=\"data row3 col6\" >0.2418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bbbe4_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_bbbe4_row4_col0\" class=\"data row4 col0\" >0.6190</td>\n",
              "      <td id=\"T_bbbe4_row4_col1\" class=\"data row4 col1\" >0.0000</td>\n",
              "      <td id=\"T_bbbe4_row4_col2\" class=\"data row4 col2\" >0.6190</td>\n",
              "      <td id=\"T_bbbe4_row4_col3\" class=\"data row4 col3\" >0.5629</td>\n",
              "      <td id=\"T_bbbe4_row4_col4\" class=\"data row4 col4\" >0.5886</td>\n",
              "      <td id=\"T_bbbe4_row4_col5\" class=\"data row4 col5\" >0.3563</td>\n",
              "      <td id=\"T_bbbe4_row4_col6\" class=\"data row4 col6\" >0.3595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bbbe4_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_bbbe4_row5_col0\" class=\"data row5 col0\" >0.6667</td>\n",
              "      <td id=\"T_bbbe4_row5_col1\" class=\"data row5 col1\" >0.0000</td>\n",
              "      <td id=\"T_bbbe4_row5_col2\" class=\"data row5 col2\" >0.6667</td>\n",
              "      <td id=\"T_bbbe4_row5_col3\" class=\"data row5 col3\" >0.4510</td>\n",
              "      <td id=\"T_bbbe4_row5_col4\" class=\"data row5 col4\" >0.5364</td>\n",
              "      <td id=\"T_bbbe4_row5_col5\" class=\"data row5 col5\" >0.3581</td>\n",
              "      <td id=\"T_bbbe4_row5_col6\" class=\"data row5 col6\" >0.4263</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bbbe4_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_bbbe4_row6_col0\" class=\"data row6 col0\" >0.6667</td>\n",
              "      <td id=\"T_bbbe4_row6_col1\" class=\"data row6 col1\" >0.0000</td>\n",
              "      <td id=\"T_bbbe4_row6_col2\" class=\"data row6 col2\" >0.6667</td>\n",
              "      <td id=\"T_bbbe4_row6_col3\" class=\"data row6 col3\" >0.5215</td>\n",
              "      <td id=\"T_bbbe4_row6_col4\" class=\"data row6 col4\" >0.5751</td>\n",
              "      <td id=\"T_bbbe4_row6_col5\" class=\"data row6 col5\" >0.4324</td>\n",
              "      <td id=\"T_bbbe4_row6_col6\" class=\"data row6 col6\" >0.4709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bbbe4_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_bbbe4_row7_col0\" class=\"data row7 col0\" >0.6190</td>\n",
              "      <td id=\"T_bbbe4_row7_col1\" class=\"data row7 col1\" >0.0000</td>\n",
              "      <td id=\"T_bbbe4_row7_col2\" class=\"data row7 col2\" >0.6190</td>\n",
              "      <td id=\"T_bbbe4_row7_col3\" class=\"data row7 col3\" >0.4286</td>\n",
              "      <td id=\"T_bbbe4_row7_col4\" class=\"data row7 col4\" >0.5048</td>\n",
              "      <td id=\"T_bbbe4_row7_col5\" class=\"data row7 col5\" >0.2258</td>\n",
              "      <td id=\"T_bbbe4_row7_col6\" class=\"data row7 col6\" >0.2807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bbbe4_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_bbbe4_row8_col0\" class=\"data row8 col0\" >0.5714</td>\n",
              "      <td id=\"T_bbbe4_row8_col1\" class=\"data row8 col1\" >0.0000</td>\n",
              "      <td id=\"T_bbbe4_row8_col2\" class=\"data row8 col2\" >0.5714</td>\n",
              "      <td id=\"T_bbbe4_row8_col3\" class=\"data row8 col3\" >0.4650</td>\n",
              "      <td id=\"T_bbbe4_row8_col4\" class=\"data row8 col4\" >0.4970</td>\n",
              "      <td id=\"T_bbbe4_row8_col5\" class=\"data row8 col5\" >0.1600</td>\n",
              "      <td id=\"T_bbbe4_row8_col6\" class=\"data row8 col6\" >0.1807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bbbe4_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_bbbe4_row9_col0\" class=\"data row9 col0\" >0.6190</td>\n",
              "      <td id=\"T_bbbe4_row9_col1\" class=\"data row9 col1\" >0.0000</td>\n",
              "      <td id=\"T_bbbe4_row9_col2\" class=\"data row9 col2\" >0.6190</td>\n",
              "      <td id=\"T_bbbe4_row9_col3\" class=\"data row9 col3\" >0.4952</td>\n",
              "      <td id=\"T_bbbe4_row9_col4\" class=\"data row9 col4\" >0.5453</td>\n",
              "      <td id=\"T_bbbe4_row9_col5\" class=\"data row9 col5\" >0.3412</td>\n",
              "      <td id=\"T_bbbe4_row9_col6\" class=\"data row9 col6\" >0.3656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bbbe4_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
              "      <td id=\"T_bbbe4_row10_col0\" class=\"data row10 col0\" >0.5948</td>\n",
              "      <td id=\"T_bbbe4_row10_col1\" class=\"data row10 col1\" >0.0000</td>\n",
              "      <td id=\"T_bbbe4_row10_col2\" class=\"data row10 col2\" >0.5948</td>\n",
              "      <td id=\"T_bbbe4_row10_col3\" class=\"data row10 col3\" >0.4702</td>\n",
              "      <td id=\"T_bbbe4_row10_col4\" class=\"data row10 col4\" >0.5197</td>\n",
              "      <td id=\"T_bbbe4_row10_col5\" class=\"data row10 col5\" >0.2917</td>\n",
              "      <td id=\"T_bbbe4_row10_col6\" class=\"data row10 col6\" >0.3165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bbbe4_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
              "      <td id=\"T_bbbe4_row11_col0\" class=\"data row11 col0\" >0.0522</td>\n",
              "      <td id=\"T_bbbe4_row11_col1\" class=\"data row11 col1\" >0.0000</td>\n",
              "      <td id=\"T_bbbe4_row11_col2\" class=\"data row11 col2\" >0.0522</td>\n",
              "      <td id=\"T_bbbe4_row11_col3\" class=\"data row11 col3\" >0.0640</td>\n",
              "      <td id=\"T_bbbe4_row11_col4\" class=\"data row11 col4\" >0.0537</td>\n",
              "      <td id=\"T_bbbe4_row11_col5\" class=\"data row11 col5\" >0.0931</td>\n",
              "      <td id=\"T_bbbe4_row11_col6\" class=\"data row11 col6\" >0.0976</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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              "colab": {
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tuned_Ridgeclf = tune_model(ridgeModel)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474,
          "referenced_widgets": [
            "6327d8b5301e4a2ca26893e347cef95c",
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            "8eb47b70b9064ed398d4dec47bfbc736",
            "b590bd3475a04d56a61949d1fc26108f",
            "173c9613087b434f9c189c8d4f4ac272",
            "67e46d6d0912476e87c0c28bc4512374",
            "e3b08533c92f45c3a8f424c65eb30479",
            "dd0e048f4d1448af918250c3123dd2e4",
            "5521f0d88646476ea1a8847797005c77",
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        "id": "QpCzpsIbvSAv",
        "outputId": "9242f0a0-13de-4af9-8179-06de841ccf0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
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        },
        {
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          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f124c503100>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_2cf43_row10_col0, #T_2cf43_row10_col1, #T_2cf43_row10_col2, #T_2cf43_row10_col3, #T_2cf43_row10_col4, #T_2cf43_row10_col5, #T_2cf43_row10_col6 {\n",
              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_2cf43\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_2cf43_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
              "      <th id=\"T_2cf43_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
              "      <th id=\"T_2cf43_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
              "      <th id=\"T_2cf43_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
              "      <th id=\"T_2cf43_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
              "      <th id=\"T_2cf43_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
              "      <th id=\"T_2cf43_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >Fold</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "      <th class=\"blank col5\" >&nbsp;</th>\n",
              "      <th class=\"blank col6\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_2cf43_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_2cf43_row0_col0\" class=\"data row0 col0\" >0.5000</td>\n",
              "      <td id=\"T_2cf43_row0_col1\" class=\"data row0 col1\" >0.0000</td>\n",
              "      <td id=\"T_2cf43_row0_col2\" class=\"data row0 col2\" >0.5000</td>\n",
              "      <td id=\"T_2cf43_row0_col3\" class=\"data row0 col3\" >0.3235</td>\n",
              "      <td id=\"T_2cf43_row0_col4\" class=\"data row0 col4\" >0.3929</td>\n",
              "      <td id=\"T_2cf43_row0_col5\" class=\"data row0 col5\" >0.1326</td>\n",
              "      <td id=\"T_2cf43_row0_col6\" class=\"data row0 col6\" >0.1514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2cf43_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_2cf43_row1_col0\" class=\"data row1 col0\" >0.5909</td>\n",
              "      <td id=\"T_2cf43_row1_col1\" class=\"data row1 col1\" >0.0000</td>\n",
              "      <td id=\"T_2cf43_row1_col2\" class=\"data row1 col2\" >0.5909</td>\n",
              "      <td id=\"T_2cf43_row1_col3\" class=\"data row1 col3\" >0.5367</td>\n",
              "      <td id=\"T_2cf43_row1_col4\" class=\"data row1 col4\" >0.5583</td>\n",
              "      <td id=\"T_2cf43_row1_col5\" class=\"data row1 col5\" >0.3613</td>\n",
              "      <td id=\"T_2cf43_row1_col6\" class=\"data row1 col6\" >0.3657</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2cf43_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_2cf43_row2_col0\" class=\"data row2 col0\" >0.5714</td>\n",
              "      <td id=\"T_2cf43_row2_col1\" class=\"data row2 col1\" >0.0000</td>\n",
              "      <td id=\"T_2cf43_row2_col2\" class=\"data row2 col2\" >0.5714</td>\n",
              "      <td id=\"T_2cf43_row2_col3\" class=\"data row2 col3\" >0.4789</td>\n",
              "      <td id=\"T_2cf43_row2_col4\" class=\"data row2 col4\" >0.5210</td>\n",
              "      <td id=\"T_2cf43_row2_col5\" class=\"data row2 col5\" >0.3102</td>\n",
              "      <td id=\"T_2cf43_row2_col6\" class=\"data row2 col6\" >0.3159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2cf43_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_2cf43_row3_col0\" class=\"data row3 col0\" >0.5238</td>\n",
              "      <td id=\"T_2cf43_row3_col1\" class=\"data row3 col1\" >0.0000</td>\n",
              "      <td id=\"T_2cf43_row3_col2\" class=\"data row3 col2\" >0.5238</td>\n",
              "      <td id=\"T_2cf43_row3_col3\" class=\"data row3 col3\" >0.4218</td>\n",
              "      <td id=\"T_2cf43_row3_col4\" class=\"data row3 col4\" >0.4667</td>\n",
              "      <td id=\"T_2cf43_row3_col5\" class=\"data row3 col5\" >0.2105</td>\n",
              "      <td id=\"T_2cf43_row3_col6\" class=\"data row3 col6\" >0.2209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2cf43_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_2cf43_row4_col0\" class=\"data row4 col0\" >0.6190</td>\n",
              "      <td id=\"T_2cf43_row4_col1\" class=\"data row4 col1\" >0.0000</td>\n",
              "      <td id=\"T_2cf43_row4_col2\" class=\"data row4 col2\" >0.6190</td>\n",
              "      <td id=\"T_2cf43_row4_col3\" class=\"data row4 col3\" >0.5629</td>\n",
              "      <td id=\"T_2cf43_row4_col4\" class=\"data row4 col4\" >0.5886</td>\n",
              "      <td id=\"T_2cf43_row4_col5\" class=\"data row4 col5\" >0.3563</td>\n",
              "      <td id=\"T_2cf43_row4_col6\" class=\"data row4 col6\" >0.3595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2cf43_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_2cf43_row5_col0\" class=\"data row5 col0\" >0.6667</td>\n",
              "      <td id=\"T_2cf43_row5_col1\" class=\"data row5 col1\" >0.0000</td>\n",
              "      <td id=\"T_2cf43_row5_col2\" class=\"data row5 col2\" >0.6667</td>\n",
              "      <td id=\"T_2cf43_row5_col3\" class=\"data row5 col3\" >0.4510</td>\n",
              "      <td id=\"T_2cf43_row5_col4\" class=\"data row5 col4\" >0.5364</td>\n",
              "      <td id=\"T_2cf43_row5_col5\" class=\"data row5 col5\" >0.3581</td>\n",
              "      <td id=\"T_2cf43_row5_col6\" class=\"data row5 col6\" >0.4263</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2cf43_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_2cf43_row6_col0\" class=\"data row6 col0\" >0.6667</td>\n",
              "      <td id=\"T_2cf43_row6_col1\" class=\"data row6 col1\" >0.0000</td>\n",
              "      <td id=\"T_2cf43_row6_col2\" class=\"data row6 col2\" >0.6667</td>\n",
              "      <td id=\"T_2cf43_row6_col3\" class=\"data row6 col3\" >0.5215</td>\n",
              "      <td id=\"T_2cf43_row6_col4\" class=\"data row6 col4\" >0.5751</td>\n",
              "      <td id=\"T_2cf43_row6_col5\" class=\"data row6 col5\" >0.4324</td>\n",
              "      <td id=\"T_2cf43_row6_col6\" class=\"data row6 col6\" >0.4709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2cf43_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_2cf43_row7_col0\" class=\"data row7 col0\" >0.6190</td>\n",
              "      <td id=\"T_2cf43_row7_col1\" class=\"data row7 col1\" >0.0000</td>\n",
              "      <td id=\"T_2cf43_row7_col2\" class=\"data row7 col2\" >0.6190</td>\n",
              "      <td id=\"T_2cf43_row7_col3\" class=\"data row7 col3\" >0.4286</td>\n",
              "      <td id=\"T_2cf43_row7_col4\" class=\"data row7 col4\" >0.5048</td>\n",
              "      <td id=\"T_2cf43_row7_col5\" class=\"data row7 col5\" >0.2258</td>\n",
              "      <td id=\"T_2cf43_row7_col6\" class=\"data row7 col6\" >0.2807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2cf43_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_2cf43_row8_col0\" class=\"data row8 col0\" >0.6190</td>\n",
              "      <td id=\"T_2cf43_row8_col1\" class=\"data row8 col1\" >0.0000</td>\n",
              "      <td id=\"T_2cf43_row8_col2\" class=\"data row8 col2\" >0.6190</td>\n",
              "      <td id=\"T_2cf43_row8_col3\" class=\"data row8 col3\" >0.5714</td>\n",
              "      <td id=\"T_2cf43_row8_col4\" class=\"data row8 col4\" >0.5333</td>\n",
              "      <td id=\"T_2cf43_row8_col5\" class=\"data row8 col5\" >0.2258</td>\n",
              "      <td id=\"T_2cf43_row8_col6\" class=\"data row8 col6\" >0.2783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2cf43_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_2cf43_row9_col0\" class=\"data row9 col0\" >0.6190</td>\n",
              "      <td id=\"T_2cf43_row9_col1\" class=\"data row9 col1\" >0.0000</td>\n",
              "      <td id=\"T_2cf43_row9_col2\" class=\"data row9 col2\" >0.6190</td>\n",
              "      <td id=\"T_2cf43_row9_col3\" class=\"data row9 col3\" >0.4952</td>\n",
              "      <td id=\"T_2cf43_row9_col4\" class=\"data row9 col4\" >0.5453</td>\n",
              "      <td id=\"T_2cf43_row9_col5\" class=\"data row9 col5\" >0.3412</td>\n",
              "      <td id=\"T_2cf43_row9_col6\" class=\"data row9 col6\" >0.3656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2cf43_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
              "      <td id=\"T_2cf43_row10_col0\" class=\"data row10 col0\" >0.5996</td>\n",
              "      <td id=\"T_2cf43_row10_col1\" class=\"data row10 col1\" >0.0000</td>\n",
              "      <td id=\"T_2cf43_row10_col2\" class=\"data row10 col2\" >0.5996</td>\n",
              "      <td id=\"T_2cf43_row10_col3\" class=\"data row10 col3\" >0.4792</td>\n",
              "      <td id=\"T_2cf43_row10_col4\" class=\"data row10 col4\" >0.5222</td>\n",
              "      <td id=\"T_2cf43_row10_col5\" class=\"data row10 col5\" >0.2954</td>\n",
              "      <td id=\"T_2cf43_row10_col6\" class=\"data row10 col6\" >0.3235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2cf43_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
              "      <td id=\"T_2cf43_row11_col0\" class=\"data row11 col0\" >0.0520</td>\n",
              "      <td id=\"T_2cf43_row11_col1\" class=\"data row11 col1\" >0.0000</td>\n",
              "      <td id=\"T_2cf43_row11_col2\" class=\"data row11 col2\" >0.0520</td>\n",
              "      <td id=\"T_2cf43_row11_col3\" class=\"data row11 col3\" >0.0719</td>\n",
              "      <td id=\"T_2cf43_row11_col4\" class=\"data row11 col4\" >0.0543</td>\n",
              "      <td id=\"T_2cf43_row11_col5\" class=\"data row11 col5\" >0.0874</td>\n",
              "      <td id=\"T_2cf43_row11_col6\" class=\"data row11 col6\" >0.0901</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/7 [00:00<?, ?it/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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    {
      "cell_type": "code",
      "source": [
        "predict_model(tuned_Ridgeclf)"
      ],
      "metadata": {
        "id": "bDS6L5-7vysJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Hungarian/"
      ],
      "metadata": {
        "id": "mD4vP4yBZ2jP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hungarian_basic = hungarian_data.copy()\n",
        "# convert unknowns (-9.0) to NaN\n",
        "hungarian_basic.replace(-9.0, np.NaN, inplace=True)"
      ],
      "metadata": {
        "id": "H8AF-kOUZ44i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# make copy for binary labeling\n",
        "hungarian_basic['num'][hungarian_basic['num'] > 0] = 1\n",
        "hungarian_basic.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "JJ3sRjx1Z_bI",
        "outputId": "db1c7523-6064-460b-d7f8-5e0fcad7fed9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "0  40.0  1.0  2.0     140.0  289.0  0.0      0.0    172.0    0.0      0.0   \n",
              "1  49.0  0.0  3.0     160.0  180.0  0.0      0.0    156.0    0.0      1.0   \n",
              "2  37.0  1.0  2.0     130.0  283.0  0.0      1.0     98.0    0.0      0.0   \n",
              "3  48.0  0.0  4.0     138.0  214.0  0.0      0.0    108.0    1.0      1.5   \n",
              "4  54.0  1.0  3.0     150.0    NaN  0.0      0.0    122.0    0.0      0.0   \n",
              "\n",
              "   slope  ca  thal  num  \n",
              "0    NaN NaN   NaN  0.0  \n",
              "1    2.0 NaN   NaN  1.0  \n",
              "2    NaN NaN   NaN  0.0  \n",
              "3    2.0 NaN   NaN  1.0  \n",
              "4    NaN NaN   NaN  0.0  "
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              "      <th>3</th>\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# try dropping slope, ca, and thal\n",
        "hungarian_basic.drop(['slope', 'ca', 'thal', ], axis=1, inplace=True) # did not fix error"
      ],
      "metadata": {
        "id": "g7yx-OPOaBgn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# split the binary data\n",
        "from sklearn.model_selection import train_test_split\n",
        "hungarian_basic_train, hungarian_basic_test = train_test_split(hungarian_basic, test_size=0.2, random_state=42)\n",
        "hungarian_basic_test_labels = hungarian_basic_test[heart_label]\n",
        "hungarian_basic_test_nolabel = hungarian_basic_test.drop([heart_label], axis=1)\n",
        "hungarian_basic_train_labels = hungarian_basic_train[heart_label]\n",
        "hungarian_basic_train_nolabel = hungarian_basic_train.drop([heart_label], axis=1)\n",
        "hungarian_basic_train.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "WL6msMoGaDPU",
        "outputId": "105f713e-da20-49fa-8ba5-56c9c00473ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "105  57.0  1.0  2.0     140.0  260.0  1.0      0.0    140.0    0.0      0.0   \n",
              "274  55.0  1.0  3.0     120.0  220.0  0.0      2.0    134.0    0.0      0.0   \n",
              "173  53.0  1.0  2.0     140.0  320.0  0.0      0.0    162.0    0.0      0.0   \n",
              "198  42.0  1.0  2.0     150.0  268.0  0.0      0.0    136.0    0.0      0.0   \n",
              "250  49.0  1.0  4.0     130.0  341.0  0.0      0.0    120.0    1.0      1.0   \n",
              "\n",
              "     num  \n",
              "105  0.0  \n",
              "274  0.0  \n",
              "173  0.0  \n",
              "198  0.0  \n",
              "250  1.0  "
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              "      <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>num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>57.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>260.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>274</th>\n",
              "      <td>55.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>134.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>53.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>320.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>162.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>42.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>268.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>136.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>250</th>\n",
              "      <td>49.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>341.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-97c70822-342a-4209-9a16-9de8a754a94b');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\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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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "more pre processing..."
      ],
      "metadata": {
        "id": "0b7UBsDeajmC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hungarian = hungarian_data.copy()\n",
        "\n",
        "# convert unknowns (-9.0) to NaN\n",
        "hungarian.replace(-9.0, np.NaN, inplace=True)\n",
        "\n",
        "hungarian.drop(['slope', 'ca', 'thal'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "-_TJKx2baakZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hungarian.drop(294,axis=0, inplace=True) # may not be necessary later # something may have gone wrong earlier, requiring it now"
      ],
      "metadata": {
        "id": "Wbsm6AiFaxKF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "# impute discrete values using mode ()\n",
        "imputeMode = SimpleImputer(strategy=\"most_frequent\") # create mode imputer\n",
        "hungarian_disc = pd.concat([hungarian.pop(x) for x in ['fbs', 'restecg', 'exang']], axis=1) # isolate discrete\n",
        "imputeMode.fit(hungarian_disc) # fit - learns the data\n",
        "imputed_disc = imputeMode.transform(hungarian_disc) # transform - imputes with chosen strategy\n",
        "hungarian_disc_imp = pd.DataFrame(imputed_disc, columns=hungarian_disc.columns, index=hungarian.index) # back to pandas DataFrame\n",
        "hungarian_disc_imp.info() # check for missing values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JSTVWurIax97",
        "outputId": "647a03b7-5ab4-4ade-c717-c51320da59da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 294 entries, 0 to 293\n",
            "Data columns (total 3 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   fbs      294 non-null    float64\n",
            " 1   restecg  294 non-null    float64\n",
            " 2   exang    294 non-null    float64\n",
            "dtypes: float64(3)\n",
            "memory usage: 7.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# NEED TO COMBINE cat, cont, and remaining\n",
        "from sklearn.impute import SimpleImputer\n",
        "# impute continuous values using mean\n",
        "imputeMean = SimpleImputer(strategy=\"mean\") # create mode imputer\n",
        "hungarian_cont = pd.concat([hungarian.pop(x) for x in ['trestbps', 'chol', 'thalach']], axis=1) # isolate continuous\n",
        "imputeMean.fit(hungarian_cont) # fit - learns the data\n",
        "imputed_cont = imputeMean.transform(hungarian_cont) # transform - imputes with chosen strategy\n",
        "hungarian_cont_imp = pd.DataFrame(imputed_cont, columns=hungarian_cont.columns, index=hungarian.index) # back to pandas DataFrame\n",
        "hungarian_cont_imp.info() # check for missing values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GuUPeNxma8DE",
        "outputId": "d9aa1dca-19c4-4150-8b4a-987fdc7e3440"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 294 entries, 0 to 293\n",
            "Data columns (total 3 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   trestbps  294 non-null    float64\n",
            " 1   chol      294 non-null    float64\n",
            " 2   thalach   294 non-null    float64\n",
            "dtypes: float64(3)\n",
            "memory usage: 7.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hungarian_imp = pd.concat((hungarian_cont_imp, hungarian_disc_imp, hungarian), axis=1)"
      ],
      "metadata": {
        "id": "N4rpkH64bI3_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hungarian_imp['num'][hungarian_imp['num'] > 0] = 1\n"
      ],
      "metadata": {
        "id": "E3YMEemtbBng"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hungarian_imp_strat = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
        "for train_index, test_index in hungarian_imp_strat.split(hungarian_imp, hungarian_imp[\"num\"]):\n",
        "  hungarian_imp_strat_train = hungarian_imp.loc[train_index]\n",
        "  hungarian_imp_strat_test = hungarian_imp.loc[test_index]"
      ],
      "metadata": {
        "id": "elLkAx1vbDvl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# split stratified data\n",
        "hungarian_imp_strat_train_X = hungarian_imp_strat_train.drop([heart_label], axis=1)\n",
        "hungarian_imp_strat_train_y = hungarian_imp_strat_train[heart_label]\n",
        "hungarian_imp_strat_test_X = hungarian_imp_strat_test.drop([heart_label], axis=1)\n",
        "hungarian_imp_strat_test_y = hungarian_imp_strat_test[heart_label]\n",
        "hungarian_imp_strat_train_X.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "n7-0B3R9bJjO",
        "outputId": "39b7ac09-9918-4bb9-dc00-48fe5f78bae0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     trestbps        chol  thalach  fbs  restecg  exang   age  sex   cp  \\\n",
              "174     140.0  187.000000    172.0  0.0      0.0    0.0  49.0  1.0  3.0   \n",
              "251     135.0  491.000000    135.0  0.0      0.0    0.0  44.0  1.0  4.0   \n",
              "265     140.0  193.000000    145.0  0.0      0.0    1.0  47.0  1.0  3.0   \n",
              "38      120.0  250.848708    148.0  1.0      1.0    0.0  48.0  0.0  2.0   \n",
              "39      150.0  227.000000    130.0  0.0      0.0    1.0  48.0  0.0  4.0   \n",
              "\n",
              "     oldpeak  \n",
              "174      0.0  \n",
              "251      0.0  \n",
              "265      1.0  \n",
              "38       0.0  \n",
              "39       1.0  "
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>trestbps</th>\n",
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              "      <th>restecg</th>\n",
              "      <th>exang</th>\n",
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              "      <th>39</th>\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1S34ctJzcNt4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.classification import *\n",
        "hungarian_pc_setup = setup(data = hungarian_imp_strat_train, test_data = hungarian_imp_strat_test, target = 'num', session_id=42)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "fDog1BrlbVVu",
        "outputId": "aba7bd16-80b0-46a3-ed50-b54199a283b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f9d93425760>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_33c4e_row8_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_33c4e\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_33c4e_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_33c4e_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_33c4e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_33c4e_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_33c4e_row0_col1\" class=\"data row0 col1\" >42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33c4e_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_33c4e_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
              "      <td id=\"T_33c4e_row1_col1\" class=\"data row1 col1\" >num</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33c4e_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_33c4e_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
              "      <td id=\"T_33c4e_row2_col1\" class=\"data row2 col1\" >Binary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33c4e_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_33c4e_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
              "      <td id=\"T_33c4e_row3_col1\" class=\"data row3 col1\" >(294, 11)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33c4e_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_33c4e_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_33c4e_row4_col1\" class=\"data row4 col1\" >(294, 11)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33c4e_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_33c4e_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
              "      <td id=\"T_33c4e_row5_col1\" class=\"data row5 col1\" >(235, 11)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33c4e_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_33c4e_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
              "      <td id=\"T_33c4e_row6_col1\" class=\"data row6 col1\" >(59, 11)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33c4e_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_33c4e_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
              "      <td id=\"T_33c4e_row7_col1\" class=\"data row7 col1\" >10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33c4e_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_33c4e_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
              "      <td id=\"T_33c4e_row8_col1\" class=\"data row8 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33c4e_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_33c4e_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
              "      <td id=\"T_33c4e_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33c4e_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_33c4e_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_33c4e_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33c4e_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_33c4e_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_33c4e_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33c4e_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_33c4e_row12_col0\" class=\"data row12 col0\" >Fold Generator</td>\n",
              "      <td id=\"T_33c4e_row12_col1\" class=\"data row12 col1\" >StratifiedKFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33c4e_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_33c4e_row13_col0\" class=\"data row13 col0\" >Fold Number</td>\n",
              "      <td id=\"T_33c4e_row13_col1\" class=\"data row13 col1\" >10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33c4e_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_33c4e_row14_col0\" class=\"data row14 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_33c4e_row14_col1\" class=\"data row14 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33c4e_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_33c4e_row15_col0\" class=\"data row15 col0\" >Use GPU</td>\n",
              "      <td id=\"T_33c4e_row15_col1\" class=\"data row15 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33c4e_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_33c4e_row16_col0\" class=\"data row16 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_33c4e_row16_col1\" class=\"data row16 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33c4e_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_33c4e_row17_col0\" class=\"data row17 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_33c4e_row17_col1\" class=\"data row17 col1\" >clf-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33c4e_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "      <td id=\"T_33c4e_row18_col0\" class=\"data row18 col0\" >USI</td>\n",
              "      <td id=\"T_33c4e_row18_col1\" class=\"data row18 col1\" >3e0b</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "compare_models()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 612,
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        "outputId": "347f15b4-9505-433b-93e6-573723c51f83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": []
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              "#T_0fd52_row0_col1, #T_0fd52_row0_col5, #T_0fd52_row0_col6, #T_0fd52_row1_col4, #T_0fd52_row1_col7, #T_0fd52_row2_col2, #T_0fd52_row3_col3 {\n",
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              "#T_0fd52_row0_col8, #T_0fd52_row1_col8, #T_0fd52_row2_col8, #T_0fd52_row3_col8, #T_0fd52_row4_col8, #T_0fd52_row5_col8, #T_0fd52_row6_col8, #T_0fd52_row7_col8, #T_0fd52_row8_col8, #T_0fd52_row9_col8, #T_0fd52_row10_col8, #T_0fd52_row11_col8, #T_0fd52_row12_col8, #T_0fd52_row14_col8 {\n",
              "  text-align: left;\n",
              "  background-color: lightgrey;\n",
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              "#T_0fd52_row13_col8 {\n",
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              "</style>\n",
              "<table id=\"T_0fd52\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_0fd52_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_0fd52_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th id=\"T_0fd52_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
              "      <th id=\"T_0fd52_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
              "      <th id=\"T_0fd52_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
              "      <th id=\"T_0fd52_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
              "      <th id=\"T_0fd52_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th id=\"T_0fd52_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
              "      <th id=\"T_0fd52_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_0fd52_level0_row0\" class=\"row_heading level0 row0\" >ridge</th>\n",
              "      <td id=\"T_0fd52_row0_col0\" class=\"data row0 col0\" >Ridge Classifier</td>\n",
              "      <td id=\"T_0fd52_row0_col1\" class=\"data row0 col1\" >0.8304</td>\n",
              "      <td id=\"T_0fd52_row0_col2\" class=\"data row0 col2\" >0.0000</td>\n",
              "      <td id=\"T_0fd52_row0_col3\" class=\"data row0 col3\" >0.7208</td>\n",
              "      <td id=\"T_0fd52_row0_col4\" class=\"data row0 col4\" >0.8012</td>\n",
              "      <td id=\"T_0fd52_row0_col5\" class=\"data row0 col5\" >0.7516</td>\n",
              "      <td id=\"T_0fd52_row0_col6\" class=\"data row0 col6\" >0.6246</td>\n",
              "      <td id=\"T_0fd52_row0_col7\" class=\"data row0 col7\" >0.6326</td>\n",
              "      <td id=\"T_0fd52_row0_col8\" class=\"data row0 col8\" >0.0440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0fd52_level0_row1\" class=\"row_heading level0 row1\" >lr</th>\n",
              "      <td id=\"T_0fd52_row1_col0\" class=\"data row1 col0\" >Logistic Regression</td>\n",
              "      <td id=\"T_0fd52_row1_col1\" class=\"data row1 col1\" >0.8303</td>\n",
              "      <td id=\"T_0fd52_row1_col2\" class=\"data row1 col2\" >0.8929</td>\n",
              "      <td id=\"T_0fd52_row1_col3\" class=\"data row1 col3\" >0.7208</td>\n",
              "      <td id=\"T_0fd52_row1_col4\" class=\"data row1 col4\" >0.8057</td>\n",
              "      <td id=\"T_0fd52_row1_col5\" class=\"data row1 col5\" >0.7506</td>\n",
              "      <td id=\"T_0fd52_row1_col6\" class=\"data row1 col6\" >0.6240</td>\n",
              "      <td id=\"T_0fd52_row1_col7\" class=\"data row1 col7\" >0.6355</td>\n",
              "      <td id=\"T_0fd52_row1_col8\" class=\"data row1 col8\" >0.7170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0fd52_level0_row2\" class=\"row_heading level0 row2\" >lda</th>\n",
              "      <td id=\"T_0fd52_row2_col0\" class=\"data row2 col0\" >Linear Discriminant Analysis</td>\n",
              "      <td id=\"T_0fd52_row2_col1\" class=\"data row2 col1\" >0.8263</td>\n",
              "      <td id=\"T_0fd52_row2_col2\" class=\"data row2 col2\" >0.9016</td>\n",
              "      <td id=\"T_0fd52_row2_col3\" class=\"data row2 col3\" >0.7208</td>\n",
              "      <td id=\"T_0fd52_row2_col4\" class=\"data row2 col4\" >0.7914</td>\n",
              "      <td id=\"T_0fd52_row2_col5\" class=\"data row2 col5\" >0.7470</td>\n",
              "      <td id=\"T_0fd52_row2_col6\" class=\"data row2 col6\" >0.6163</td>\n",
              "      <td id=\"T_0fd52_row2_col7\" class=\"data row2 col7\" >0.6240</td>\n",
              "      <td id=\"T_0fd52_row2_col8\" class=\"data row2 col8\" >0.0580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0fd52_level0_row3\" class=\"row_heading level0 row3\" >nb</th>\n",
              "      <td id=\"T_0fd52_row3_col0\" class=\"data row3 col0\" >Naive Bayes</td>\n",
              "      <td id=\"T_0fd52_row3_col1\" class=\"data row3 col1\" >0.8085</td>\n",
              "      <td id=\"T_0fd52_row3_col2\" class=\"data row3 col2\" >0.8986</td>\n",
              "      <td id=\"T_0fd52_row3_col3\" class=\"data row3 col3\" >0.7681</td>\n",
              "      <td id=\"T_0fd52_row3_col4\" class=\"data row3 col4\" >0.7277</td>\n",
              "      <td id=\"T_0fd52_row3_col5\" class=\"data row3 col5\" >0.7392</td>\n",
              "      <td id=\"T_0fd52_row3_col6\" class=\"data row3 col6\" >0.5899</td>\n",
              "      <td id=\"T_0fd52_row3_col7\" class=\"data row3 col7\" >0.5983</td>\n",
              "      <td id=\"T_0fd52_row3_col8\" class=\"data row3 col8\" >0.0790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0fd52_level0_row4\" class=\"row_heading level0 row4\" >et</th>\n",
              "      <td id=\"T_0fd52_row4_col0\" class=\"data row4 col0\" >Extra Trees Classifier</td>\n",
              "      <td id=\"T_0fd52_row4_col1\" class=\"data row4 col1\" >0.8043</td>\n",
              "      <td id=\"T_0fd52_row4_col2\" class=\"data row4 col2\" >0.8925</td>\n",
              "      <td id=\"T_0fd52_row4_col3\" class=\"data row4 col3\" >0.6611</td>\n",
              "      <td id=\"T_0fd52_row4_col4\" class=\"data row4 col4\" >0.7841</td>\n",
              "      <td id=\"T_0fd52_row4_col5\" class=\"data row4 col5\" >0.7001</td>\n",
              "      <td id=\"T_0fd52_row4_col6\" class=\"data row4 col6\" >0.5601</td>\n",
              "      <td id=\"T_0fd52_row4_col7\" class=\"data row4 col7\" >0.5755</td>\n",
              "      <td id=\"T_0fd52_row4_col8\" class=\"data row4 col8\" >0.4380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0fd52_level0_row5\" class=\"row_heading level0 row5\" >qda</th>\n",
              "      <td id=\"T_0fd52_row5_col0\" class=\"data row5 col0\" >Quadratic Discriminant Analysis</td>\n",
              "      <td id=\"T_0fd52_row5_col1\" class=\"data row5 col1\" >0.8000</td>\n",
              "      <td id=\"T_0fd52_row5_col2\" class=\"data row5 col2\" >0.8742</td>\n",
              "      <td id=\"T_0fd52_row5_col3\" class=\"data row5 col3\" >0.7431</td>\n",
              "      <td id=\"T_0fd52_row5_col4\" class=\"data row5 col4\" >0.7368</td>\n",
              "      <td id=\"T_0fd52_row5_col5\" class=\"data row5 col5\" >0.7256</td>\n",
              "      <td id=\"T_0fd52_row5_col6\" class=\"data row5 col6\" >0.5708</td>\n",
              "      <td id=\"T_0fd52_row5_col7\" class=\"data row5 col7\" >0.5848</td>\n",
              "      <td id=\"T_0fd52_row5_col8\" class=\"data row5 col8\" >0.0570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0fd52_level0_row6\" class=\"row_heading level0 row6\" >gbc</th>\n",
              "      <td id=\"T_0fd52_row6_col0\" class=\"data row6 col0\" >Gradient Boosting Classifier</td>\n",
              "      <td id=\"T_0fd52_row6_col1\" class=\"data row6 col1\" >0.7877</td>\n",
              "      <td id=\"T_0fd52_row6_col2\" class=\"data row6 col2\" >0.8875</td>\n",
              "      <td id=\"T_0fd52_row6_col3\" class=\"data row6 col3\" >0.6639</td>\n",
              "      <td id=\"T_0fd52_row6_col4\" class=\"data row6 col4\" >0.7394</td>\n",
              "      <td id=\"T_0fd52_row6_col5\" class=\"data row6 col5\" >0.6776</td>\n",
              "      <td id=\"T_0fd52_row6_col6\" class=\"data row6 col6\" >0.5258</td>\n",
              "      <td id=\"T_0fd52_row6_col7\" class=\"data row6 col7\" >0.5414</td>\n",
              "      <td id=\"T_0fd52_row6_col8\" class=\"data row6 col8\" >0.2570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0fd52_level0_row7\" class=\"row_heading level0 row7\" >xgboost</th>\n",
              "      <td id=\"T_0fd52_row7_col0\" class=\"data row7 col0\" >Extreme Gradient Boosting</td>\n",
              "      <td id=\"T_0fd52_row7_col1\" class=\"data row7 col1\" >0.7786</td>\n",
              "      <td id=\"T_0fd52_row7_col2\" class=\"data row7 col2\" >0.8656</td>\n",
              "      <td id=\"T_0fd52_row7_col3\" class=\"data row7 col3\" >0.6597</td>\n",
              "      <td id=\"T_0fd52_row7_col4\" class=\"data row7 col4\" >0.7328</td>\n",
              "      <td id=\"T_0fd52_row7_col5\" class=\"data row7 col5\" >0.6786</td>\n",
              "      <td id=\"T_0fd52_row7_col6\" class=\"data row7 col6\" >0.5133</td>\n",
              "      <td id=\"T_0fd52_row7_col7\" class=\"data row7 col7\" >0.5271</td>\n",
              "      <td id=\"T_0fd52_row7_col8\" class=\"data row7 col8\" >0.2100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0fd52_level0_row8\" class=\"row_heading level0 row8\" >lightgbm</th>\n",
              "      <td id=\"T_0fd52_row8_col0\" class=\"data row8 col0\" >Light Gradient Boosting Machine</td>\n",
              "      <td id=\"T_0fd52_row8_col1\" class=\"data row8 col1\" >0.7746</td>\n",
              "      <td id=\"T_0fd52_row8_col2\" class=\"data row8 col2\" >0.8636</td>\n",
              "      <td id=\"T_0fd52_row8_col3\" class=\"data row8 col3\" >0.6736</td>\n",
              "      <td id=\"T_0fd52_row8_col4\" class=\"data row8 col4\" >0.7089</td>\n",
              "      <td id=\"T_0fd52_row8_col5\" class=\"data row8 col5\" >0.6684</td>\n",
              "      <td id=\"T_0fd52_row8_col6\" class=\"data row8 col6\" >0.5030</td>\n",
              "      <td id=\"T_0fd52_row8_col7\" class=\"data row8 col7\" >0.5206</td>\n",
              "      <td id=\"T_0fd52_row8_col8\" class=\"data row8 col8\" >0.2180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0fd52_level0_row9\" class=\"row_heading level0 row9\" >ada</th>\n",
              "      <td id=\"T_0fd52_row9_col0\" class=\"data row9 col0\" >Ada Boost Classifier</td>\n",
              "      <td id=\"T_0fd52_row9_col1\" class=\"data row9 col1\" >0.7745</td>\n",
              "      <td id=\"T_0fd52_row9_col2\" class=\"data row9 col2\" >0.7775</td>\n",
              "      <td id=\"T_0fd52_row9_col3\" class=\"data row9 col3\" >0.6028</td>\n",
              "      <td id=\"T_0fd52_row9_col4\" class=\"data row9 col4\" >0.7354</td>\n",
              "      <td id=\"T_0fd52_row9_col5\" class=\"data row9 col5\" >0.6525</td>\n",
              "      <td id=\"T_0fd52_row9_col6\" class=\"data row9 col6\" >0.4907</td>\n",
              "      <td id=\"T_0fd52_row9_col7\" class=\"data row9 col7\" >0.5034</td>\n",
              "      <td id=\"T_0fd52_row9_col8\" class=\"data row9 col8\" >0.2420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0fd52_level0_row10\" class=\"row_heading level0 row10\" >rf</th>\n",
              "      <td id=\"T_0fd52_row10_col0\" class=\"data row10 col0\" >Random Forest Classifier</td>\n",
              "      <td id=\"T_0fd52_row10_col1\" class=\"data row10 col1\" >0.7705</td>\n",
              "      <td id=\"T_0fd52_row10_col2\" class=\"data row10 col2\" >0.8815</td>\n",
              "      <td id=\"T_0fd52_row10_col3\" class=\"data row10 col3\" >0.6139</td>\n",
              "      <td id=\"T_0fd52_row10_col4\" class=\"data row10 col4\" >0.7180</td>\n",
              "      <td id=\"T_0fd52_row10_col5\" class=\"data row10 col5\" >0.6543</td>\n",
              "      <td id=\"T_0fd52_row10_col6\" class=\"data row10 col6\" >0.4865</td>\n",
              "      <td id=\"T_0fd52_row10_col7\" class=\"data row10 col7\" >0.4949</td>\n",
              "      <td id=\"T_0fd52_row10_col8\" class=\"data row10 col8\" >0.6220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0fd52_level0_row11\" class=\"row_heading level0 row11\" >dt</th>\n",
              "      <td id=\"T_0fd52_row11_col0\" class=\"data row11 col0\" >Decision Tree Classifier</td>\n",
              "      <td id=\"T_0fd52_row11_col1\" class=\"data row11 col1\" >0.7308</td>\n",
              "      <td id=\"T_0fd52_row11_col2\" class=\"data row11 col2\" >0.7172</td>\n",
              "      <td id=\"T_0fd52_row11_col3\" class=\"data row11 col3\" >0.6611</td>\n",
              "      <td id=\"T_0fd52_row11_col4\" class=\"data row11 col4\" >0.6579</td>\n",
              "      <td id=\"T_0fd52_row11_col5\" class=\"data row11 col5\" >0.6411</td>\n",
              "      <td id=\"T_0fd52_row11_col6\" class=\"data row11 col6\" >0.4312</td>\n",
              "      <td id=\"T_0fd52_row11_col7\" class=\"data row11 col7\" >0.4467</td>\n",
              "      <td id=\"T_0fd52_row11_col8\" class=\"data row11 col8\" >0.0490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0fd52_level0_row12\" class=\"row_heading level0 row12\" >knn</th>\n",
              "      <td id=\"T_0fd52_row12_col0\" class=\"data row12 col0\" >K Neighbors Classifier</td>\n",
              "      <td id=\"T_0fd52_row12_col1\" class=\"data row12 col1\" >0.6685</td>\n",
              "      <td id=\"T_0fd52_row12_col2\" class=\"data row12 col2\" >0.6473</td>\n",
              "      <td id=\"T_0fd52_row12_col3\" class=\"data row12 col3\" >0.4028</td>\n",
              "      <td id=\"T_0fd52_row12_col4\" class=\"data row12 col4\" >0.6188</td>\n",
              "      <td id=\"T_0fd52_row12_col5\" class=\"data row12 col5\" >0.4569</td>\n",
              "      <td id=\"T_0fd52_row12_col6\" class=\"data row12 col6\" >0.2376</td>\n",
              "      <td id=\"T_0fd52_row12_col7\" class=\"data row12 col7\" >0.2614</td>\n",
              "      <td id=\"T_0fd52_row12_col8\" class=\"data row12 col8\" >0.1200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0fd52_level0_row13\" class=\"row_heading level0 row13\" >svm</th>\n",
              "      <td id=\"T_0fd52_row13_col0\" class=\"data row13 col0\" >SVM - Linear Kernel</td>\n",
              "      <td id=\"T_0fd52_row13_col1\" class=\"data row13 col1\" >0.6462</td>\n",
              "      <td id=\"T_0fd52_row13_col2\" class=\"data row13 col2\" >0.0000</td>\n",
              "      <td id=\"T_0fd52_row13_col3\" class=\"data row13 col3\" >0.4014</td>\n",
              "      <td id=\"T_0fd52_row13_col4\" class=\"data row13 col4\" >0.4552</td>\n",
              "      <td id=\"T_0fd52_row13_col5\" class=\"data row13 col5\" >0.3692</td>\n",
              "      <td id=\"T_0fd52_row13_col6\" class=\"data row13 col6\" >0.1944</td>\n",
              "      <td id=\"T_0fd52_row13_col7\" class=\"data row13 col7\" >0.2319</td>\n",
              "      <td id=\"T_0fd52_row13_col8\" class=\"data row13 col8\" >0.0430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0fd52_level0_row14\" class=\"row_heading level0 row14\" >dummy</th>\n",
              "      <td id=\"T_0fd52_row14_col0\" class=\"data row14 col0\" >Dummy Classifier</td>\n",
              "      <td id=\"T_0fd52_row14_col1\" class=\"data row14 col1\" >0.6386</td>\n",
              "      <td id=\"T_0fd52_row14_col2\" class=\"data row14 col2\" >0.5000</td>\n",
              "      <td id=\"T_0fd52_row14_col3\" class=\"data row14 col3\" >0.0000</td>\n",
              "      <td id=\"T_0fd52_row14_col4\" class=\"data row14 col4\" >0.0000</td>\n",
              "      <td id=\"T_0fd52_row14_col5\" class=\"data row14 col5\" >0.0000</td>\n",
              "      <td id=\"T_0fd52_row14_col6\" class=\"data row14 col6\" >0.0000</td>\n",
              "      <td id=\"T_0fd52_row14_col7\" class=\"data row14 col7\" >0.0000</td>\n",
              "      <td id=\"T_0fd52_row14_col8\" class=\"data row14 col8\" >0.0560</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
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        {
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              "Processing:   0%|          | 0/65 [00:00<?, ?it/s]"
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              "RidgeClassifier(alpha=1.0, class_weight=None, copy_X=True, fit_intercept=True,\n",
              "                max_iter=None, positive=False, random_state=42, solver='auto',\n",
              "                tol=0.0001)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: /~https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RidgeClassifier(alpha=1.0, class_weight=None, copy_X=True, fit_intercept=True,\n",
              "                max_iter=None, positive=False, random_state=42, solver=&#x27;auto&#x27;,\n",
              "                tol=0.0001)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RidgeClassifier</label><div class=\"sk-toggleable__content\"><pre>RidgeClassifier(alpha=1.0, class_weight=None, copy_X=True, fit_intercept=True,\n",
              "                max_iter=None, positive=False, random_state=42, solver=&#x27;auto&#x27;,\n",
              "                tol=0.0001)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ridge = create_model('ridge')"
      ],
      "metadata": {
        "colab": {
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          "height": 457,
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        "id": "0AHCMceeca5B",
        "outputId": "bbf7317d-8331-42d3-97a0-30ec1e11f83a"
      },
      "execution_count": null,
      "outputs": [
        {
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              "<pandas.io.formats.style.Styler at 0x7f9da44a09a0>"
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              "<style type=\"text/css\">\n",
              "#T_d12e3_row10_col0, #T_d12e3_row10_col1, #T_d12e3_row10_col2, #T_d12e3_row10_col3, #T_d12e3_row10_col4, #T_d12e3_row10_col5, #T_d12e3_row10_col6 {\n",
              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_d12e3\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_d12e3_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
              "      <th id=\"T_d12e3_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
              "      <th id=\"T_d12e3_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
              "      <th id=\"T_d12e3_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
              "      <th id=\"T_d12e3_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
              "      <th id=\"T_d12e3_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
              "      <th id=\"T_d12e3_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >Fold</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "      <th class=\"blank col5\" >&nbsp;</th>\n",
              "      <th class=\"blank col6\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_d12e3_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_d12e3_row0_col0\" class=\"data row0 col0\" >0.8333</td>\n",
              "      <td id=\"T_d12e3_row0_col1\" class=\"data row0 col1\" >0.0000</td>\n",
              "      <td id=\"T_d12e3_row0_col2\" class=\"data row0 col2\" >0.6667</td>\n",
              "      <td id=\"T_d12e3_row0_col3\" class=\"data row0 col3\" >0.8571</td>\n",
              "      <td id=\"T_d12e3_row0_col4\" class=\"data row0 col4\" >0.7500</td>\n",
              "      <td id=\"T_d12e3_row0_col5\" class=\"data row0 col5\" >0.6279</td>\n",
              "      <td id=\"T_d12e3_row0_col6\" class=\"data row0 col6\" >0.6391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d12e3_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_d12e3_row1_col0\" class=\"data row1 col0\" >0.8750</td>\n",
              "      <td id=\"T_d12e3_row1_col1\" class=\"data row1 col1\" >0.0000</td>\n",
              "      <td id=\"T_d12e3_row1_col2\" class=\"data row1 col2\" >0.7778</td>\n",
              "      <td id=\"T_d12e3_row1_col3\" class=\"data row1 col3\" >0.8750</td>\n",
              "      <td id=\"T_d12e3_row1_col4\" class=\"data row1 col4\" >0.8235</td>\n",
              "      <td id=\"T_d12e3_row1_col5\" class=\"data row1 col5\" >0.7273</td>\n",
              "      <td id=\"T_d12e3_row1_col6\" class=\"data row1 col6\" >0.7303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d12e3_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_d12e3_row2_col0\" class=\"data row2 col0\" >0.7917</td>\n",
              "      <td id=\"T_d12e3_row2_col1\" class=\"data row2 col1\" >0.0000</td>\n",
              "      <td id=\"T_d12e3_row2_col2\" class=\"data row2 col2\" >0.6667</td>\n",
              "      <td id=\"T_d12e3_row2_col3\" class=\"data row2 col3\" >0.7500</td>\n",
              "      <td id=\"T_d12e3_row2_col4\" class=\"data row2 col4\" >0.7059</td>\n",
              "      <td id=\"T_d12e3_row2_col5\" class=\"data row2 col5\" >0.5455</td>\n",
              "      <td id=\"T_d12e3_row2_col6\" class=\"data row2 col6\" >0.5477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d12e3_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_d12e3_row3_col0\" class=\"data row3 col0\" >0.7083</td>\n",
              "      <td id=\"T_d12e3_row3_col1\" class=\"data row3 col1\" >0.0000</td>\n",
              "      <td id=\"T_d12e3_row3_col2\" class=\"data row3 col2\" >0.4444</td>\n",
              "      <td id=\"T_d12e3_row3_col3\" class=\"data row3 col3\" >0.6667</td>\n",
              "      <td id=\"T_d12e3_row3_col4\" class=\"data row3 col4\" >0.5333</td>\n",
              "      <td id=\"T_d12e3_row3_col5\" class=\"data row3 col5\" >0.3333</td>\n",
              "      <td id=\"T_d12e3_row3_col6\" class=\"data row3 col6\" >0.3478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d12e3_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_d12e3_row4_col0\" class=\"data row4 col0\" >0.7917</td>\n",
              "      <td id=\"T_d12e3_row4_col1\" class=\"data row4 col1\" >0.0000</td>\n",
              "      <td id=\"T_d12e3_row4_col2\" class=\"data row4 col2\" >0.7778</td>\n",
              "      <td id=\"T_d12e3_row4_col3\" class=\"data row4 col3\" >0.7000</td>\n",
              "      <td id=\"T_d12e3_row4_col4\" class=\"data row4 col4\" >0.7368</td>\n",
              "      <td id=\"T_d12e3_row4_col5\" class=\"data row4 col5\" >0.5652</td>\n",
              "      <td id=\"T_d12e3_row4_col6\" class=\"data row4 col6\" >0.5674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d12e3_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_d12e3_row5_col0\" class=\"data row5 col0\" >0.8696</td>\n",
              "      <td id=\"T_d12e3_row5_col1\" class=\"data row5 col1\" >0.0000</td>\n",
              "      <td id=\"T_d12e3_row5_col2\" class=\"data row5 col2\" >0.6250</td>\n",
              "      <td id=\"T_d12e3_row5_col3\" class=\"data row5 col3\" >1.0000</td>\n",
              "      <td id=\"T_d12e3_row5_col4\" class=\"data row5 col4\" >0.7692</td>\n",
              "      <td id=\"T_d12e3_row5_col5\" class=\"data row5 col5\" >0.6849</td>\n",
              "      <td id=\"T_d12e3_row5_col6\" class=\"data row5 col6\" >0.7217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d12e3_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_d12e3_row6_col0\" class=\"data row6 col0\" >0.8696</td>\n",
              "      <td id=\"T_d12e3_row6_col1\" class=\"data row6 col1\" >0.0000</td>\n",
              "      <td id=\"T_d12e3_row6_col2\" class=\"data row6 col2\" >0.8750</td>\n",
              "      <td id=\"T_d12e3_row6_col3\" class=\"data row6 col3\" >0.7778</td>\n",
              "      <td id=\"T_d12e3_row6_col4\" class=\"data row6 col4\" >0.8235</td>\n",
              "      <td id=\"T_d12e3_row6_col5\" class=\"data row6 col5\" >0.7206</td>\n",
              "      <td id=\"T_d12e3_row6_col6\" class=\"data row6 col6\" >0.7238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d12e3_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_d12e3_row7_col0\" class=\"data row7 col0\" >0.8696</td>\n",
              "      <td id=\"T_d12e3_row7_col1\" class=\"data row7 col1\" >0.0000</td>\n",
              "      <td id=\"T_d12e3_row7_col2\" class=\"data row7 col2\" >0.7500</td>\n",
              "      <td id=\"T_d12e3_row7_col3\" class=\"data row7 col3\" >0.8571</td>\n",
              "      <td id=\"T_d12e3_row7_col4\" class=\"data row7 col4\" >0.8000</td>\n",
              "      <td id=\"T_d12e3_row7_col5\" class=\"data row7 col5\" >0.7039</td>\n",
              "      <td id=\"T_d12e3_row7_col6\" class=\"data row7 col6\" >0.7073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d12e3_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_d12e3_row8_col0\" class=\"data row8 col0\" >0.8696</td>\n",
              "      <td id=\"T_d12e3_row8_col1\" class=\"data row8 col1\" >0.0000</td>\n",
              "      <td id=\"T_d12e3_row8_col2\" class=\"data row8 col2\" >0.8750</td>\n",
              "      <td id=\"T_d12e3_row8_col3\" class=\"data row8 col3\" >0.7778</td>\n",
              "      <td id=\"T_d12e3_row8_col4\" class=\"data row8 col4\" >0.8235</td>\n",
              "      <td id=\"T_d12e3_row8_col5\" class=\"data row8 col5\" >0.7206</td>\n",
              "      <td id=\"T_d12e3_row8_col6\" class=\"data row8 col6\" >0.7238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d12e3_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_d12e3_row9_col0\" class=\"data row9 col0\" >0.8261</td>\n",
              "      <td id=\"T_d12e3_row9_col1\" class=\"data row9 col1\" >0.0000</td>\n",
              "      <td id=\"T_d12e3_row9_col2\" class=\"data row9 col2\" >0.7500</td>\n",
              "      <td id=\"T_d12e3_row9_col3\" class=\"data row9 col3\" >0.7500</td>\n",
              "      <td id=\"T_d12e3_row9_col4\" class=\"data row9 col4\" >0.7500</td>\n",
              "      <td id=\"T_d12e3_row9_col5\" class=\"data row9 col5\" >0.6167</td>\n",
              "      <td id=\"T_d12e3_row9_col6\" class=\"data row9 col6\" >0.6167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d12e3_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
              "      <td id=\"T_d12e3_row10_col0\" class=\"data row10 col0\" >0.8304</td>\n",
              "      <td id=\"T_d12e3_row10_col1\" class=\"data row10 col1\" >0.0000</td>\n",
              "      <td id=\"T_d12e3_row10_col2\" class=\"data row10 col2\" >0.7208</td>\n",
              "      <td id=\"T_d12e3_row10_col3\" class=\"data row10 col3\" >0.8012</td>\n",
              "      <td id=\"T_d12e3_row10_col4\" class=\"data row10 col4\" >0.7516</td>\n",
              "      <td id=\"T_d12e3_row10_col5\" class=\"data row10 col5\" >0.6246</td>\n",
              "      <td id=\"T_d12e3_row10_col6\" class=\"data row10 col6\" >0.6326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d12e3_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
              "      <td id=\"T_d12e3_row11_col0\" class=\"data row11 col0\" >0.0510</td>\n",
              "      <td id=\"T_d12e3_row11_col1\" class=\"data row11 col1\" >0.0000</td>\n",
              "      <td id=\"T_d12e3_row11_col2\" class=\"data row11 col2\" >0.1213</td>\n",
              "      <td id=\"T_d12e3_row11_col3\" class=\"data row11 col3\" >0.0927</td>\n",
              "      <td id=\"T_d12e3_row11_col4\" class=\"data row11 col4\" >0.0825</td>\n",
              "      <td id=\"T_d12e3_row11_col5\" class=\"data row11 col5\" >0.1156</td>\n",
              "      <td id=\"T_d12e3_row11_col6\" class=\"data row11 col6\" >0.1151</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6d0eb18ccb6b4ae8b178557763e2abf5"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tuned_ridge = tune_model(ridge)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474,
          "referenced_widgets": [
            "799784604c9449c3a3fc4618d2758084",
            "4076c0a56b0c44e08915505d9f4bdf23",
            "eb6b54db87e84cf690043727a634c77f",
            "b32093152af54d86a360a9bb1458b1bb",
            "2551af57a0824043b52c5c19fc43e982",
            "4d07fdef153745a3801741b33454455e",
            "a20a75648f474f0f86af582c1df38465",
            "18c34bb4f37746918fa031a9fe55d7cf",
            "2bae895e8f7d47ceb214d0c4cc4b55a1",
            "397bdd306fc64bde9d9d1201d0e36167",
            "e9869933ffe944ddabae22090699d2cf"
          ]
        },
        "id": "cno19ZOocn9-",
        "outputId": "10389830-6f04-4e72-b854-1d8b0a616e8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f9d9326db80>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_eb94e_row10_col0, #T_eb94e_row10_col1, #T_eb94e_row10_col2, #T_eb94e_row10_col3, #T_eb94e_row10_col4, #T_eb94e_row10_col5, #T_eb94e_row10_col6 {\n",
              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_eb94e\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_eb94e_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
              "      <th id=\"T_eb94e_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
              "      <th id=\"T_eb94e_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
              "      <th id=\"T_eb94e_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
              "      <th id=\"T_eb94e_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
              "      <th id=\"T_eb94e_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
              "      <th id=\"T_eb94e_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >Fold</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "      <th class=\"blank col5\" >&nbsp;</th>\n",
              "      <th class=\"blank col6\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_eb94e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_eb94e_row0_col0\" class=\"data row0 col0\" >0.8333</td>\n",
              "      <td id=\"T_eb94e_row0_col1\" class=\"data row0 col1\" >0.0000</td>\n",
              "      <td id=\"T_eb94e_row0_col2\" class=\"data row0 col2\" >0.6667</td>\n",
              "      <td id=\"T_eb94e_row0_col3\" class=\"data row0 col3\" >0.8571</td>\n",
              "      <td id=\"T_eb94e_row0_col4\" class=\"data row0 col4\" >0.7500</td>\n",
              "      <td id=\"T_eb94e_row0_col5\" class=\"data row0 col5\" >0.6279</td>\n",
              "      <td id=\"T_eb94e_row0_col6\" class=\"data row0 col6\" >0.6391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_eb94e_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_eb94e_row1_col0\" class=\"data row1 col0\" >0.8750</td>\n",
              "      <td id=\"T_eb94e_row1_col1\" class=\"data row1 col1\" >0.0000</td>\n",
              "      <td id=\"T_eb94e_row1_col2\" class=\"data row1 col2\" >0.7778</td>\n",
              "      <td id=\"T_eb94e_row1_col3\" class=\"data row1 col3\" >0.8750</td>\n",
              "      <td id=\"T_eb94e_row1_col4\" class=\"data row1 col4\" >0.8235</td>\n",
              "      <td id=\"T_eb94e_row1_col5\" class=\"data row1 col5\" >0.7273</td>\n",
              "      <td id=\"T_eb94e_row1_col6\" class=\"data row1 col6\" >0.7303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_eb94e_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_eb94e_row2_col0\" class=\"data row2 col0\" >0.7917</td>\n",
              "      <td id=\"T_eb94e_row2_col1\" class=\"data row2 col1\" >0.0000</td>\n",
              "      <td id=\"T_eb94e_row2_col2\" class=\"data row2 col2\" >0.6667</td>\n",
              "      <td id=\"T_eb94e_row2_col3\" class=\"data row2 col3\" >0.7500</td>\n",
              "      <td id=\"T_eb94e_row2_col4\" class=\"data row2 col4\" >0.7059</td>\n",
              "      <td id=\"T_eb94e_row2_col5\" class=\"data row2 col5\" >0.5455</td>\n",
              "      <td id=\"T_eb94e_row2_col6\" class=\"data row2 col6\" >0.5477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_eb94e_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_eb94e_row3_col0\" class=\"data row3 col0\" >0.7083</td>\n",
              "      <td id=\"T_eb94e_row3_col1\" class=\"data row3 col1\" >0.0000</td>\n",
              "      <td id=\"T_eb94e_row3_col2\" class=\"data row3 col2\" >0.4444</td>\n",
              "      <td id=\"T_eb94e_row3_col3\" class=\"data row3 col3\" >0.6667</td>\n",
              "      <td id=\"T_eb94e_row3_col4\" class=\"data row3 col4\" >0.5333</td>\n",
              "      <td id=\"T_eb94e_row3_col5\" class=\"data row3 col5\" >0.3333</td>\n",
              "      <td id=\"T_eb94e_row3_col6\" class=\"data row3 col6\" >0.3478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_eb94e_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_eb94e_row4_col0\" class=\"data row4 col0\" >0.8333</td>\n",
              "      <td id=\"T_eb94e_row4_col1\" class=\"data row4 col1\" >0.0000</td>\n",
              "      <td id=\"T_eb94e_row4_col2\" class=\"data row4 col2\" >0.7778</td>\n",
              "      <td id=\"T_eb94e_row4_col3\" class=\"data row4 col3\" >0.7778</td>\n",
              "      <td id=\"T_eb94e_row4_col4\" class=\"data row4 col4\" >0.7778</td>\n",
              "      <td id=\"T_eb94e_row4_col5\" class=\"data row4 col5\" >0.6444</td>\n",
              "      <td id=\"T_eb94e_row4_col6\" class=\"data row4 col6\" >0.6444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_eb94e_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_eb94e_row5_col0\" class=\"data row5 col0\" >0.8696</td>\n",
              "      <td id=\"T_eb94e_row5_col1\" class=\"data row5 col1\" >0.0000</td>\n",
              "      <td id=\"T_eb94e_row5_col2\" class=\"data row5 col2\" >0.6250</td>\n",
              "      <td id=\"T_eb94e_row5_col3\" class=\"data row5 col3\" >1.0000</td>\n",
              "      <td id=\"T_eb94e_row5_col4\" class=\"data row5 col4\" >0.7692</td>\n",
              "      <td id=\"T_eb94e_row5_col5\" class=\"data row5 col5\" >0.6849</td>\n",
              "      <td id=\"T_eb94e_row5_col6\" class=\"data row5 col6\" >0.7217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_eb94e_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_eb94e_row6_col0\" class=\"data row6 col0\" >0.8261</td>\n",
              "      <td id=\"T_eb94e_row6_col1\" class=\"data row6 col1\" >0.0000</td>\n",
              "      <td id=\"T_eb94e_row6_col2\" class=\"data row6 col2\" >0.7500</td>\n",
              "      <td id=\"T_eb94e_row6_col3\" class=\"data row6 col3\" >0.7500</td>\n",
              "      <td id=\"T_eb94e_row6_col4\" class=\"data row6 col4\" >0.7500</td>\n",
              "      <td id=\"T_eb94e_row6_col5\" class=\"data row6 col5\" >0.6167</td>\n",
              "      <td id=\"T_eb94e_row6_col6\" class=\"data row6 col6\" >0.6167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_eb94e_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_eb94e_row7_col0\" class=\"data row7 col0\" >0.9130</td>\n",
              "      <td id=\"T_eb94e_row7_col1\" class=\"data row7 col1\" >0.0000</td>\n",
              "      <td id=\"T_eb94e_row7_col2\" class=\"data row7 col2\" >0.7500</td>\n",
              "      <td id=\"T_eb94e_row7_col3\" class=\"data row7 col3\" >1.0000</td>\n",
              "      <td id=\"T_eb94e_row7_col4\" class=\"data row7 col4\" >0.8571</td>\n",
              "      <td id=\"T_eb94e_row7_col5\" class=\"data row7 col5\" >0.7965</td>\n",
              "      <td id=\"T_eb94e_row7_col6\" class=\"data row7 col6\" >0.8135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_eb94e_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_eb94e_row8_col0\" class=\"data row8 col0\" >0.9130</td>\n",
              "      <td id=\"T_eb94e_row8_col1\" class=\"data row8 col1\" >0.0000</td>\n",
              "      <td id=\"T_eb94e_row8_col2\" class=\"data row8 col2\" >1.0000</td>\n",
              "      <td id=\"T_eb94e_row8_col3\" class=\"data row8 col3\" >0.8000</td>\n",
              "      <td id=\"T_eb94e_row8_col4\" class=\"data row8 col4\" >0.8889</td>\n",
              "      <td id=\"T_eb94e_row8_col5\" class=\"data row8 col5\" >0.8189</td>\n",
              "      <td id=\"T_eb94e_row8_col6\" class=\"data row8 col6\" >0.8327</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_eb94e_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_eb94e_row9_col0\" class=\"data row9 col0\" >0.7826</td>\n",
              "      <td id=\"T_eb94e_row9_col1\" class=\"data row9 col1\" >0.0000</td>\n",
              "      <td id=\"T_eb94e_row9_col2\" class=\"data row9 col2\" >0.6250</td>\n",
              "      <td id=\"T_eb94e_row9_col3\" class=\"data row9 col3\" >0.7143</td>\n",
              "      <td id=\"T_eb94e_row9_col4\" class=\"data row9 col4\" >0.6667</td>\n",
              "      <td id=\"T_eb94e_row9_col5\" class=\"data row9 col5\" >0.5064</td>\n",
              "      <td id=\"T_eb94e_row9_col6\" class=\"data row9 col6\" >0.5089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_eb94e_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
              "      <td id=\"T_eb94e_row10_col0\" class=\"data row10 col0\" >0.8346</td>\n",
              "      <td id=\"T_eb94e_row10_col1\" class=\"data row10 col1\" >0.0000</td>\n",
              "      <td id=\"T_eb94e_row10_col2\" class=\"data row10 col2\" >0.7083</td>\n",
              "      <td id=\"T_eb94e_row10_col3\" class=\"data row10 col3\" >0.8191</td>\n",
              "      <td id=\"T_eb94e_row10_col4\" class=\"data row10 col4\" >0.7522</td>\n",
              "      <td id=\"T_eb94e_row10_col5\" class=\"data row10 col5\" >0.6302</td>\n",
              "      <td id=\"T_eb94e_row10_col6\" class=\"data row10 col6\" >0.6403</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_eb94e_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
              "      <td id=\"T_eb94e_row11_col0\" class=\"data row11 col0\" >0.0598</td>\n",
              "      <td id=\"T_eb94e_row11_col1\" class=\"data row11 col1\" >0.0000</td>\n",
              "      <td id=\"T_eb94e_row11_col2\" class=\"data row11 col2\" >0.1361</td>\n",
              "      <td id=\"T_eb94e_row11_col3\" class=\"data row11 col3\" >0.1076</td>\n",
              "      <td id=\"T_eb94e_row11_col4\" class=\"data row11 col4\" >0.0965</td>\n",
              "      <td id=\"T_eb94e_row11_col5\" class=\"data row11 col5\" >0.1366</td>\n",
              "      <td id=\"T_eb94e_row11_col6\" class=\"data row11 col6\" >0.1392</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
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          "data": {
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              "Processing:   0%|          | 0/7 [00:00<?, ?it/s]"
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n"
          ]
        },
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    {
      "cell_type": "code",
      "source": [
        "predict_model(tuned_ridge)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MlQcUL9LcsH4",
        "outputId": "963b85b7-c330-4490-83b0-c1944fc9610b"
      },
      "execution_count": null,
      "outputs": [
        {
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              "<pandas.io.formats.style.Styler at 0x7f9d911da190>"
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              "</style>\n",
              "<table id=\"T_526c5\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_526c5_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_526c5_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th id=\"T_526c5_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
              "      <th id=\"T_526c5_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
              "      <th id=\"T_526c5_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
              "      <th id=\"T_526c5_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
              "      <th id=\"T_526c5_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th id=\"T_526c5_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_526c5_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_526c5_row0_col0\" class=\"data row0 col0\" >Ridge Classifier</td>\n",
              "      <td id=\"T_526c5_row0_col1\" class=\"data row0 col1\" >0.8644</td>\n",
              "      <td id=\"T_526c5_row0_col2\" class=\"data row0 col2\" >0.8308</td>\n",
              "      <td id=\"T_526c5_row0_col3\" class=\"data row0 col3\" >0.7143</td>\n",
              "      <td id=\"T_526c5_row0_col4\" class=\"data row0 col4\" >0.8824</td>\n",
              "      <td id=\"T_526c5_row0_col5\" class=\"data row0 col5\" >0.7895</td>\n",
              "      <td id=\"T_526c5_row0_col6\" class=\"data row0 col6\" >0.6911</td>\n",
              "      <td id=\"T_526c5_row0_col7\" class=\"data row0 col7\" >0.6995</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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          "metadata": {}
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          "data": {
            "text/plain": [
              "     trestbps        chol  thalach  fbs  restecg  exang   age  sex   cp  \\\n",
              "293     130.0  182.000000    148.0  0.0      0.0    0.0  53.0  1.0  4.0   \n",
              "246     120.0  171.000000    137.0  0.0      0.0    0.0  54.0  1.0  1.0   \n",
              "171     120.0  243.000000    160.0  0.0      0.0    0.0  29.0  1.0  2.0   \n",
              "290     120.0  166.000000    180.0  0.0      0.0    0.0  36.0  1.0  2.0   \n",
              "287     140.0  250.848709    140.0  0.0      0.0    0.0  59.0  1.0  4.0   \n",
              "207     120.0  308.000000    180.0  0.0      2.0    0.0  35.0  1.0  2.0   \n",
              "140     160.0  331.000000     94.0  0.0      0.0    1.0  52.0  1.0  4.0   \n",
              "257     130.0  394.000000    150.0  0.0      2.0    0.0  55.0  0.0  2.0   \n",
              "104     118.0  186.000000    124.0  0.0      0.0    0.0  46.0  1.0  4.0   \n",
              "83      160.0  196.000000    165.0  0.0      0.0    0.0  52.0  1.0  2.0   \n",
              "141     140.0  341.000000    125.0  0.0      1.0    1.0  50.0  1.0  4.0   \n",
              "193     130.0  245.000000    160.0  0.0      0.0    0.0  48.0  1.0  2.0   \n",
              "159     160.0  195.000000    130.0  0.0      1.0    0.0  54.0  1.0  2.0   \n",
              "255     145.0  248.000000     96.0  0.0      0.0    1.0  55.0  1.0  4.0   \n",
              "139     150.0  247.000000    130.0  0.0      0.0    1.0  43.0  1.0  4.0   \n",
              "86      170.0  263.000000    112.0  1.0      0.0    1.0  65.0  1.0  4.0   \n",
              "240     160.0  193.000000    102.0  0.0      0.0    1.0  48.0  1.0  4.0   \n",
              "222     160.0  303.000000    150.0  0.0      0.0    1.0  51.0  0.0  4.0   \n",
              "170     140.0  179.000000    160.0  0.0      0.0    0.0  58.0  1.0  3.0   \n",
              "33      130.0  172.000000    130.0  0.0      1.0    0.0  41.0  1.0  4.0   \n",
              "185     160.0  312.000000    130.0  0.0      0.0    0.0  54.0  0.0  2.0   \n",
              "283     130.0  220.000000    160.0  0.0      0.0    1.0  51.0  0.0  3.0   \n",
              "110     130.0  188.000000    124.0  0.0      0.0    0.0  59.0  0.0  2.0   \n",
              "232     130.0  281.000000    167.0  0.0      0.0    0.0  40.0  1.0  3.0   \n",
              "202     120.0  230.000000    150.0  0.0      0.0    0.0  46.0  1.0  3.0   \n",
              "2       130.0  283.000000     98.0  0.0      1.0    0.0  37.0  1.0  2.0   \n",
              "137     120.0  250.848709    146.0  0.0      1.0    0.0  39.0  1.0  2.0   \n",
              "55      160.0  194.000000    170.0  0.0      0.0    0.0  51.0  0.0  2.0   \n",
              "8       140.0  207.000000    130.0  0.0      0.0    1.0  37.0  1.0  4.0   \n",
              "275     180.0  250.848709    180.0  0.0      0.0    0.0  45.0  0.0  2.0   \n",
              "3       138.0  214.000000    108.0  0.0      0.0    1.0  48.0  0.0  4.0   \n",
              "155     155.0  342.000000    150.0  1.0      0.0    1.0  56.0  1.0  4.0   \n",
              "90      110.0  250.848709    160.0  0.0      0.0    0.0  49.0  0.0  2.0   \n",
              "31      130.0  250.848709    114.0  0.0      0.0    0.0  56.0  1.0  3.0   \n",
              "164     120.0  196.000000    150.0  0.0      0.0    0.0  42.0  1.0  2.0   \n",
              "203     160.0  147.000000    146.0  0.0      0.0    0.0  42.0  1.0  3.0   \n",
              "56      120.0  270.000000    153.0  0.0      0.0    1.0  31.0  1.0  4.0   \n",
              "29      125.0  188.000000    145.0  0.0      0.0    0.0  51.0  1.0  2.0   \n",
              "186     160.0  211.000000     92.0  1.0      1.0    0.0  58.0  1.0  3.0   \n",
              "253     125.0  292.000000    115.0  0.0      1.0    1.0  61.0  1.0  4.0   \n",
              "248     122.0  275.000000    150.0  1.0      1.0    1.0  48.0  1.0  4.0   \n",
              "252     120.0  260.000000    115.0  0.0      0.0    0.0  48.0  1.0  4.0   \n",
              "22      124.0  201.000000    164.0  0.0      0.0    0.0  49.0  0.0  2.0   \n",
              "234     112.0  250.000000    142.0  0.0      0.0    0.0  41.0  1.0  4.0   \n",
              "91      130.0  307.000000    140.0  0.0      0.0    0.0  39.0  1.0  4.0   \n",
              "285     140.0  358.000000    170.0  0.0      0.0    0.0  42.0  1.0  4.0   \n",
              "73      120.0  218.000000    115.0  0.0      1.0    0.0  44.0  0.0  4.0   \n",
              "156     110.0  250.848709    150.0  0.0      0.0    1.0  38.0  1.0  4.0   \n",
              "80      110.0  277.000000    160.0  0.0      0.0    0.0  55.0  1.0  3.0   \n",
              "165     140.0  250.848709    140.0  0.0      0.0    0.0  52.0  0.0  2.0   \n",
              "148     120.0  168.000000    160.0  0.0      0.0    0.0  50.0  1.0  2.0   \n",
              "114     130.0  207.000000    135.0  0.0      1.0    0.0  49.0  0.0  3.0   \n",
              "143     140.0  243.000000    155.0  0.0      0.0    0.0  53.0  1.0  4.0   \n",
              "1       160.0  180.000000    156.0  0.0      0.0    0.0  49.0  0.0  3.0   \n",
              "269     130.0  242.000000     91.0  0.0      0.0    1.0  54.0  1.0  4.0   \n",
              "144     120.0  279.000000    150.0  0.0      0.0    0.0  56.0  0.0  2.0   \n",
              "98      120.0   85.000000    140.0  0.0      0.0    0.0  56.0  1.0  4.0   \n",
              "77      140.0  167.000000    150.0  0.0      0.0    0.0  35.0  0.0  4.0   \n",
              "119     140.0  156.000000    180.0  0.0      0.0    0.0  34.0  1.0  1.0   \n",
              "\n",
              "     oldpeak  num  prediction_label  \n",
              "293      0.0  0.0                 0  \n",
              "246      2.0  0.0                 0  \n",
              "171      0.0  0.0                 0  \n",
              "290      0.0  0.0                 0  \n",
              "287      0.0  0.0                 0  \n",
              "207      0.0  0.0                 0  \n",
              "140      2.5  1.0                 1  \n",
              "257      0.0  0.0                 0  \n",
              "104      0.0  1.0                 0  \n",
              "83       0.0  0.0                 0  \n",
              "141      2.5  1.0                 1  \n",
              "193      0.0  0.0                 0  \n",
              "159      1.0  0.0                 0  \n",
              "255      2.0  1.0                 1  \n",
              "139      2.0  1.0                 1  \n",
              "86       2.0  1.0                 1  \n",
              "240      3.0  1.0                 1  \n",
              "222      1.0  1.0                 0  \n",
              "170      0.0  0.0                 0  \n",
              "33       2.0  1.0                 1  \n",
              "185      0.0  0.0                 0  \n",
              "283      2.0  0.0                 1  \n",
              "110      1.0  0.0                 0  \n",
              "232      0.0  0.0                 0  \n",
              "202      0.0  0.0                 0  \n",
              "2        0.0  0.0                 0  \n",
              "137      2.0  0.0                 0  \n",
              "55       0.0  0.0                 0  \n",
              "8        1.5  1.0                 1  \n",
              "275      0.0  0.0                 0  \n",
              "3        1.5  1.0                 1  \n",
              "155      3.0  1.0                 1  \n",
              "90       0.0  0.0                 0  \n",
              "31       0.0  0.0                 0  \n",
              "164      0.0  0.0                 0  \n",
              "203      0.0  0.0                 0  \n",
              "56       1.5  1.0                 1  \n",
              "29       0.0  0.0                 0  \n",
              "186      0.0  1.0                 0  \n",
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              "234      0.0  0.0                 0  \n",
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              "285      0.0  0.0                 0  \n",
              "73       0.0  0.0                 0  \n",
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              "1        1.0  1.0                 0  \n",
              "269      1.0  1.0                 1  \n",
              "144      1.0  1.0                 0  \n",
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              "      <th></th>\n",
              "      <th>trestbps</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>290</th>\n",
              "      <td>120.0</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>180.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>287</th>\n",
              "      <td>140.0</td>\n",
              "      <td>250.848709</td>\n",
              "      <td>140.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>207</th>\n",
              "      <td>120.0</td>\n",
              "      <td>308.000000</td>\n",
              "      <td>180.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>160.0</td>\n",
              "      <td>331.000000</td>\n",
              "      <td>94.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>257</th>\n",
              "      <td>130.0</td>\n",
              "      <td>394.000000</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>118.0</td>\n",
              "      <td>186.000000</td>\n",
              "      <td>124.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>160.0</td>\n",
              "      <td>196.000000</td>\n",
              "      <td>165.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>140.0</td>\n",
              "      <td>341.000000</td>\n",
              "      <td>125.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>130.0</td>\n",
              "      <td>245.000000</td>\n",
              "      <td>160.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159</th>\n",
              "      <td>160.0</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>130.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>255</th>\n",
              "      <td>145.0</td>\n",
              "      <td>248.000000</td>\n",
              "      <td>96.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>150.0</td>\n",
              "      <td>247.000000</td>\n",
              "      <td>130.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>170.0</td>\n",
              "      <td>263.000000</td>\n",
              "      <td>112.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>240</th>\n",
              "      <td>160.0</td>\n",
              "      <td>193.000000</td>\n",
              "      <td>102.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>222</th>\n",
              "      <td>160.0</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>140.0</td>\n",
              "      <td>179.000000</td>\n",
              "      <td>160.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>130.0</td>\n",
              "      <td>172.000000</td>\n",
              "      <td>130.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>185</th>\n",
              "      <td>160.0</td>\n",
              "      <td>312.000000</td>\n",
              "      <td>130.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>283</th>\n",
              "      <td>130.0</td>\n",
              "      <td>220.000000</td>\n",
              "      <td>160.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>130.0</td>\n",
              "      <td>188.000000</td>\n",
              "      <td>124.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>232</th>\n",
              "      <td>130.0</td>\n",
              "      <td>281.000000</td>\n",
              "      <td>167.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>202</th>\n",
              "      <td>120.0</td>\n",
              "      <td>230.000000</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>130.0</td>\n",
              "      <td>283.000000</td>\n",
              "      <td>98.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>120.0</td>\n",
              "      <td>250.848709</td>\n",
              "      <td>146.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>160.0</td>\n",
              "      <td>194.000000</td>\n",
              "      <td>170.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>140.0</td>\n",
              "      <td>207.000000</td>\n",
              "      <td>130.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>275</th>\n",
              "      <td>180.0</td>\n",
              "      <td>250.848709</td>\n",
              "      <td>180.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>138.0</td>\n",
              "      <td>214.000000</td>\n",
              "      <td>108.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>155.0</td>\n",
              "      <td>342.000000</td>\n",
              "      <td>150.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>110.0</td>\n",
              "      <td>250.848709</td>\n",
              "      <td>160.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>130.0</td>\n",
              "      <td>250.848709</td>\n",
              "      <td>114.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164</th>\n",
              "      <td>120.0</td>\n",
              "      <td>196.000000</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>203</th>\n",
              "      <td>160.0</td>\n",
              "      <td>147.000000</td>\n",
              "      <td>146.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>120.0</td>\n",
              "      <td>270.000000</td>\n",
              "      <td>153.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>125.0</td>\n",
              "      <td>188.000000</td>\n",
              "      <td>145.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>186</th>\n",
              "      <td>160.0</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>92.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>253</th>\n",
              "      <td>125.0</td>\n",
              "      <td>292.000000</td>\n",
              "      <td>115.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248</th>\n",
              "      <td>122.0</td>\n",
              "      <td>275.000000</td>\n",
              "      <td>150.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>252</th>\n",
              "      <td>120.0</td>\n",
              "      <td>260.000000</td>\n",
              "      <td>115.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>124.0</td>\n",
              "      <td>201.000000</td>\n",
              "      <td>164.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>234</th>\n",
              "      <td>112.0</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>142.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>130.0</td>\n",
              "      <td>307.000000</td>\n",
              "      <td>140.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>285</th>\n",
              "      <td>140.0</td>\n",
              "      <td>358.000000</td>\n",
              "      <td>170.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>120.0</td>\n",
              "      <td>218.000000</td>\n",
              "      <td>115.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>110.0</td>\n",
              "      <td>250.848709</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>110.0</td>\n",
              "      <td>277.000000</td>\n",
              "      <td>160.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>140.0</td>\n",
              "      <td>250.848709</td>\n",
              "      <td>140.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>120.0</td>\n",
              "      <td>168.000000</td>\n",
              "      <td>160.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>130.0</td>\n",
              "      <td>207.000000</td>\n",
              "      <td>135.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>140.0</td>\n",
              "      <td>243.000000</td>\n",
              "      <td>155.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>160.0</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>156.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>269</th>\n",
              "      <td>130.0</td>\n",
              "      <td>242.000000</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>120.0</td>\n",
              "      <td>279.000000</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>120.0</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>140.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>140.0</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>140.0</td>\n",
              "      <td>156.000000</td>\n",
              "      <td>180.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-45b32046-b74c-428c-95fe-aef995354e19')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-45b32046-b74c-428c-95fe-aef995354e19 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-45b32046-b74c-428c-95fe-aef995354e19');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "p3dCr1hocvyi"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}