PyCaret_Heart_Disease-Cleveland.ipynb

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        "6c37138667014578b8f08cf3f65790e6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
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  "cells": [
    {
      "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": "code",
      "source": [
        "# pycaret\n",
        "!pip install pycaret\n",
        "import pycaret as pc"
      ],
      "metadata": {
        "id": "kKKoAT3tQNqE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "238f7340-34cd-4c0f-9678-5cb461ad8824"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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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=75061 sha256=f53097fb85bd991c6ef2104d3e8799b56c84f51fb9c297360f18c1596bbc3ec1\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=a9a5d6cfbb2e5859aa84856f9a0d9db6b4a09188f040f8f6c65a5602460f491d\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, jedi, deprecation, deprecated, ansi2html, sktime, scikit-plot, pyod, dash, pmdarima, jupyter-dash, category-encoders, tbats, plotly-resampler, pycaret\n",
            "Successfully installed ansi2html-1.8.0 category-encoders-2.6.0 dash-2.9.2 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 jupyter-dash-0.4.2 kaleido-0.2.1 orjson-3.8.10 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-0.17.1 tbats-1.1.2 trace-updater-0.0.9.1 wurlitzer-3.0.3 xxhash-3.2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.classification import *"
      ],
      "metadata": {
        "id": "bAPZqZ37oHAi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#from pc.utils import enable_colab\n",
        "#enable_colab()\n",
        "\n",
        "# ERROR IN ABOVE - BUT NOT NECESSARY FOR MODELING"
      ],
      "metadata": {
        "id": "KF-7P78hlX-N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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": "XCVoL6th6RgI"
      },
      "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": "1f127562-a9ec-4c0a-ac1b-36ad35571f00"
      },
      "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": "073b6089-2a94-4e91-9eab-77d500837688"
      },
      "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  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d1fd3b90-edcb-4131-beca-b5d0635e0383\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>num</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>45.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>264.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>132.0</td>\n",
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              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>68.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>193.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>57.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>131.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>57.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>236.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>38.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>138.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>173.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d1fd3b90-edcb-4131-beca-b5d0635e0383')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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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": {
        "id": "UrCeHciD8m9e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f01498e-9ca5-4f94-9074-4d9556ede1f4"
      },
      "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": "bb63d84e-b0a1-484d-e3b5-52f1424d110a"
      },
      "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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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>41.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>204.0</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "cleveland.corr()"
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      "metadata": {
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          "height": 488
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        "id": "STY_l3XieVQD",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               age       sex        cp  trestbps      chol       fbs  \\\n",
              "age       1.000000 -0.097542  0.104139  0.284946  0.208950  0.118530   \n",
              "sex      -0.097542  1.000000  0.010084 -0.064456 -0.199915  0.047862   \n",
              "cp        0.104139  0.010084  1.000000 -0.036077  0.072319 -0.039975   \n",
              "trestbps  0.284946 -0.064456 -0.036077  1.000000  0.130120  0.175340   \n",
              "chol      0.208950 -0.199915  0.072319  0.130120  1.000000  0.009841   \n",
              "fbs       0.118530  0.047862 -0.039975  0.175340  0.009841  1.000000   \n",
              "restecg   0.148868  0.021647  0.067505  0.146560  0.171043  0.069564   \n",
              "thalach  -0.393806 -0.048663 -0.334422 -0.045351 -0.003432 -0.007854   \n",
              "exang     0.091661  0.146201  0.384060  0.064762  0.061310  0.025665   \n",
              "oldpeak   0.203805  0.102173  0.202277  0.189171  0.046564  0.005747   \n",
              "slope     0.161770  0.037533  0.152050  0.117382 -0.004062  0.059894   \n",
              "ca        0.365323  0.086048  0.233117  0.097528  0.123726  0.140764   \n",
              "thal      0.128303  0.380581  0.262089  0.134424  0.018351  0.064625   \n",
              "num       0.223120  0.276816  0.414446  0.150825  0.085164  0.025264   \n",
              "\n",
              "           restecg   thalach     exang   oldpeak     slope        ca  \\\n",
              "age       0.148868 -0.393806  0.091661  0.203805  0.161770  0.365323   \n",
              "sex       0.021647 -0.048663  0.146201  0.102173  0.037533  0.086048   \n",
              "cp        0.067505 -0.334422  0.384060  0.202277  0.152050  0.233117   \n",
              "trestbps  0.146560 -0.045351  0.064762  0.189171  0.117382  0.097528   \n",
              "chol      0.171043 -0.003432  0.061310  0.046564 -0.004062  0.123726   \n",
              "fbs       0.069564 -0.007854  0.025665  0.005747  0.059894  0.140764   \n",
              "restecg   1.000000 -0.083389  0.084867  0.114133  0.133946  0.131749   \n",
              "thalach  -0.083389  1.000000 -0.378103 -0.343085 -0.385601 -0.265699   \n",
              "exang     0.084867 -0.378103  1.000000  0.288223  0.257748  0.145788   \n",
              "oldpeak   0.114133 -0.343085  0.288223  1.000000  0.577537  0.301067   \n",
              "slope     0.133946 -0.385601  0.257748  0.577537  1.000000  0.110803   \n",
              "ca        0.131749 -0.265699  0.145788  0.301067  0.110803  1.000000   \n",
              "thal      0.024325 -0.274142  0.325240  0.342405  0.286792  0.255446   \n",
              "num       0.169202 -0.417167  0.431894  0.424510  0.339213  0.460033   \n",
              "\n",
              "              thal       num  \n",
              "age       0.128303  0.223120  \n",
              "sex       0.380581  0.276816  \n",
              "cp        0.262089  0.414446  \n",
              "trestbps  0.134424  0.150825  \n",
              "chol      0.018351  0.085164  \n",
              "fbs       0.064625  0.025264  \n",
              "restecg   0.024325  0.169202  \n",
              "thalach  -0.274142 -0.417167  \n",
              "exang     0.325240  0.431894  \n",
              "oldpeak   0.342405  0.424510  \n",
              "slope     0.286792  0.339213  \n",
              "ca        0.255446  0.460033  \n",
              "thal      1.000000  0.522057  \n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.097542</td>\n",
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              "      <td>0.208950</td>\n",
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              "      <td>-0.393806</td>\n",
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              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <td>-0.097542</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.010084</td>\n",
              "      <td>-0.064456</td>\n",
              "      <td>-0.199915</td>\n",
              "      <td>0.047862</td>\n",
              "      <td>0.021647</td>\n",
              "      <td>-0.048663</td>\n",
              "      <td>0.146201</td>\n",
              "      <td>0.102173</td>\n",
              "      <td>0.037533</td>\n",
              "      <td>0.086048</td>\n",
              "      <td>0.380581</td>\n",
              "      <td>0.276816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cp</th>\n",
              "      <td>0.104139</td>\n",
              "      <td>0.010084</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.036077</td>\n",
              "      <td>0.072319</td>\n",
              "      <td>-0.039975</td>\n",
              "      <td>0.067505</td>\n",
              "      <td>-0.334422</td>\n",
              "      <td>0.384060</td>\n",
              "      <td>0.202277</td>\n",
              "      <td>0.152050</td>\n",
              "      <td>0.233117</td>\n",
              "      <td>0.262089</td>\n",
              "      <td>0.414446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trestbps</th>\n",
              "      <td>0.284946</td>\n",
              "      <td>-0.064456</td>\n",
              "      <td>-0.036077</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.130120</td>\n",
              "      <td>0.175340</td>\n",
              "      <td>0.146560</td>\n",
              "      <td>-0.045351</td>\n",
              "      <td>0.064762</td>\n",
              "      <td>0.189171</td>\n",
              "      <td>0.117382</td>\n",
              "      <td>0.097528</td>\n",
              "      <td>0.134424</td>\n",
              "      <td>0.150825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chol</th>\n",
              "      <td>0.208950</td>\n",
              "      <td>-0.199915</td>\n",
              "      <td>0.072319</td>\n",
              "      <td>0.130120</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.009841</td>\n",
              "      <td>0.171043</td>\n",
              "      <td>-0.003432</td>\n",
              "      <td>0.061310</td>\n",
              "      <td>0.046564</td>\n",
              "      <td>-0.004062</td>\n",
              "      <td>0.123726</td>\n",
              "      <td>0.018351</td>\n",
              "      <td>0.085164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fbs</th>\n",
              "      <td>0.118530</td>\n",
              "      <td>0.047862</td>\n",
              "      <td>-0.039975</td>\n",
              "      <td>0.175340</td>\n",
              "      <td>0.009841</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.069564</td>\n",
              "      <td>-0.007854</td>\n",
              "      <td>0.025665</td>\n",
              "      <td>0.005747</td>\n",
              "      <td>0.059894</td>\n",
              "      <td>0.140764</td>\n",
              "      <td>0.064625</td>\n",
              "      <td>0.025264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>restecg</th>\n",
              "      <td>0.148868</td>\n",
              "      <td>0.021647</td>\n",
              "      <td>0.067505</td>\n",
              "      <td>0.146560</td>\n",
              "      <td>0.171043</td>\n",
              "      <td>0.069564</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.083389</td>\n",
              "      <td>0.084867</td>\n",
              "      <td>0.114133</td>\n",
              "      <td>0.133946</td>\n",
              "      <td>0.131749</td>\n",
              "      <td>0.024325</td>\n",
              "      <td>0.169202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thalach</th>\n",
              "      <td>-0.393806</td>\n",
              "      <td>-0.048663</td>\n",
              "      <td>-0.334422</td>\n",
              "      <td>-0.045351</td>\n",
              "      <td>-0.003432</td>\n",
              "      <td>-0.007854</td>\n",
              "      <td>-0.083389</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.378103</td>\n",
              "      <td>-0.343085</td>\n",
              "      <td>-0.385601</td>\n",
              "      <td>-0.265699</td>\n",
              "      <td>-0.274142</td>\n",
              "      <td>-0.417167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exang</th>\n",
              "      <td>0.091661</td>\n",
              "      <td>0.146201</td>\n",
              "      <td>0.384060</td>\n",
              "      <td>0.064762</td>\n",
              "      <td>0.061310</td>\n",
              "      <td>0.025665</td>\n",
              "      <td>0.084867</td>\n",
              "      <td>-0.378103</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.288223</td>\n",
              "      <td>0.257748</td>\n",
              "      <td>0.145788</td>\n",
              "      <td>0.325240</td>\n",
              "      <td>0.431894</td>\n",
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              "    <tr>\n",
              "      <th>oldpeak</th>\n",
              "      <td>0.203805</td>\n",
              "      <td>0.102173</td>\n",
              "      <td>0.202277</td>\n",
              "      <td>0.189171</td>\n",
              "      <td>0.046564</td>\n",
              "      <td>0.005747</td>\n",
              "      <td>0.114133</td>\n",
              "      <td>-0.343085</td>\n",
              "      <td>0.288223</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.577537</td>\n",
              "      <td>0.301067</td>\n",
              "      <td>0.342405</td>\n",
              "      <td>0.424510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>slope</th>\n",
              "      <td>0.161770</td>\n",
              "      <td>0.037533</td>\n",
              "      <td>0.152050</td>\n",
              "      <td>0.117382</td>\n",
              "      <td>-0.004062</td>\n",
              "      <td>0.059894</td>\n",
              "      <td>0.133946</td>\n",
              "      <td>-0.385601</td>\n",
              "      <td>0.257748</td>\n",
              "      <td>0.577537</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.110803</td>\n",
              "      <td>0.286792</td>\n",
              "      <td>0.339213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ca</th>\n",
              "      <td>0.365323</td>\n",
              "      <td>0.086048</td>\n",
              "      <td>0.233117</td>\n",
              "      <td>0.097528</td>\n",
              "      <td>0.123726</td>\n",
              "      <td>0.140764</td>\n",
              "      <td>0.131749</td>\n",
              "      <td>-0.265699</td>\n",
              "      <td>0.145788</td>\n",
              "      <td>0.301067</td>\n",
              "      <td>0.110803</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.255446</td>\n",
              "      <td>0.460033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thal</th>\n",
              "      <td>0.128303</td>\n",
              "      <td>0.380581</td>\n",
              "      <td>0.262089</td>\n",
              "      <td>0.134424</td>\n",
              "      <td>0.018351</td>\n",
              "      <td>0.064625</td>\n",
              "      <td>0.024325</td>\n",
              "      <td>-0.274142</td>\n",
              "      <td>0.325240</td>\n",
              "      <td>0.342405</td>\n",
              "      <td>0.286792</td>\n",
              "      <td>0.255446</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.522057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num</th>\n",
              "      <td>0.223120</td>\n",
              "      <td>0.276816</td>\n",
              "      <td>0.414446</td>\n",
              "      <td>0.150825</td>\n",
              "      <td>0.085164</td>\n",
              "      <td>0.025264</td>\n",
              "      <td>0.169202</td>\n",
              "      <td>-0.417167</td>\n",
              "      <td>0.431894</td>\n",
              "      <td>0.424510</td>\n",
              "      <td>0.339213</td>\n",
              "      <td>0.460033</td>\n",
              "      <td>0.522057</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cleveland.drop(['slope', 'cp', 'thal', 'fbs', 'restecg'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "PyFVv4YDeh8E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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": "f4fc30a7-90ba-416c-c8ce-6f038427931c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  sex  trestbps   chol  thalach  exang  oldpeak   ca  num\n",
              "0  63.0  1.0     145.0  233.0    150.0    0.0      2.3  0.0  0.0\n",
              "1  67.0  1.0     160.0  286.0    108.0    1.0      1.5  3.0  1.0\n",
              "2  67.0  1.0     120.0  229.0    129.0    1.0      2.6  2.0  1.0\n",
              "3  37.0  1.0     130.0  250.0    187.0    0.0      3.5  0.0  0.0\n",
              "4  41.0  0.0     130.0  204.0    172.0    0.0      1.4  0.0  0.0"
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              "      <th>0</th>\n",
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              "      <td>229.0</td>\n",
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              "      <th>3</th>\n",
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              "      <th>4</th>\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-46d420a3-a5d0-4a88-8c82-5533788e6dd2 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-46d420a3-a5d0-4a88-8c82-5533788e6dd2');\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": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### STRATIFIED SAMPLING\n",
        "\n",
        "(due to difference sex representation)"
      ],
      "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": "8f144b5f-72ff-4b52-dbe6-33c65b749143"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    0.540984\n",
              "1.0    0.459016\n",
              "Name: num, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cleveland_strat_train[\"num\"].value_counts()/len(cleveland_strat_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vx2mSr8RQce9",
        "outputId": "ea838a40-150e-4397-be4b-51d18d23c281"
      },
      "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": 15
        }
      ]
    },
    {
      "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": "2f1e21f3-1772-4eea-8e2e-c6262a3f70bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age  sex  trestbps   chol  thalach  exang  oldpeak   ca\n",
              "98   52.0  1.0     134.0  201.0    158.0    0.0      0.8  1.0\n",
              "207  50.0  1.0     144.0  200.0    126.0    1.0      0.9  0.0\n",
              "8    63.0  1.0     130.0  254.0    147.0    0.0      1.4  1.0\n",
              "13   44.0  1.0     120.0  263.0    173.0    0.0      0.0  0.0\n",
              "12   56.0  1.0     130.0  256.0    142.0    1.0      0.6  1.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-edaba3a6-e43c-4b75-83d9-620297f5ff65\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>ca</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>52.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>134.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>158.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>207</th>\n",
              "      <td>50.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>63.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>254.0</td>\n",
              "      <td>147.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>44.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>263.0</td>\n",
              "      <td>173.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>56.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>256.0</td>\n",
              "      <td>142.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-edaba3a6-e43c-4b75-83d9-620297f5ff65')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-edaba3a6-e43c-4b75-83d9-620297f5ff65 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-edaba3a6-e43c-4b75-83d9-620297f5ff65');\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": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Correlated Block"
      ],
      "metadata": {
        "id": "Y7nSgU9gffIS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# set up for correlated\n",
        "cleveland_pc_setup = setup(data = cleveland_strat_train, test_data = cleveland_strat_test, target = 'num', session_id=42)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "VJFLlPimfakU",
        "outputId": "c5688770-75d4-4a1b-c063-8f051db7e32b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f5998b5a910>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_40b06_row8_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_40b06\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_40b06_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_40b06_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_40b06_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_40b06_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_40b06_row0_col1\" class=\"data row0 col1\" >42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40b06_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_40b06_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
              "      <td id=\"T_40b06_row1_col1\" class=\"data row1 col1\" >num</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40b06_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_40b06_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
              "      <td id=\"T_40b06_row2_col1\" class=\"data row2 col1\" >Binary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40b06_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_40b06_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
              "      <td id=\"T_40b06_row3_col1\" class=\"data row3 col1\" >(303, 9)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40b06_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_40b06_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_40b06_row4_col1\" class=\"data row4 col1\" >(303, 9)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40b06_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_40b06_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
              "      <td id=\"T_40b06_row5_col1\" class=\"data row5 col1\" >(242, 9)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40b06_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_40b06_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
              "      <td id=\"T_40b06_row6_col1\" class=\"data row6 col1\" >(61, 9)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40b06_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_40b06_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
              "      <td id=\"T_40b06_row7_col1\" class=\"data row7 col1\" >8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40b06_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_40b06_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
              "      <td id=\"T_40b06_row8_col1\" class=\"data row8 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40b06_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_40b06_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
              "      <td id=\"T_40b06_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40b06_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_40b06_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_40b06_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40b06_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_40b06_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_40b06_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40b06_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_40b06_row12_col0\" class=\"data row12 col0\" >Fold Generator</td>\n",
              "      <td id=\"T_40b06_row12_col1\" class=\"data row12 col1\" >StratifiedKFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40b06_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_40b06_row13_col0\" class=\"data row13 col0\" >Fold Number</td>\n",
              "      <td id=\"T_40b06_row13_col1\" class=\"data row13 col1\" >10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40b06_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_40b06_row14_col0\" class=\"data row14 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_40b06_row14_col1\" class=\"data row14 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40b06_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_40b06_row15_col0\" class=\"data row15 col0\" >Use GPU</td>\n",
              "      <td id=\"T_40b06_row15_col1\" class=\"data row15 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40b06_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_40b06_row16_col0\" class=\"data row16 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_40b06_row16_col1\" class=\"data row16 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40b06_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_40b06_row17_col0\" class=\"data row17 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_40b06_row17_col1\" class=\"data row17 col1\" >clf-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_40b06_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "      <td id=\"T_40b06_row18_col0\" class=\"data row18 col0\" >USI</td>\n",
              "      <td id=\"T_40b06_row18_col1\" class=\"data row18 col1\" >b1b8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# compare models\n",
        "compare_models()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 647,
          "referenced_widgets": [
            "1e03f11a05144ede9937cfdf8b49ef47",
            "c4b8d9d6263c4c4f85b4152fa8b8aac1",
            "d1c73695f57842daa137d9e91f347c09",
            "3e48ceb71a7e45a4beca6563f15af55d",
            "0eab6181334c4dc18c066838b906980f",
            "da94e7ae98f642889307afb41fdf4dcf",
            "b840fee55854478383b4ec602e5e42a3",
            "c0b3f161afd243cb858b7a30966828fb",
            "04bd4cd045f8440a85f532de5ad61649",
            "98775553bb9b4f4e814a31aa28e8350d",
            "be1297023c1544fcb389ec7ff88fe68f"
          ]
        },
        "id": "OPt9xEiZfeAa",
        "outputId": "d3fdf0fe-20b4-47ec-d0db-5a51ff7521b0"
      },
      "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 0x7f59996c5130>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_1e937 th {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_1e937_row0_col0, #T_1e937_row0_col2, #T_1e937_row0_col3, #T_1e937_row0_col4, #T_1e937_row1_col0, #T_1e937_row1_col1, #T_1e937_row1_col2, #T_1e937_row1_col3, #T_1e937_row1_col5, #T_1e937_row1_col6, #T_1e937_row1_col7, #T_1e937_row2_col0, #T_1e937_row2_col1, #T_1e937_row2_col2, #T_1e937_row2_col4, #T_1e937_row2_col5, #T_1e937_row2_col6, #T_1e937_row2_col7, #T_1e937_row3_col0, #T_1e937_row3_col1, #T_1e937_row3_col3, #T_1e937_row3_col4, #T_1e937_row3_col5, #T_1e937_row3_col6, #T_1e937_row3_col7, #T_1e937_row4_col0, #T_1e937_row4_col1, #T_1e937_row4_col2, #T_1e937_row4_col3, #T_1e937_row4_col4, #T_1e937_row4_col5, #T_1e937_row4_col6, #T_1e937_row4_col7, #T_1e937_row5_col0, #T_1e937_row5_col1, #T_1e937_row5_col2, #T_1e937_row5_col3, #T_1e937_row5_col4, #T_1e937_row5_col5, #T_1e937_row5_col6, #T_1e937_row5_col7, #T_1e937_row6_col0, #T_1e937_row6_col1, #T_1e937_row6_col2, #T_1e937_row6_col3, #T_1e937_row6_col4, #T_1e937_row6_col5, #T_1e937_row6_col6, #T_1e937_row6_col7, #T_1e937_row7_col0, #T_1e937_row7_col1, #T_1e937_row7_col2, #T_1e937_row7_col3, #T_1e937_row7_col4, #T_1e937_row7_col5, #T_1e937_row7_col6, #T_1e937_row7_col7, #T_1e937_row8_col0, #T_1e937_row8_col1, #T_1e937_row8_col2, #T_1e937_row8_col3, #T_1e937_row8_col4, #T_1e937_row8_col5, #T_1e937_row8_col6, #T_1e937_row8_col7, #T_1e937_row9_col0, #T_1e937_row9_col1, #T_1e937_row9_col2, #T_1e937_row9_col3, #T_1e937_row9_col4, #T_1e937_row9_col5, #T_1e937_row9_col6, #T_1e937_row9_col7, #T_1e937_row10_col0, #T_1e937_row10_col1, #T_1e937_row10_col2, #T_1e937_row10_col3, #T_1e937_row10_col4, #T_1e937_row10_col5, #T_1e937_row10_col6, #T_1e937_row10_col7, #T_1e937_row11_col0, #T_1e937_row11_col1, #T_1e937_row11_col2, #T_1e937_row11_col3, #T_1e937_row11_col4, #T_1e937_row11_col5, #T_1e937_row11_col6, #T_1e937_row11_col7, #T_1e937_row12_col0, #T_1e937_row12_col1, #T_1e937_row12_col2, #T_1e937_row12_col3, #T_1e937_row12_col4, #T_1e937_row12_col5, #T_1e937_row12_col6, #T_1e937_row12_col7, #T_1e937_row13_col0, #T_1e937_row13_col1, #T_1e937_row13_col2, #T_1e937_row13_col3, #T_1e937_row13_col4, #T_1e937_row13_col5, #T_1e937_row13_col6, #T_1e937_row13_col7, #T_1e937_row14_col0, #T_1e937_row14_col1, #T_1e937_row14_col2, #T_1e937_row14_col3, #T_1e937_row14_col4, #T_1e937_row14_col5, #T_1e937_row14_col6, #T_1e937_row14_col7 {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_1e937_row0_col1, #T_1e937_row0_col5, #T_1e937_row0_col6, #T_1e937_row0_col7, #T_1e937_row1_col4, #T_1e937_row2_col3, #T_1e937_row3_col2 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "}\n",
              "#T_1e937_row0_col8, #T_1e937_row1_col8, #T_1e937_row2_col8, #T_1e937_row3_col8, #T_1e937_row4_col8, #T_1e937_row5_col8, #T_1e937_row6_col8, #T_1e937_row7_col8, #T_1e937_row8_col8, #T_1e937_row9_col8, #T_1e937_row10_col8, #T_1e937_row11_col8, #T_1e937_row12_col8, #T_1e937_row14_col8 {\n",
              "  text-align: left;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "#T_1e937_row13_col8 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_1e937\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_1e937_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_1e937_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th id=\"T_1e937_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
              "      <th id=\"T_1e937_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
              "      <th id=\"T_1e937_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
              "      <th id=\"T_1e937_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
              "      <th id=\"T_1e937_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th id=\"T_1e937_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
              "      <th id=\"T_1e937_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_1e937_level0_row0\" class=\"row_heading level0 row0\" >lr</th>\n",
              "      <td id=\"T_1e937_row0_col0\" class=\"data row0 col0\" >Logistic Regression</td>\n",
              "      <td id=\"T_1e937_row0_col1\" class=\"data row0 col1\" >0.7975</td>\n",
              "      <td id=\"T_1e937_row0_col2\" class=\"data row0 col2\" >0.8710</td>\n",
              "      <td id=\"T_1e937_row0_col3\" class=\"data row0 col3\" >0.7402</td>\n",
              "      <td id=\"T_1e937_row0_col4\" class=\"data row0 col4\" >0.8172</td>\n",
              "      <td id=\"T_1e937_row0_col5\" class=\"data row0 col5\" >0.7681</td>\n",
              "      <td id=\"T_1e937_row0_col6\" class=\"data row0 col6\" >0.5901</td>\n",
              "      <td id=\"T_1e937_row0_col7\" class=\"data row0 col7\" >0.6008</td>\n",
              "      <td id=\"T_1e937_row0_col8\" class=\"data row0 col8\" >0.5230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1e937_level0_row1\" class=\"row_heading level0 row1\" >ridge</th>\n",
              "      <td id=\"T_1e937_row1_col0\" class=\"data row1 col0\" >Ridge Classifier</td>\n",
              "      <td id=\"T_1e937_row1_col1\" class=\"data row1 col1\" >0.7892</td>\n",
              "      <td id=\"T_1e937_row1_col2\" class=\"data row1 col2\" >0.0000</td>\n",
              "      <td id=\"T_1e937_row1_col3\" class=\"data row1 col3\" >0.7038</td>\n",
              "      <td id=\"T_1e937_row1_col4\" class=\"data row1 col4\" >0.8248</td>\n",
              "      <td id=\"T_1e937_row1_col5\" class=\"data row1 col5\" >0.7506</td>\n",
              "      <td id=\"T_1e937_row1_col6\" class=\"data row1 col6\" >0.5712</td>\n",
              "      <td id=\"T_1e937_row1_col7\" class=\"data row1 col7\" >0.5850</td>\n",
              "      <td id=\"T_1e937_row1_col8\" class=\"data row1 col8\" >0.0450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1e937_level0_row2\" class=\"row_heading level0 row2\" >et</th>\n",
              "      <td id=\"T_1e937_row2_col0\" class=\"data row2 col0\" >Extra Trees Classifier</td>\n",
              "      <td id=\"T_1e937_row2_col1\" class=\"data row2 col1\" >0.7848</td>\n",
              "      <td id=\"T_1e937_row2_col2\" class=\"data row2 col2\" >0.8643</td>\n",
              "      <td id=\"T_1e937_row2_col3\" class=\"data row2 col3\" >0.7576</td>\n",
              "      <td id=\"T_1e937_row2_col4\" class=\"data row2 col4\" >0.7778</td>\n",
              "      <td id=\"T_1e937_row2_col5\" class=\"data row2 col5\" >0.7637</td>\n",
              "      <td id=\"T_1e937_row2_col6\" class=\"data row2 col6\" >0.5668</td>\n",
              "      <td id=\"T_1e937_row2_col7\" class=\"data row2 col7\" >0.5715</td>\n",
              "      <td id=\"T_1e937_row2_col8\" class=\"data row2 col8\" >0.3910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1e937_level0_row3\" class=\"row_heading level0 row3\" >lda</th>\n",
              "      <td id=\"T_1e937_row3_col0\" class=\"data row3 col0\" >Linear Discriminant Analysis</td>\n",
              "      <td id=\"T_1e937_row3_col1\" class=\"data row3 col1\" >0.7808</td>\n",
              "      <td id=\"T_1e937_row3_col2\" class=\"data row3 col2\" >0.8735</td>\n",
              "      <td id=\"T_1e937_row3_col3\" class=\"data row3 col3\" >0.6947</td>\n",
              "      <td id=\"T_1e937_row3_col4\" class=\"data row3 col4\" >0.8162</td>\n",
              "      <td id=\"T_1e937_row3_col5\" class=\"data row3 col5\" >0.7427</td>\n",
              "      <td id=\"T_1e937_row3_col6\" class=\"data row3 col6\" >0.5547</td>\n",
              "      <td id=\"T_1e937_row3_col7\" class=\"data row3 col7\" >0.5674</td>\n",
              "      <td id=\"T_1e937_row3_col8\" class=\"data row3 col8\" >0.0570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1e937_level0_row4\" class=\"row_heading level0 row4\" >nb</th>\n",
              "      <td id=\"T_1e937_row4_col0\" class=\"data row4 col0\" >Naive Bayes</td>\n",
              "      <td id=\"T_1e937_row4_col1\" class=\"data row4 col1\" >0.7767</td>\n",
              "      <td id=\"T_1e937_row4_col2\" class=\"data row4 col2\" >0.8651</td>\n",
              "      <td id=\"T_1e937_row4_col3\" class=\"data row4 col3\" >0.7303</td>\n",
              "      <td id=\"T_1e937_row4_col4\" class=\"data row4 col4\" >0.7935</td>\n",
              "      <td id=\"T_1e937_row4_col5\" class=\"data row4 col5\" >0.7524</td>\n",
              "      <td id=\"T_1e937_row4_col6\" class=\"data row4 col6\" >0.5500</td>\n",
              "      <td id=\"T_1e937_row4_col7\" class=\"data row4 col7\" >0.5603</td>\n",
              "      <td id=\"T_1e937_row4_col8\" class=\"data row4 col8\" >0.0520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1e937_level0_row5\" class=\"row_heading level0 row5\" >rf</th>\n",
              "      <td id=\"T_1e937_row5_col0\" class=\"data row5 col0\" >Random Forest Classifier</td>\n",
              "      <td id=\"T_1e937_row5_col1\" class=\"data row5 col1\" >0.7720</td>\n",
              "      <td id=\"T_1e937_row5_col2\" class=\"data row5 col2\" >0.8374</td>\n",
              "      <td id=\"T_1e937_row5_col3\" class=\"data row5 col3\" >0.7023</td>\n",
              "      <td id=\"T_1e937_row5_col4\" class=\"data row5 col4\" >0.7820</td>\n",
              "      <td id=\"T_1e937_row5_col5\" class=\"data row5 col5\" >0.7360</td>\n",
              "      <td id=\"T_1e937_row5_col6\" class=\"data row5 col6\" >0.5371</td>\n",
              "      <td id=\"T_1e937_row5_col7\" class=\"data row5 col7\" >0.5435</td>\n",
              "      <td id=\"T_1e937_row5_col8\" class=\"data row5 col8\" >0.4090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1e937_level0_row6\" class=\"row_heading level0 row6\" >qda</th>\n",
              "      <td id=\"T_1e937_row6_col0\" class=\"data row6 col0\" >Quadratic Discriminant Analysis</td>\n",
              "      <td id=\"T_1e937_row6_col1\" class=\"data row6 col1\" >0.7642</td>\n",
              "      <td id=\"T_1e937_row6_col2\" class=\"data row6 col2\" >0.8493</td>\n",
              "      <td id=\"T_1e937_row6_col3\" class=\"data row6 col3\" >0.7030</td>\n",
              "      <td id=\"T_1e937_row6_col4\" class=\"data row6 col4\" >0.7770</td>\n",
              "      <td id=\"T_1e937_row6_col5\" class=\"data row6 col5\" >0.7330</td>\n",
              "      <td id=\"T_1e937_row6_col6\" class=\"data row6 col6\" >0.5230</td>\n",
              "      <td id=\"T_1e937_row6_col7\" class=\"data row6 col7\" >0.5299</td>\n",
              "      <td id=\"T_1e937_row6_col8\" class=\"data row6 col8\" >0.0570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1e937_level0_row7\" class=\"row_heading level0 row7\" >gbc</th>\n",
              "      <td id=\"T_1e937_row7_col0\" class=\"data row7 col0\" >Gradient Boosting Classifier</td>\n",
              "      <td id=\"T_1e937_row7_col1\" class=\"data row7 col1\" >0.7598</td>\n",
              "      <td id=\"T_1e937_row7_col2\" class=\"data row7 col2\" >0.8306</td>\n",
              "      <td id=\"T_1e937_row7_col3\" class=\"data row7 col3\" >0.7295</td>\n",
              "      <td id=\"T_1e937_row7_col4\" class=\"data row7 col4\" >0.7430</td>\n",
              "      <td id=\"T_1e937_row7_col5\" class=\"data row7 col5\" >0.7338</td>\n",
              "      <td id=\"T_1e937_row7_col6\" class=\"data row7 col6\" >0.5155</td>\n",
              "      <td id=\"T_1e937_row7_col7\" class=\"data row7 col7\" >0.5183</td>\n",
              "      <td id=\"T_1e937_row7_col8\" class=\"data row7 col8\" >0.2730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1e937_level0_row8\" class=\"row_heading level0 row8\" >lightgbm</th>\n",
              "      <td id=\"T_1e937_row8_col0\" class=\"data row8 col0\" >Light Gradient Boosting Machine</td>\n",
              "      <td id=\"T_1e937_row8_col1\" class=\"data row8 col1\" >0.7557</td>\n",
              "      <td id=\"T_1e937_row8_col2\" class=\"data row8 col2\" >0.8322</td>\n",
              "      <td id=\"T_1e937_row8_col3\" class=\"data row8 col3\" >0.6939</td>\n",
              "      <td id=\"T_1e937_row8_col4\" class=\"data row8 col4\" >0.7652</td>\n",
              "      <td id=\"T_1e937_row8_col5\" class=\"data row8 col5\" >0.7224</td>\n",
              "      <td id=\"T_1e937_row8_col6\" class=\"data row8 col6\" >0.5056</td>\n",
              "      <td id=\"T_1e937_row8_col7\" class=\"data row8 col7\" >0.5135</td>\n",
              "      <td id=\"T_1e937_row8_col8\" class=\"data row8 col8\" >0.1660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1e937_level0_row9\" class=\"row_heading level0 row9\" >xgboost</th>\n",
              "      <td id=\"T_1e937_row9_col0\" class=\"data row9 col0\" >Extreme Gradient Boosting</td>\n",
              "      <td id=\"T_1e937_row9_col1\" class=\"data row9 col1\" >0.7475</td>\n",
              "      <td id=\"T_1e937_row9_col2\" class=\"data row9 col2\" >0.8171</td>\n",
              "      <td id=\"T_1e937_row9_col3\" class=\"data row9 col3\" >0.7023</td>\n",
              "      <td id=\"T_1e937_row9_col4\" class=\"data row9 col4\" >0.7447</td>\n",
              "      <td id=\"T_1e937_row9_col5\" class=\"data row9 col5\" >0.7161</td>\n",
              "      <td id=\"T_1e937_row9_col6\" class=\"data row9 col6\" >0.4896</td>\n",
              "      <td id=\"T_1e937_row9_col7\" class=\"data row9 col7\" >0.4973</td>\n",
              "      <td id=\"T_1e937_row9_col8\" class=\"data row9 col8\" >0.0980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1e937_level0_row10\" class=\"row_heading level0 row10\" >ada</th>\n",
              "      <td id=\"T_1e937_row10_col0\" class=\"data row10 col0\" >Ada Boost Classifier</td>\n",
              "      <td id=\"T_1e937_row10_col1\" class=\"data row10 col1\" >0.7148</td>\n",
              "      <td id=\"T_1e937_row10_col2\" class=\"data row10 col2\" >0.7899</td>\n",
              "      <td id=\"T_1e937_row10_col3\" class=\"data row10 col3\" >0.6492</td>\n",
              "      <td id=\"T_1e937_row10_col4\" class=\"data row10 col4\" >0.7177</td>\n",
              "      <td id=\"T_1e937_row10_col5\" class=\"data row10 col5\" >0.6737</td>\n",
              "      <td id=\"T_1e937_row10_col6\" class=\"data row10 col6\" >0.4226</td>\n",
              "      <td id=\"T_1e937_row10_col7\" class=\"data row10 col7\" >0.4313</td>\n",
              "      <td id=\"T_1e937_row10_col8\" class=\"data row10 col8\" >0.4110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1e937_level0_row11\" class=\"row_heading level0 row11\" >dt</th>\n",
              "      <td id=\"T_1e937_row11_col0\" class=\"data row11 col0\" >Decision Tree Classifier</td>\n",
              "      <td id=\"T_1e937_row11_col1\" class=\"data row11 col1\" >0.6980</td>\n",
              "      <td id=\"T_1e937_row11_col2\" class=\"data row11 col2\" >0.6947</td>\n",
              "      <td id=\"T_1e937_row11_col3\" class=\"data row11 col3\" >0.6492</td>\n",
              "      <td id=\"T_1e937_row11_col4\" class=\"data row11 col4\" >0.6860</td>\n",
              "      <td id=\"T_1e937_row11_col5\" class=\"data row11 col5\" >0.6632</td>\n",
              "      <td id=\"T_1e937_row11_col6\" class=\"data row11 col6\" >0.3905</td>\n",
              "      <td id=\"T_1e937_row11_col7\" class=\"data row11 col7\" >0.3944</td>\n",
              "      <td id=\"T_1e937_row11_col8\" class=\"data row11 col8\" >0.0520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1e937_level0_row12\" class=\"row_heading level0 row12\" >knn</th>\n",
              "      <td id=\"T_1e937_row12_col0\" class=\"data row12 col0\" >K Neighbors Classifier</td>\n",
              "      <td id=\"T_1e937_row12_col1\" class=\"data row12 col1\" >0.6198</td>\n",
              "      <td id=\"T_1e937_row12_col2\" class=\"data row12 col2\" >0.6611</td>\n",
              "      <td id=\"T_1e937_row12_col3\" class=\"data row12 col3\" >0.5053</td>\n",
              "      <td id=\"T_1e937_row12_col4\" class=\"data row12 col4\" >0.6138</td>\n",
              "      <td id=\"T_1e937_row12_col5\" class=\"data row12 col5\" >0.5446</td>\n",
              "      <td id=\"T_1e937_row12_col6\" class=\"data row12 col6\" >0.2266</td>\n",
              "      <td id=\"T_1e937_row12_col7\" class=\"data row12 col7\" >0.2344</td>\n",
              "      <td id=\"T_1e937_row12_col8\" class=\"data row12 col8\" >0.0550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1e937_level0_row13\" class=\"row_heading level0 row13\" >svm</th>\n",
              "      <td id=\"T_1e937_row13_col0\" class=\"data row13 col0\" >SVM - Linear Kernel</td>\n",
              "      <td id=\"T_1e937_row13_col1\" class=\"data row13 col1\" >0.5860</td>\n",
              "      <td id=\"T_1e937_row13_col2\" class=\"data row13 col2\" >0.0000</td>\n",
              "      <td id=\"T_1e937_row13_col3\" class=\"data row13 col3\" >0.6917</td>\n",
              "      <td id=\"T_1e937_row13_col4\" class=\"data row13 col4\" >0.4445</td>\n",
              "      <td id=\"T_1e937_row13_col5\" class=\"data row13 col5\" >0.5088</td>\n",
              "      <td id=\"T_1e937_row13_col6\" class=\"data row13 col6\" >0.1795</td>\n",
              "      <td id=\"T_1e937_row13_col7\" class=\"data row13 col7\" >0.2024</td>\n",
              "      <td id=\"T_1e937_row13_col8\" class=\"data row13 col8\" >0.0390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_1e937_level0_row14\" class=\"row_heading level0 row14\" >dummy</th>\n",
              "      <td id=\"T_1e937_row14_col0\" class=\"data row14 col0\" >Dummy Classifier</td>\n",
              "      <td id=\"T_1e937_row14_col1\" class=\"data row14 col1\" >0.5413</td>\n",
              "      <td id=\"T_1e937_row14_col2\" class=\"data row14 col2\" >0.5000</td>\n",
              "      <td id=\"T_1e937_row14_col3\" class=\"data row14 col3\" >0.0000</td>\n",
              "      <td id=\"T_1e937_row14_col4\" class=\"data row14 col4\" >0.0000</td>\n",
              "      <td id=\"T_1e937_row14_col5\" class=\"data row14 col5\" >0.0000</td>\n",
              "      <td id=\"T_1e937_row14_col6\" class=\"data row14 col6\" >0.0000</td>\n",
              "      <td id=\"T_1e937_row14_col7\" class=\"data row14 col7\" >0.0000</td>\n",
              "      <td id=\"T_1e937_row14_col8\" class=\"data row14 col8\" >0.0990</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/65 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1e03f11a05144ede9937cfdf8b49ef47"
            }
          },
          "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": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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=42, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ],
            "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>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=42, 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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" 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=42, solver=&#x27;lbfgs&#x27;, tol=0.0001, verbose=0,\n",
              "                   warm_start=False)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create model\n",
        "lr = create_model('lr')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457,
          "referenced_widgets": [
            "1d3d9919dfa548afa57cdc9c80a04807",
            "4936ff36fb39472ab279e8d62f9c53aa",
            "31e4bdc402af4c3f829d21d745795b63",
            "1defc2aa2f9b4e47863a8b0e622df0a9",
            "f5f32e9aec0d45b1a6f4c4343d37409b",
            "7ead8429f9954e8189eeceb4779243ae",
            "bcbf046adaf54a8896b8c26e0b73ef46",
            "5c6ed7a2b2084fc9a8e0076340313175",
            "0c058deb61674a4cb19fb66417c98797",
            "378cb10559414dab9085874d14de0e17",
            "b59d4f64088443179809c3ad7b15cd8d"
          ]
        },
        "id": "FNinMF7mfiWp",
        "outputId": "5f6a79a4-d2fb-47ec-ef16-7ac80649a25d"
      },
      "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 0x7f59aa7a2a90>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_e8131_row10_col0, #T_e8131_row10_col1, #T_e8131_row10_col2, #T_e8131_row10_col3, #T_e8131_row10_col4, #T_e8131_row10_col5, #T_e8131_row10_col6 {\n",
              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_e8131\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_e8131_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
              "      <th id=\"T_e8131_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
              "      <th id=\"T_e8131_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
              "      <th id=\"T_e8131_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
              "      <th id=\"T_e8131_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
              "      <th id=\"T_e8131_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
              "      <th id=\"T_e8131_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_e8131_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_e8131_row0_col0\" class=\"data row0 col0\" >0.7600</td>\n",
              "      <td id=\"T_e8131_row0_col1\" class=\"data row0 col1\" >0.8654</td>\n",
              "      <td id=\"T_e8131_row0_col2\" class=\"data row0 col2\" >0.5833</td>\n",
              "      <td id=\"T_e8131_row0_col3\" class=\"data row0 col3\" >0.8750</td>\n",
              "      <td id=\"T_e8131_row0_col4\" class=\"data row0 col4\" >0.7000</td>\n",
              "      <td id=\"T_e8131_row0_col5\" class=\"data row0 col5\" >0.5130</td>\n",
              "      <td id=\"T_e8131_row0_col6\" class=\"data row0 col6\" >0.5424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e8131_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_e8131_row1_col0\" class=\"data row1 col0\" >0.8400</td>\n",
              "      <td id=\"T_e8131_row1_col1\" class=\"data row1 col1\" >0.9286</td>\n",
              "      <td id=\"T_e8131_row1_col2\" class=\"data row1 col2\" >0.8182</td>\n",
              "      <td id=\"T_e8131_row1_col3\" class=\"data row1 col3\" >0.8182</td>\n",
              "      <td id=\"T_e8131_row1_col4\" class=\"data row1 col4\" >0.8182</td>\n",
              "      <td id=\"T_e8131_row1_col5\" class=\"data row1 col5\" >0.6753</td>\n",
              "      <td id=\"T_e8131_row1_col6\" class=\"data row1 col6\" >0.6753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e8131_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_e8131_row2_col0\" class=\"data row2 col0\" >0.6667</td>\n",
              "      <td id=\"T_e8131_row2_col1\" class=\"data row2 col1\" >0.6923</td>\n",
              "      <td id=\"T_e8131_row2_col2\" class=\"data row2 col2\" >0.6364</td>\n",
              "      <td id=\"T_e8131_row2_col3\" class=\"data row2 col3\" >0.6364</td>\n",
              "      <td id=\"T_e8131_row2_col4\" class=\"data row2 col4\" >0.6364</td>\n",
              "      <td id=\"T_e8131_row2_col5\" class=\"data row2 col5\" >0.3287</td>\n",
              "      <td id=\"T_e8131_row2_col6\" class=\"data row2 col6\" >0.3287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e8131_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_e8131_row3_col0\" class=\"data row3 col0\" >0.8750</td>\n",
              "      <td id=\"T_e8131_row3_col1\" class=\"data row3 col1\" >0.8531</td>\n",
              "      <td id=\"T_e8131_row3_col2\" class=\"data row3 col2\" >0.8182</td>\n",
              "      <td id=\"T_e8131_row3_col3\" class=\"data row3 col3\" >0.9000</td>\n",
              "      <td id=\"T_e8131_row3_col4\" class=\"data row3 col4\" >0.8571</td>\n",
              "      <td id=\"T_e8131_row3_col5\" class=\"data row3 col5\" >0.7465</td>\n",
              "      <td id=\"T_e8131_row3_col6\" class=\"data row3 col6\" >0.7492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e8131_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_e8131_row4_col0\" class=\"data row4 col0\" >0.8750</td>\n",
              "      <td id=\"T_e8131_row4_col1\" class=\"data row4 col1\" >0.9580</td>\n",
              "      <td id=\"T_e8131_row4_col2\" class=\"data row4 col2\" >0.8182</td>\n",
              "      <td id=\"T_e8131_row4_col3\" class=\"data row4 col3\" >0.9000</td>\n",
              "      <td id=\"T_e8131_row4_col4\" class=\"data row4 col4\" >0.8571</td>\n",
              "      <td id=\"T_e8131_row4_col5\" class=\"data row4 col5\" >0.7465</td>\n",
              "      <td id=\"T_e8131_row4_col6\" class=\"data row4 col6\" >0.7492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e8131_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_e8131_row5_col0\" class=\"data row5 col0\" >0.7500</td>\n",
              "      <td id=\"T_e8131_row5_col1\" class=\"data row5 col1\" >0.8601</td>\n",
              "      <td id=\"T_e8131_row5_col2\" class=\"data row5 col2\" >0.7273</td>\n",
              "      <td id=\"T_e8131_row5_col3\" class=\"data row5 col3\" >0.7273</td>\n",
              "      <td id=\"T_e8131_row5_col4\" class=\"data row5 col4\" >0.7273</td>\n",
              "      <td id=\"T_e8131_row5_col5\" class=\"data row5 col5\" >0.4965</td>\n",
              "      <td id=\"T_e8131_row5_col6\" class=\"data row5 col6\" >0.4965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e8131_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_e8131_row6_col0\" class=\"data row6 col0\" >0.7917</td>\n",
              "      <td id=\"T_e8131_row6_col1\" class=\"data row6 col1\" >0.9720</td>\n",
              "      <td id=\"T_e8131_row6_col2\" class=\"data row6 col2\" >0.5455</td>\n",
              "      <td id=\"T_e8131_row6_col3\" class=\"data row6 col3\" >1.0000</td>\n",
              "      <td id=\"T_e8131_row6_col4\" class=\"data row6 col4\" >0.7059</td>\n",
              "      <td id=\"T_e8131_row6_col5\" class=\"data row6 col5\" >0.5652</td>\n",
              "      <td id=\"T_e8131_row6_col6\" class=\"data row6 col6\" >0.6276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e8131_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_e8131_row7_col0\" class=\"data row7 col0\" >0.8333</td>\n",
              "      <td id=\"T_e8131_row7_col1\" class=\"data row7 col1\" >0.8671</td>\n",
              "      <td id=\"T_e8131_row7_col2\" class=\"data row7 col2\" >0.9091</td>\n",
              "      <td id=\"T_e8131_row7_col3\" class=\"data row7 col3\" >0.7692</td>\n",
              "      <td id=\"T_e8131_row7_col4\" class=\"data row7 col4\" >0.8333</td>\n",
              "      <td id=\"T_e8131_row7_col5\" class=\"data row7 col5\" >0.6690</td>\n",
              "      <td id=\"T_e8131_row7_col6\" class=\"data row7 col6\" >0.6783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e8131_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_e8131_row8_col0\" class=\"data row8 col0\" >0.8333</td>\n",
              "      <td id=\"T_e8131_row8_col1\" class=\"data row8 col1\" >0.8811</td>\n",
              "      <td id=\"T_e8131_row8_col2\" class=\"data row8 col2\" >0.8182</td>\n",
              "      <td id=\"T_e8131_row8_col3\" class=\"data row8 col3\" >0.8182</td>\n",
              "      <td id=\"T_e8131_row8_col4\" class=\"data row8 col4\" >0.8182</td>\n",
              "      <td id=\"T_e8131_row8_col5\" class=\"data row8 col5\" >0.6643</td>\n",
              "      <td id=\"T_e8131_row8_col6\" class=\"data row8 col6\" >0.6643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e8131_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_e8131_row9_col0\" class=\"data row9 col0\" >0.7500</td>\n",
              "      <td id=\"T_e8131_row9_col1\" class=\"data row9 col1\" >0.8322</td>\n",
              "      <td id=\"T_e8131_row9_col2\" class=\"data row9 col2\" >0.7273</td>\n",
              "      <td id=\"T_e8131_row9_col3\" class=\"data row9 col3\" >0.7273</td>\n",
              "      <td id=\"T_e8131_row9_col4\" class=\"data row9 col4\" >0.7273</td>\n",
              "      <td id=\"T_e8131_row9_col5\" class=\"data row9 col5\" >0.4965</td>\n",
              "      <td id=\"T_e8131_row9_col6\" class=\"data row9 col6\" >0.4965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e8131_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
              "      <td id=\"T_e8131_row10_col0\" class=\"data row10 col0\" >0.7975</td>\n",
              "      <td id=\"T_e8131_row10_col1\" class=\"data row10 col1\" >0.8710</td>\n",
              "      <td id=\"T_e8131_row10_col2\" class=\"data row10 col2\" >0.7402</td>\n",
              "      <td id=\"T_e8131_row10_col3\" class=\"data row10 col3\" >0.8172</td>\n",
              "      <td id=\"T_e8131_row10_col4\" class=\"data row10 col4\" >0.7681</td>\n",
              "      <td id=\"T_e8131_row10_col5\" class=\"data row10 col5\" >0.5901</td>\n",
              "      <td id=\"T_e8131_row10_col6\" class=\"data row10 col6\" >0.6008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e8131_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
              "      <td id=\"T_e8131_row11_col0\" class=\"data row11 col0\" >0.0628</td>\n",
              "      <td id=\"T_e8131_row11_col1\" class=\"data row11 col1\" >0.0742</td>\n",
              "      <td id=\"T_e8131_row11_col2\" class=\"data row11 col2\" >0.1125</td>\n",
              "      <td id=\"T_e8131_row11_col3\" class=\"data row11 col3\" >0.1008</td>\n",
              "      <td id=\"T_e8131_row11_col4\" class=\"data row11 col4\" >0.0737</td>\n",
              "      <td id=\"T_e8131_row11_col5\" class=\"data row11 col5\" >0.1266</td>\n",
              "      <td id=\"T_e8131_row11_col6\" class=\"data row11 col6\" >0.1263</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": "1d3d9919dfa548afa57cdc9c80a04807"
            }
          },
          "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": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# tune model\n",
        "tuned_lr = tune_model(lr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474,
          "referenced_widgets": [
            "fb228bbf78e549b996822af3f8527017",
            "b685761a7e18483b812b1361dd2fdd06",
            "c6a2a92a919b4008885bece55ae21eb4",
            "b0ab5b0500464711b5d781c86a2aaf61",
            "c645e50d69cd48ae8b065b9c84f2aad4",
            "2d70304307ba487091edaf8ac253dc9c",
            "361198c4c6ed47709644b7ee40ca68b5",
            "0ddd9a65d31445acb410871e8f72f7df",
            "786f5b51651949ff84d2b6e26cfaabb4",
            "9129a92b63ee4621a6935a12c74979dc",
            "6c37138667014578b8f08cf3f65790e6"
          ]
        },
        "id": "5xZIPqH7fkww",
        "outputId": "3eed9ad1-cae8-4df3-b815-5b7a3adfd090"
      },
      "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 0x7f5998bccc70>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_90433_row10_col0, #T_90433_row10_col1, #T_90433_row10_col2, #T_90433_row10_col3, #T_90433_row10_col4, #T_90433_row10_col5, #T_90433_row10_col6 {\n",
              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_90433\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_90433_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
              "      <th id=\"T_90433_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
              "      <th id=\"T_90433_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
              "      <th id=\"T_90433_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
              "      <th id=\"T_90433_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
              "      <th id=\"T_90433_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
              "      <th id=\"T_90433_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_90433_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_90433_row0_col0\" class=\"data row0 col0\" >0.8400</td>\n",
              "      <td id=\"T_90433_row0_col1\" class=\"data row0 col1\" >0.8526</td>\n",
              "      <td id=\"T_90433_row0_col2\" class=\"data row0 col2\" >0.7500</td>\n",
              "      <td id=\"T_90433_row0_col3\" class=\"data row0 col3\" >0.9000</td>\n",
              "      <td id=\"T_90433_row0_col4\" class=\"data row0 col4\" >0.8182</td>\n",
              "      <td id=\"T_90433_row0_col5\" class=\"data row0 col5\" >0.6774</td>\n",
              "      <td id=\"T_90433_row0_col6\" class=\"data row0 col6\" >0.6864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_90433_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_90433_row1_col0\" class=\"data row1 col0\" >0.8400</td>\n",
              "      <td id=\"T_90433_row1_col1\" class=\"data row1 col1\" >0.9351</td>\n",
              "      <td id=\"T_90433_row1_col2\" class=\"data row1 col2\" >0.8182</td>\n",
              "      <td id=\"T_90433_row1_col3\" class=\"data row1 col3\" >0.8182</td>\n",
              "      <td id=\"T_90433_row1_col4\" class=\"data row1 col4\" >0.8182</td>\n",
              "      <td id=\"T_90433_row1_col5\" class=\"data row1 col5\" >0.6753</td>\n",
              "      <td id=\"T_90433_row1_col6\" class=\"data row1 col6\" >0.6753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_90433_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_90433_row2_col0\" class=\"data row2 col0\" >0.6250</td>\n",
              "      <td id=\"T_90433_row2_col1\" class=\"data row2 col1\" >0.6993</td>\n",
              "      <td id=\"T_90433_row2_col2\" class=\"data row2 col2\" >0.6364</td>\n",
              "      <td id=\"T_90433_row2_col3\" class=\"data row2 col3\" >0.5833</td>\n",
              "      <td id=\"T_90433_row2_col4\" class=\"data row2 col4\" >0.6087</td>\n",
              "      <td id=\"T_90433_row2_col5\" class=\"data row2 col5\" >0.2500</td>\n",
              "      <td id=\"T_90433_row2_col6\" class=\"data row2 col6\" >0.2509</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_90433_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_90433_row3_col0\" class=\"data row3 col0\" >0.8750</td>\n",
              "      <td id=\"T_90433_row3_col1\" class=\"data row3 col1\" >0.8462</td>\n",
              "      <td id=\"T_90433_row3_col2\" class=\"data row3 col2\" >0.8182</td>\n",
              "      <td id=\"T_90433_row3_col3\" class=\"data row3 col3\" >0.9000</td>\n",
              "      <td id=\"T_90433_row3_col4\" class=\"data row3 col4\" >0.8571</td>\n",
              "      <td id=\"T_90433_row3_col5\" class=\"data row3 col5\" >0.7465</td>\n",
              "      <td id=\"T_90433_row3_col6\" class=\"data row3 col6\" >0.7492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_90433_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_90433_row4_col0\" class=\"data row4 col0\" >0.8750</td>\n",
              "      <td id=\"T_90433_row4_col1\" class=\"data row4 col1\" >0.9441</td>\n",
              "      <td id=\"T_90433_row4_col2\" class=\"data row4 col2\" >0.8182</td>\n",
              "      <td id=\"T_90433_row4_col3\" class=\"data row4 col3\" >0.9000</td>\n",
              "      <td id=\"T_90433_row4_col4\" class=\"data row4 col4\" >0.8571</td>\n",
              "      <td id=\"T_90433_row4_col5\" class=\"data row4 col5\" >0.7465</td>\n",
              "      <td id=\"T_90433_row4_col6\" class=\"data row4 col6\" >0.7492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_90433_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_90433_row5_col0\" class=\"data row5 col0\" >0.7500</td>\n",
              "      <td id=\"T_90433_row5_col1\" class=\"data row5 col1\" >0.8671</td>\n",
              "      <td id=\"T_90433_row5_col2\" class=\"data row5 col2\" >0.7273</td>\n",
              "      <td id=\"T_90433_row5_col3\" class=\"data row5 col3\" >0.7273</td>\n",
              "      <td id=\"T_90433_row5_col4\" class=\"data row5 col4\" >0.7273</td>\n",
              "      <td id=\"T_90433_row5_col5\" class=\"data row5 col5\" >0.4965</td>\n",
              "      <td id=\"T_90433_row5_col6\" class=\"data row5 col6\" >0.4965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_90433_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_90433_row6_col0\" class=\"data row6 col0\" >0.8333</td>\n",
              "      <td id=\"T_90433_row6_col1\" class=\"data row6 col1\" >0.9790</td>\n",
              "      <td id=\"T_90433_row6_col2\" class=\"data row6 col2\" >0.6364</td>\n",
              "      <td id=\"T_90433_row6_col3\" class=\"data row6 col3\" >1.0000</td>\n",
              "      <td id=\"T_90433_row6_col4\" class=\"data row6 col4\" >0.7778</td>\n",
              "      <td id=\"T_90433_row6_col5\" class=\"data row6 col5\" >0.6547</td>\n",
              "      <td id=\"T_90433_row6_col6\" class=\"data row6 col6\" >0.6976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_90433_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_90433_row7_col0\" class=\"data row7 col0\" >0.8333</td>\n",
              "      <td id=\"T_90433_row7_col1\" class=\"data row7 col1\" >0.8741</td>\n",
              "      <td id=\"T_90433_row7_col2\" class=\"data row7 col2\" >0.9091</td>\n",
              "      <td id=\"T_90433_row7_col3\" class=\"data row7 col3\" >0.7692</td>\n",
              "      <td id=\"T_90433_row7_col4\" class=\"data row7 col4\" >0.8333</td>\n",
              "      <td id=\"T_90433_row7_col5\" class=\"data row7 col5\" >0.6690</td>\n",
              "      <td id=\"T_90433_row7_col6\" class=\"data row7 col6\" >0.6783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_90433_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_90433_row8_col0\" class=\"data row8 col0\" >0.8333</td>\n",
              "      <td id=\"T_90433_row8_col1\" class=\"data row8 col1\" >0.8741</td>\n",
              "      <td id=\"T_90433_row8_col2\" class=\"data row8 col2\" >0.8182</td>\n",
              "      <td id=\"T_90433_row8_col3\" class=\"data row8 col3\" >0.8182</td>\n",
              "      <td id=\"T_90433_row8_col4\" class=\"data row8 col4\" >0.8182</td>\n",
              "      <td id=\"T_90433_row8_col5\" class=\"data row8 col5\" >0.6643</td>\n",
              "      <td id=\"T_90433_row8_col6\" class=\"data row8 col6\" >0.6643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_90433_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_90433_row9_col0\" class=\"data row9 col0\" >0.7917</td>\n",
              "      <td id=\"T_90433_row9_col1\" class=\"data row9 col1\" >0.8252</td>\n",
              "      <td id=\"T_90433_row9_col2\" class=\"data row9 col2\" >0.8182</td>\n",
              "      <td id=\"T_90433_row9_col3\" class=\"data row9 col3\" >0.7500</td>\n",
              "      <td id=\"T_90433_row9_col4\" class=\"data row9 col4\" >0.7826</td>\n",
              "      <td id=\"T_90433_row9_col5\" class=\"data row9 col5\" >0.5833</td>\n",
              "      <td id=\"T_90433_row9_col6\" class=\"data row9 col6\" >0.5854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_90433_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
              "      <td id=\"T_90433_row10_col0\" class=\"data row10 col0\" >0.8097</td>\n",
              "      <td id=\"T_90433_row10_col1\" class=\"data row10 col1\" >0.8697</td>\n",
              "      <td id=\"T_90433_row10_col2\" class=\"data row10 col2\" >0.7750</td>\n",
              "      <td id=\"T_90433_row10_col3\" class=\"data row10 col3\" >0.8166</td>\n",
              "      <td id=\"T_90433_row10_col4\" class=\"data row10 col4\" >0.7899</td>\n",
              "      <td id=\"T_90433_row10_col5\" class=\"data row10 col5\" >0.6164</td>\n",
              "      <td id=\"T_90433_row10_col6\" class=\"data row10 col6\" >0.6233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_90433_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
              "      <td id=\"T_90433_row11_col0\" class=\"data row11 col0\" >0.0707</td>\n",
              "      <td id=\"T_90433_row11_col1\" class=\"data row11 col1\" >0.0733</td>\n",
              "      <td id=\"T_90433_row11_col2\" class=\"data row11 col2\" >0.0829</td>\n",
              "      <td id=\"T_90433_row11_col3\" class=\"data row11 col3\" >0.1112</td>\n",
              "      <td id=\"T_90433_row11_col4\" class=\"data row11 col4\" >0.0709</td>\n",
              "      <td id=\"T_90433_row11_col5\" class=\"data row11 col5\" >0.1405</td>\n",
              "      <td id=\"T_90433_row11_col6\" class=\"data row11 col6\" >0.1431</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/7 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fb228bbf78e549b996822af3f8527017"
            }
          },
          "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": "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>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict_model(tuned_lr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "JmQ4bbHYfnWd",
        "outputId": "b5d64c06-7dc9-45be-e03d-bbf35e27a018"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f5998172220>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_c1b3f\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_c1b3f_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_c1b3f_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th id=\"T_c1b3f_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
              "      <th id=\"T_c1b3f_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
              "      <th id=\"T_c1b3f_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
              "      <th id=\"T_c1b3f_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
              "      <th id=\"T_c1b3f_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th id=\"T_c1b3f_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_c1b3f_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_c1b3f_row0_col0\" class=\"data row0 col0\" >Logistic Regression</td>\n",
              "      <td id=\"T_c1b3f_row0_col1\" class=\"data row0 col1\" >0.8525</td>\n",
              "      <td id=\"T_c1b3f_row0_col2\" class=\"data row0 col2\" >0.9383</td>\n",
              "      <td id=\"T_c1b3f_row0_col3\" class=\"data row0 col3\" >0.8929</td>\n",
              "      <td id=\"T_c1b3f_row0_col4\" class=\"data row0 col4\" >0.8065</td>\n",
              "      <td id=\"T_c1b3f_row0_col5\" class=\"data row0 col5\" >0.8475</td>\n",
              "      <td id=\"T_c1b3f_row0_col6\" class=\"data row0 col6\" >0.7053</td>\n",
              "      <td id=\"T_c1b3f_row0_col7\" class=\"data row0 col7\" >0.7087</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
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              "      age  sex  trestbps   chol  thalach  exang  oldpeak   ca  num  \\\n",
              "219  59.0  1.0     138.0  271.0    182.0    0.0      0.0  0.0  0.0   \n",
              "271  66.0  1.0     160.0  228.0    138.0    0.0      2.3  0.0  0.0   \n",
              "89   51.0  0.0     130.0  256.0    149.0    0.0      0.5  0.0  0.0   \n",
              "101  34.0  1.0     118.0  182.0    174.0    0.0      0.0  0.0  0.0   \n",
              "67   54.0  1.0     150.0  232.0    165.0    0.0      1.6  0.0  0.0   \n",
              "..    ...  ...       ...    ...      ...    ...      ...  ...  ...   \n",
              "285  58.0  1.0     114.0  318.0    140.0    0.0      4.4  3.0  1.0   \n",
              "243  61.0  1.0     134.0  234.0    145.0    0.0      2.6  2.0  1.0   \n",
              "94   63.0  0.0     135.0  252.0    172.0    0.0      0.0  0.0  0.0   \n",
              "291  55.0  0.0     132.0  342.0    166.0    0.0      1.2  0.0  0.0   \n",
              "155  70.0  1.0     130.0  322.0    109.0    0.0      2.4  3.0  1.0   \n",
              "\n",
              "     prediction_label  prediction_score  \n",
              "219                 0            0.8540  \n",
              "271                 1            0.5821  \n",
              "89                  0            0.9039  \n",
              "101                 0            0.8577  \n",
              "67                  0            0.6569  \n",
              "..                ...               ...  \n",
              "285                 1            0.9892  \n",
              "243                 1            0.9148  \n",
              "94                  0            0.9655  \n",
              "291                 0            0.8965  \n",
              "155                 1            0.9929  \n",
              "\n",
              "[61 rows x 11 columns]"
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            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## BINARY MODEL & Uncorreleated (do not execute to keep printed..)"
      ],
      "metadata": {
        "id": "M8Jxh9TmH6ZX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# set up\n",
        "cleveland_pc_setup = setup(data = cleveland_strat_train, test_data = cleveland_strat_test, target = 'num', session_id=42)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "6s4gkRVTmmth",
        "outputId": "1ea7c50c-6e2a-42e3-e3e0-8732003d709b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fb46accddf0>"
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            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_a1ed8_row8_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_a1ed8\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_a1ed8_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_a1ed8_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ed8_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_a1ed8_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_a1ed8_row0_col1\" class=\"data row0 col1\" >42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ed8_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_a1ed8_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
              "      <td id=\"T_a1ed8_row1_col1\" class=\"data row1 col1\" >num</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ed8_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_a1ed8_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
              "      <td id=\"T_a1ed8_row2_col1\" class=\"data row2 col1\" >Binary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ed8_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_a1ed8_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
              "      <td id=\"T_a1ed8_row3_col1\" class=\"data row3 col1\" >(303, 14)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ed8_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_a1ed8_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_a1ed8_row4_col1\" class=\"data row4 col1\" >(303, 14)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ed8_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_a1ed8_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
              "      <td id=\"T_a1ed8_row5_col1\" class=\"data row5 col1\" >(242, 14)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ed8_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_a1ed8_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
              "      <td id=\"T_a1ed8_row6_col1\" class=\"data row6 col1\" >(61, 14)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ed8_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_a1ed8_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
              "      <td id=\"T_a1ed8_row7_col1\" class=\"data row7 col1\" >13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ed8_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_a1ed8_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
              "      <td id=\"T_a1ed8_row8_col1\" class=\"data row8 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ed8_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_a1ed8_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
              "      <td id=\"T_a1ed8_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ed8_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_a1ed8_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_a1ed8_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ed8_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_a1ed8_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_a1ed8_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ed8_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_a1ed8_row12_col0\" class=\"data row12 col0\" >Fold Generator</td>\n",
              "      <td id=\"T_a1ed8_row12_col1\" class=\"data row12 col1\" >StratifiedKFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ed8_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_a1ed8_row13_col0\" class=\"data row13 col0\" >Fold Number</td>\n",
              "      <td id=\"T_a1ed8_row13_col1\" class=\"data row13 col1\" >10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ed8_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_a1ed8_row14_col0\" class=\"data row14 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_a1ed8_row14_col1\" class=\"data row14 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ed8_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_a1ed8_row15_col0\" class=\"data row15 col0\" >Use GPU</td>\n",
              "      <td id=\"T_a1ed8_row15_col1\" class=\"data row15 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ed8_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_a1ed8_row16_col0\" class=\"data row16 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_a1ed8_row16_col1\" class=\"data row16 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ed8_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_a1ed8_row17_col0\" class=\"data row17 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_a1ed8_row17_col1\" class=\"data row17 col1\" >clf-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ed8_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "      <td id=\"T_a1ed8_row18_col0\" class=\"data row18 col0\" >USI</td>\n",
              "      <td id=\"T_a1ed8_row18_col1\" class=\"data row18 col1\" >8b13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# compare models\n",
        "compare_models()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 612,
          "referenced_widgets": [
            "e28d206f1ea1418996cee58638276083",
            "c04be690adbc497099e592c55740a3b7",
            "4898e5af1e7143a18d53f1129760e1d0",
            "c489cf7131a64375942140c6eb288f0a",
            "b494c45a611b405498590cb59cdfba6e",
            "c57add4f2fc1492ebd0c714393f393e6",
            "d4a5ad62b2194fdf96a047396e963f6e",
            "b863d156717147e59a11a56ac518f2bf",
            "56d74e55a8cb42348197622410bf5a6d",
            "be9d76edafb64abaa04718c832ff9453",
            "d8d0a268b12045c1835058c93415f08d"
          ]
        },
        "id": "73slbChxmmo_",
        "outputId": "ee41a14c-3059-414e-efbf-9a53ea34d55c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
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          "metadata": {}
        },
        {
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          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fb4886fdbe0>"
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              "#T_50bdb th {\n",
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              "#T_50bdb_row0_col0, #T_50bdb_row0_col2, #T_50bdb_row0_col3, #T_50bdb_row1_col0, #T_50bdb_row1_col1, #T_50bdb_row1_col2, #T_50bdb_row1_col3, #T_50bdb_row1_col4, #T_50bdb_row1_col5, #T_50bdb_row1_col6, #T_50bdb_row1_col7, #T_50bdb_row2_col0, #T_50bdb_row2_col1, #T_50bdb_row2_col3, #T_50bdb_row2_col4, #T_50bdb_row2_col5, #T_50bdb_row2_col6, #T_50bdb_row2_col7, #T_50bdb_row3_col0, #T_50bdb_row3_col1, #T_50bdb_row3_col2, #T_50bdb_row3_col4, #T_50bdb_row3_col5, #T_50bdb_row3_col6, #T_50bdb_row3_col7, #T_50bdb_row4_col0, #T_50bdb_row4_col1, #T_50bdb_row4_col2, #T_50bdb_row4_col3, #T_50bdb_row4_col4, #T_50bdb_row4_col5, #T_50bdb_row4_col6, #T_50bdb_row4_col7, #T_50bdb_row5_col0, #T_50bdb_row5_col1, #T_50bdb_row5_col2, #T_50bdb_row5_col3, #T_50bdb_row5_col4, #T_50bdb_row5_col5, #T_50bdb_row5_col6, #T_50bdb_row5_col7, #T_50bdb_row6_col0, #T_50bdb_row6_col1, #T_50bdb_row6_col2, #T_50bdb_row6_col3, #T_50bdb_row6_col4, #T_50bdb_row6_col5, #T_50bdb_row6_col6, #T_50bdb_row6_col7, #T_50bdb_row7_col0, #T_50bdb_row7_col1, #T_50bdb_row7_col2, #T_50bdb_row7_col3, #T_50bdb_row7_col4, #T_50bdb_row7_col5, #T_50bdb_row7_col6, #T_50bdb_row7_col7, #T_50bdb_row8_col0, #T_50bdb_row8_col1, #T_50bdb_row8_col2, #T_50bdb_row8_col3, #T_50bdb_row8_col4, #T_50bdb_row8_col5, #T_50bdb_row8_col6, #T_50bdb_row8_col7, #T_50bdb_row9_col0, #T_50bdb_row9_col1, #T_50bdb_row9_col2, #T_50bdb_row9_col3, #T_50bdb_row9_col4, #T_50bdb_row9_col5, #T_50bdb_row9_col6, #T_50bdb_row9_col7, #T_50bdb_row10_col0, #T_50bdb_row10_col1, #T_50bdb_row10_col2, #T_50bdb_row10_col3, #T_50bdb_row10_col4, #T_50bdb_row10_col5, #T_50bdb_row10_col6, #T_50bdb_row10_col7, #T_50bdb_row11_col0, #T_50bdb_row11_col1, #T_50bdb_row11_col2, #T_50bdb_row11_col3, #T_50bdb_row11_col4, #T_50bdb_row11_col5, #T_50bdb_row11_col6, #T_50bdb_row11_col7, #T_50bdb_row12_col0, #T_50bdb_row12_col1, #T_50bdb_row12_col2, #T_50bdb_row12_col3, #T_50bdb_row12_col4, #T_50bdb_row12_col5, #T_50bdb_row12_col6, #T_50bdb_row12_col7, #T_50bdb_row13_col0, #T_50bdb_row13_col1, #T_50bdb_row13_col2, #T_50bdb_row13_col3, #T_50bdb_row13_col4, #T_50bdb_row13_col5, #T_50bdb_row13_col6, #T_50bdb_row13_col7, #T_50bdb_row14_col0, #T_50bdb_row14_col1, #T_50bdb_row14_col2, #T_50bdb_row14_col3, #T_50bdb_row14_col4, #T_50bdb_row14_col5, #T_50bdb_row14_col6, #T_50bdb_row14_col7 {\n",
              "  text-align: left;\n",
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              "#T_50bdb_row0_col1, #T_50bdb_row0_col4, #T_50bdb_row0_col5, #T_50bdb_row0_col6, #T_50bdb_row0_col7, #T_50bdb_row2_col2, #T_50bdb_row3_col3 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "}\n",
              "#T_50bdb_row0_col8, #T_50bdb_row1_col8, #T_50bdb_row2_col8, #T_50bdb_row3_col8, #T_50bdb_row4_col8, #T_50bdb_row5_col8, #T_50bdb_row6_col8, #T_50bdb_row7_col8, #T_50bdb_row8_col8, #T_50bdb_row9_col8, #T_50bdb_row10_col8, #T_50bdb_row11_col8, #T_50bdb_row13_col8, #T_50bdb_row14_col8 {\n",
              "  text-align: left;\n",
              "  background-color: lightgrey;\n",
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              "#T_50bdb_row12_col8 {\n",
              "  text-align: left;\n",
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              "  background-color: lightgrey;\n",
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              "</style>\n",
              "<table id=\"T_50bdb\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_50bdb_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_50bdb_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th id=\"T_50bdb_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
              "      <th id=\"T_50bdb_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
              "      <th id=\"T_50bdb_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
              "      <th id=\"T_50bdb_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
              "      <th id=\"T_50bdb_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th id=\"T_50bdb_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
              "      <th id=\"T_50bdb_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_50bdb_level0_row0\" class=\"row_heading level0 row0\" >lda</th>\n",
              "      <td id=\"T_50bdb_row0_col0\" class=\"data row0 col0\" >Linear Discriminant Analysis</td>\n",
              "      <td id=\"T_50bdb_row0_col1\" class=\"data row0 col1\" >0.8257</td>\n",
              "      <td id=\"T_50bdb_row0_col2\" class=\"data row0 col2\" >0.8906</td>\n",
              "      <td id=\"T_50bdb_row0_col3\" class=\"data row0 col3\" >0.7659</td>\n",
              "      <td id=\"T_50bdb_row0_col4\" class=\"data row0 col4\" >0.8581</td>\n",
              "      <td id=\"T_50bdb_row0_col5\" class=\"data row0 col5\" >0.8037</td>\n",
              "      <td id=\"T_50bdb_row0_col6\" class=\"data row0 col6\" >0.6479</td>\n",
              "      <td id=\"T_50bdb_row0_col7\" class=\"data row0 col7\" >0.6578</td>\n",
              "      <td id=\"T_50bdb_row0_col8\" class=\"data row0 col8\" >0.0670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_50bdb_level0_row1\" class=\"row_heading level0 row1\" >ridge</th>\n",
              "      <td id=\"T_50bdb_row1_col0\" class=\"data row1 col0\" >Ridge Classifier</td>\n",
              "      <td id=\"T_50bdb_row1_col1\" class=\"data row1 col1\" >0.8215</td>\n",
              "      <td id=\"T_50bdb_row1_col2\" class=\"data row1 col2\" >0.0000</td>\n",
              "      <td id=\"T_50bdb_row1_col3\" class=\"data row1 col3\" >0.7659</td>\n",
              "      <td id=\"T_50bdb_row1_col4\" class=\"data row1 col4\" >0.8499</td>\n",
              "      <td id=\"T_50bdb_row1_col5\" class=\"data row1 col5\" >0.7998</td>\n",
              "      <td id=\"T_50bdb_row1_col6\" class=\"data row1 col6\" >0.6396</td>\n",
              "      <td id=\"T_50bdb_row1_col7\" class=\"data row1 col7\" >0.6493</td>\n",
              "      <td id=\"T_50bdb_row1_col8\" class=\"data row1 col8\" >0.0830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_50bdb_level0_row2\" class=\"row_heading level0 row2\" >nb</th>\n",
              "      <td id=\"T_50bdb_row2_col0\" class=\"data row2 col0\" >Naive Bayes</td>\n",
              "      <td id=\"T_50bdb_row2_col1\" class=\"data row2 col1\" >0.8180</td>\n",
              "      <td id=\"T_50bdb_row2_col2\" class=\"data row2 col2\" >0.8907</td>\n",
              "      <td id=\"T_50bdb_row2_col3\" class=\"data row2 col3\" >0.7659</td>\n",
              "      <td id=\"T_50bdb_row2_col4\" class=\"data row2 col4\" >0.8298</td>\n",
              "      <td id=\"T_50bdb_row2_col5\" class=\"data row2 col5\" >0.7939</td>\n",
              "      <td id=\"T_50bdb_row2_col6\" class=\"data row2 col6\" >0.6316</td>\n",
              "      <td id=\"T_50bdb_row2_col7\" class=\"data row2 col7\" >0.6367</td>\n",
              "      <td id=\"T_50bdb_row2_col8\" class=\"data row2 col8\" >0.0730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_50bdb_level0_row3\" class=\"row_heading level0 row3\" >lightgbm</th>\n",
              "      <td id=\"T_50bdb_row3_col0\" class=\"data row3 col0\" >Light Gradient Boosting Machine</td>\n",
              "      <td id=\"T_50bdb_row3_col1\" class=\"data row3 col1\" >0.8175</td>\n",
              "      <td id=\"T_50bdb_row3_col2\" class=\"data row3 col2\" >0.8581</td>\n",
              "      <td id=\"T_50bdb_row3_col3\" class=\"data row3 col3\" >0.7841</td>\n",
              "      <td id=\"T_50bdb_row3_col4\" class=\"data row3 col4\" >0.8295</td>\n",
              "      <td id=\"T_50bdb_row3_col5\" class=\"data row3 col5\" >0.8004</td>\n",
              "      <td id=\"T_50bdb_row3_col6\" class=\"data row3 col6\" >0.6328</td>\n",
              "      <td id=\"T_50bdb_row3_col7\" class=\"data row3 col7\" >0.6399</td>\n",
              "      <td id=\"T_50bdb_row3_col8\" class=\"data row3 col8\" >0.2880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_50bdb_level0_row4\" class=\"row_heading level0 row4\" >lr</th>\n",
              "      <td id=\"T_50bdb_row4_col0\" class=\"data row4 col0\" >Logistic Regression</td>\n",
              "      <td id=\"T_50bdb_row4_col1\" class=\"data row4 col1\" >0.8173</td>\n",
              "      <td id=\"T_50bdb_row4_col2\" class=\"data row4 col2\" >0.8857</td>\n",
              "      <td id=\"T_50bdb_row4_col3\" class=\"data row4 col3\" >0.7659</td>\n",
              "      <td id=\"T_50bdb_row4_col4\" class=\"data row4 col4\" >0.8392</td>\n",
              "      <td id=\"T_50bdb_row4_col5\" class=\"data row4 col5\" >0.7952</td>\n",
              "      <td id=\"T_50bdb_row4_col6\" class=\"data row4 col6\" >0.6311</td>\n",
              "      <td id=\"T_50bdb_row4_col7\" class=\"data row4 col7\" >0.6408</td>\n",
              "      <td id=\"T_50bdb_row4_col8\" class=\"data row4 col8\" >0.3980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_50bdb_level0_row5\" class=\"row_heading level0 row5\" >rf</th>\n",
              "      <td id=\"T_50bdb_row5_col0\" class=\"data row5 col0\" >Random Forest Classifier</td>\n",
              "      <td id=\"T_50bdb_row5_col1\" class=\"data row5 col1\" >0.8012</td>\n",
              "      <td id=\"T_50bdb_row5_col2\" class=\"data row5 col2\" >0.8794</td>\n",
              "      <td id=\"T_50bdb_row5_col3\" class=\"data row5 col3\" >0.7394</td>\n",
              "      <td id=\"T_50bdb_row5_col4\" class=\"data row5 col4\" >0.8336</td>\n",
              "      <td id=\"T_50bdb_row5_col5\" class=\"data row5 col5\" >0.7755</td>\n",
              "      <td id=\"T_50bdb_row5_col6\" class=\"data row5 col6\" >0.5984</td>\n",
              "      <td id=\"T_50bdb_row5_col7\" class=\"data row5 col7\" >0.6102</td>\n",
              "      <td id=\"T_50bdb_row5_col8\" class=\"data row5 col8\" >0.4120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_50bdb_level0_row6\" class=\"row_heading level0 row6\" >xgboost</th>\n",
              "      <td id=\"T_50bdb_row6_col0\" class=\"data row6 col0\" >Extreme Gradient Boosting</td>\n",
              "      <td id=\"T_50bdb_row6_col1\" class=\"data row6 col1\" >0.7888</td>\n",
              "      <td id=\"T_50bdb_row6_col2\" class=\"data row6 col2\" >0.8501</td>\n",
              "      <td id=\"T_50bdb_row6_col3\" class=\"data row6 col3\" >0.7667</td>\n",
              "      <td id=\"T_50bdb_row6_col4\" class=\"data row6 col4\" >0.7832</td>\n",
              "      <td id=\"T_50bdb_row6_col5\" class=\"data row6 col5\" >0.7712</td>\n",
              "      <td id=\"T_50bdb_row6_col6\" class=\"data row6 col6\" >0.5757</td>\n",
              "      <td id=\"T_50bdb_row6_col7\" class=\"data row6 col7\" >0.5802</td>\n",
              "      <td id=\"T_50bdb_row6_col8\" class=\"data row6 col8\" >0.0990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_50bdb_level0_row7\" class=\"row_heading level0 row7\" >gbc</th>\n",
              "      <td id=\"T_50bdb_row7_col0\" class=\"data row7 col0\" >Gradient Boosting Classifier</td>\n",
              "      <td id=\"T_50bdb_row7_col1\" class=\"data row7 col1\" >0.7885</td>\n",
              "      <td id=\"T_50bdb_row7_col2\" class=\"data row7 col2\" >0.8469</td>\n",
              "      <td id=\"T_50bdb_row7_col3\" class=\"data row7 col3\" >0.7659</td>\n",
              "      <td id=\"T_50bdb_row7_col4\" class=\"data row7 col4\" >0.7871</td>\n",
              "      <td id=\"T_50bdb_row7_col5\" class=\"data row7 col5\" >0.7712</td>\n",
              "      <td id=\"T_50bdb_row7_col6\" class=\"data row7 col6\" >0.5752</td>\n",
              "      <td id=\"T_50bdb_row7_col7\" class=\"data row7 col7\" >0.5814</td>\n",
              "      <td id=\"T_50bdb_row7_col8\" class=\"data row7 col8\" >0.1460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_50bdb_level0_row8\" class=\"row_heading level0 row8\" >et</th>\n",
              "      <td id=\"T_50bdb_row8_col0\" class=\"data row8 col0\" >Extra Trees Classifier</td>\n",
              "      <td id=\"T_50bdb_row8_col1\" class=\"data row8 col1\" >0.7885</td>\n",
              "      <td id=\"T_50bdb_row8_col2\" class=\"data row8 col2\" >0.8875</td>\n",
              "      <td id=\"T_50bdb_row8_col3\" class=\"data row8 col3\" >0.7386</td>\n",
              "      <td id=\"T_50bdb_row8_col4\" class=\"data row8 col4\" >0.7991</td>\n",
              "      <td id=\"T_50bdb_row8_col5\" class=\"data row8 col5\" >0.7639</td>\n",
              "      <td id=\"T_50bdb_row8_col6\" class=\"data row8 col6\" >0.5729</td>\n",
              "      <td id=\"T_50bdb_row8_col7\" class=\"data row8 col7\" >0.5788</td>\n",
              "      <td id=\"T_50bdb_row8_col8\" class=\"data row8 col8\" >0.2190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_50bdb_level0_row9\" class=\"row_heading level0 row9\" >qda</th>\n",
              "      <td id=\"T_50bdb_row9_col0\" class=\"data row9 col0\" >Quadratic Discriminant Analysis</td>\n",
              "      <td id=\"T_50bdb_row9_col1\" class=\"data row9 col1\" >0.7847</td>\n",
              "      <td id=\"T_50bdb_row9_col2\" class=\"data row9 col2\" >0.8670</td>\n",
              "      <td id=\"T_50bdb_row9_col3\" class=\"data row9 col3\" >0.7568</td>\n",
              "      <td id=\"T_50bdb_row9_col4\" class=\"data row9 col4\" >0.7856</td>\n",
              "      <td id=\"T_50bdb_row9_col5\" class=\"data row9 col5\" >0.7662</td>\n",
              "      <td id=\"T_50bdb_row9_col6\" class=\"data row9 col6\" >0.5671</td>\n",
              "      <td id=\"T_50bdb_row9_col7\" class=\"data row9 col7\" >0.5729</td>\n",
              "      <td id=\"T_50bdb_row9_col8\" class=\"data row9 col8\" >0.0670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_50bdb_level0_row10\" class=\"row_heading level0 row10\" >dt</th>\n",
              "      <td id=\"T_50bdb_row10_col0\" class=\"data row10 col0\" >Decision Tree Classifier</td>\n",
              "      <td id=\"T_50bdb_row10_col1\" class=\"data row10 col1\" >0.7473</td>\n",
              "      <td id=\"T_50bdb_row10_col2\" class=\"data row10 col2\" >0.7460</td>\n",
              "      <td id=\"T_50bdb_row10_col3\" class=\"data row10 col3\" >0.7212</td>\n",
              "      <td id=\"T_50bdb_row10_col4\" class=\"data row10 col4\" >0.7485</td>\n",
              "      <td id=\"T_50bdb_row10_col5\" class=\"data row10 col5\" >0.7238</td>\n",
              "      <td id=\"T_50bdb_row10_col6\" class=\"data row10 col6\" >0.4923</td>\n",
              "      <td id=\"T_50bdb_row10_col7\" class=\"data row10 col7\" >0.5049</td>\n",
              "      <td id=\"T_50bdb_row10_col8\" class=\"data row10 col8\" >0.0690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_50bdb_level0_row11\" class=\"row_heading level0 row11\" >ada</th>\n",
              "      <td id=\"T_50bdb_row11_col0\" class=\"data row11 col0\" >Ada Boost Classifier</td>\n",
              "      <td id=\"T_50bdb_row11_col1\" class=\"data row11 col1\" >0.7432</td>\n",
              "      <td id=\"T_50bdb_row11_col2\" class=\"data row11 col2\" >0.8442</td>\n",
              "      <td id=\"T_50bdb_row11_col3\" class=\"data row11 col3\" >0.6765</td>\n",
              "      <td id=\"T_50bdb_row11_col4\" class=\"data row11 col4\" >0.7509</td>\n",
              "      <td id=\"T_50bdb_row11_col5\" class=\"data row11 col5\" >0.7068</td>\n",
              "      <td id=\"T_50bdb_row11_col6\" class=\"data row11 col6\" >0.4801</td>\n",
              "      <td id=\"T_50bdb_row11_col7\" class=\"data row11 col7\" >0.4868</td>\n",
              "      <td id=\"T_50bdb_row11_col8\" class=\"data row11 col8\" >0.1430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_50bdb_level0_row12\" class=\"row_heading level0 row12\" >svm</th>\n",
              "      <td id=\"T_50bdb_row12_col0\" class=\"data row12 col0\" >SVM - Linear Kernel</td>\n",
              "      <td id=\"T_50bdb_row12_col1\" class=\"data row12 col1\" >0.6778</td>\n",
              "      <td id=\"T_50bdb_row12_col2\" class=\"data row12 col2\" >0.0000</td>\n",
              "      <td id=\"T_50bdb_row12_col3\" class=\"data row12 col3\" >0.5045</td>\n",
              "      <td id=\"T_50bdb_row12_col4\" class=\"data row12 col4\" >0.7348</td>\n",
              "      <td id=\"T_50bdb_row12_col5\" class=\"data row12 col5\" >0.5343</td>\n",
              "      <td id=\"T_50bdb_row12_col6\" class=\"data row12 col6\" >0.3329</td>\n",
              "      <td id=\"T_50bdb_row12_col7\" class=\"data row12 col7\" >0.4002</td>\n",
              "      <td id=\"T_50bdb_row12_col8\" class=\"data row12 col8\" >0.0570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_50bdb_level0_row13\" class=\"row_heading level0 row13\" >knn</th>\n",
              "      <td id=\"T_50bdb_row13_col0\" class=\"data row13 col0\" >K Neighbors Classifier</td>\n",
              "      <td id=\"T_50bdb_row13_col1\" class=\"data row13 col1\" >0.6322</td>\n",
              "      <td id=\"T_50bdb_row13_col2\" class=\"data row13 col2\" >0.6701</td>\n",
              "      <td id=\"T_50bdb_row13_col3\" class=\"data row13 col3\" >0.5045</td>\n",
              "      <td id=\"T_50bdb_row13_col4\" class=\"data row13 col4\" >0.6319</td>\n",
              "      <td id=\"T_50bdb_row13_col5\" class=\"data row13 col5\" >0.5533</td>\n",
              "      <td id=\"T_50bdb_row13_col6\" class=\"data row13 col6\" >0.2496</td>\n",
              "      <td id=\"T_50bdb_row13_col7\" class=\"data row13 col7\" >0.2586</td>\n",
              "      <td id=\"T_50bdb_row13_col8\" class=\"data row13 col8\" >0.0740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_50bdb_level0_row14\" class=\"row_heading level0 row14\" >dummy</th>\n",
              "      <td id=\"T_50bdb_row14_col0\" class=\"data row14 col0\" >Dummy Classifier</td>\n",
              "      <td id=\"T_50bdb_row14_col1\" class=\"data row14 col1\" >0.5413</td>\n",
              "      <td id=\"T_50bdb_row14_col2\" class=\"data row14 col2\" >0.5000</td>\n",
              "      <td id=\"T_50bdb_row14_col3\" class=\"data row14 col3\" >0.0000</td>\n",
              "      <td id=\"T_50bdb_row14_col4\" class=\"data row14 col4\" >0.0000</td>\n",
              "      <td id=\"T_50bdb_row14_col5\" class=\"data row14 col5\" >0.0000</td>\n",
              "      <td id=\"T_50bdb_row14_col6\" class=\"data row14 col6\" >0.0000</td>\n",
              "      <td id=\"T_50bdb_row14_col7\" class=\"data row14 col7\" >0.0000</td>\n",
              "      <td id=\"T_50bdb_row14_col8\" class=\"data row14 col8\" >0.1080</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/65 [00:00<?, ?it/s]"
            ],
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          },
          "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"
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            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearDiscriminantAnalysis(covariance_estimator=None, n_components=None,\n",
              "                           priors=None, shrinkage=None, solver='svd',\n",
              "                           store_covariance=False, tol=0.0001)"
            ],
            "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>LinearDiscriminantAnalysis(covariance_estimator=None, n_components=None,\n",
              "                           priors=None, shrinkage=None, solver=&#x27;svd&#x27;,\n",
              "                           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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearDiscriminantAnalysis</label><div class=\"sk-toggleable__content\"><pre>LinearDiscriminantAnalysis(covariance_estimator=None, n_components=None,\n",
              "                           priors=None, shrinkage=None, solver=&#x27;svd&#x27;,\n",
              "                           store_covariance=False, tol=0.0001)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create model\n",
        "lda = create_model('lda')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457,
          "referenced_widgets": [
            "c6d6d63684aa402d8b5252a1e6c3da4c",
            "c09e04ec3a224d4c8ce50ce6d9d29e26",
            "76ef5686e8ee4c14927a389a830cec6a",
            "4810358844034c02875b4479c6bca995",
            "2f7cf92905094ae6921b4afe3203aff4",
            "df2d8c83dea34447a0ae1c7fb10af182",
            "e06296e3c9e54ecdb666c35f3c9a52ad",
            "1d85bdee98c345a3ae95e0bce041eff2",
            "cb7438895880457097354ea3625e4aac",
            "f1b1e623627c4641b75956606a7a0d6a",
            "706da08b59ce4f9eb628ade1db24f1de"
          ]
        },
        "id": "EcRIUk_ImmkM",
        "outputId": "3e4292dc-ca19-4586-af6c-e92fe241cb17"
      },
      "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 0x7fb4690b7850>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_8bafe_row10_col0, #T_8bafe_row10_col1, #T_8bafe_row10_col2, #T_8bafe_row10_col3, #T_8bafe_row10_col4, #T_8bafe_row10_col5, #T_8bafe_row10_col6 {\n",
              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_8bafe\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_8bafe_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
              "      <th id=\"T_8bafe_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
              "      <th id=\"T_8bafe_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
              "      <th id=\"T_8bafe_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
              "      <th id=\"T_8bafe_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
              "      <th id=\"T_8bafe_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
              "      <th id=\"T_8bafe_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_8bafe_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_8bafe_row0_col0\" class=\"data row0 col0\" >0.8800</td>\n",
              "      <td id=\"T_8bafe_row0_col1\" class=\"data row0 col1\" >0.8910</td>\n",
              "      <td id=\"T_8bafe_row0_col2\" class=\"data row0 col2\" >0.7500</td>\n",
              "      <td id=\"T_8bafe_row0_col3\" class=\"data row0 col3\" >1.0000</td>\n",
              "      <td id=\"T_8bafe_row0_col4\" class=\"data row0 col4\" >0.8571</td>\n",
              "      <td id=\"T_8bafe_row0_col5\" class=\"data row0 col5\" >0.7573</td>\n",
              "      <td id=\"T_8bafe_row0_col6\" class=\"data row0 col6\" >0.7806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8bafe_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_8bafe_row1_col0\" class=\"data row1 col0\" >0.9600</td>\n",
              "      <td id=\"T_8bafe_row1_col1\" class=\"data row1 col1\" >0.9870</td>\n",
              "      <td id=\"T_8bafe_row1_col2\" class=\"data row1 col2\" >0.9091</td>\n",
              "      <td id=\"T_8bafe_row1_col3\" class=\"data row1 col3\" >1.0000</td>\n",
              "      <td id=\"T_8bafe_row1_col4\" class=\"data row1 col4\" >0.9524</td>\n",
              "      <td id=\"T_8bafe_row1_col5\" class=\"data row1 col5\" >0.9180</td>\n",
              "      <td id=\"T_8bafe_row1_col6\" class=\"data row1 col6\" >0.9211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8bafe_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_8bafe_row2_col0\" class=\"data row2 col0\" >0.7500</td>\n",
              "      <td id=\"T_8bafe_row2_col1\" class=\"data row2 col1\" >0.7762</td>\n",
              "      <td id=\"T_8bafe_row2_col2\" class=\"data row2 col2\" >0.6364</td>\n",
              "      <td id=\"T_8bafe_row2_col3\" class=\"data row2 col3\" >0.7778</td>\n",
              "      <td id=\"T_8bafe_row2_col4\" class=\"data row2 col4\" >0.7000</td>\n",
              "      <td id=\"T_8bafe_row2_col5\" class=\"data row2 col5\" >0.4894</td>\n",
              "      <td id=\"T_8bafe_row2_col6\" class=\"data row2 col6\" >0.4966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8bafe_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_8bafe_row3_col0\" class=\"data row3 col0\" >0.8750</td>\n",
              "      <td id=\"T_8bafe_row3_col1\" class=\"data row3 col1\" >0.8741</td>\n",
              "      <td id=\"T_8bafe_row3_col2\" class=\"data row3 col2\" >0.8182</td>\n",
              "      <td id=\"T_8bafe_row3_col3\" class=\"data row3 col3\" >0.9000</td>\n",
              "      <td id=\"T_8bafe_row3_col4\" class=\"data row3 col4\" >0.8571</td>\n",
              "      <td id=\"T_8bafe_row3_col5\" class=\"data row3 col5\" >0.7465</td>\n",
              "      <td id=\"T_8bafe_row3_col6\" class=\"data row3 col6\" >0.7492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8bafe_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_8bafe_row4_col0\" class=\"data row4 col0\" >0.7500</td>\n",
              "      <td id=\"T_8bafe_row4_col1\" class=\"data row4 col1\" >0.9161</td>\n",
              "      <td id=\"T_8bafe_row4_col2\" class=\"data row4 col2\" >0.7273</td>\n",
              "      <td id=\"T_8bafe_row4_col3\" class=\"data row4 col3\" >0.7273</td>\n",
              "      <td id=\"T_8bafe_row4_col4\" class=\"data row4 col4\" >0.7273</td>\n",
              "      <td id=\"T_8bafe_row4_col5\" class=\"data row4 col5\" >0.4965</td>\n",
              "      <td id=\"T_8bafe_row4_col6\" class=\"data row4 col6\" >0.4965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8bafe_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_8bafe_row5_col0\" class=\"data row5 col0\" >0.9167</td>\n",
              "      <td id=\"T_8bafe_row5_col1\" class=\"data row5 col1\" >0.9231</td>\n",
              "      <td id=\"T_8bafe_row5_col2\" class=\"data row5 col2\" >0.9091</td>\n",
              "      <td id=\"T_8bafe_row5_col3\" class=\"data row5 col3\" >0.9091</td>\n",
              "      <td id=\"T_8bafe_row5_col4\" class=\"data row5 col4\" >0.9091</td>\n",
              "      <td id=\"T_8bafe_row5_col5\" class=\"data row5 col5\" >0.8322</td>\n",
              "      <td id=\"T_8bafe_row5_col6\" class=\"data row5 col6\" >0.8322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8bafe_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_8bafe_row6_col0\" class=\"data row6 col0\" >0.8750</td>\n",
              "      <td id=\"T_8bafe_row6_col1\" class=\"data row6 col1\" >0.9860</td>\n",
              "      <td id=\"T_8bafe_row6_col2\" class=\"data row6 col2\" >0.7273</td>\n",
              "      <td id=\"T_8bafe_row6_col3\" class=\"data row6 col3\" >1.0000</td>\n",
              "      <td id=\"T_8bafe_row6_col4\" class=\"data row6 col4\" >0.8421</td>\n",
              "      <td id=\"T_8bafe_row6_col5\" class=\"data row6 col5\" >0.7429</td>\n",
              "      <td id=\"T_8bafe_row6_col6\" class=\"data row6 col6\" >0.7687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8bafe_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_8bafe_row7_col0\" class=\"data row7 col0\" >0.6667</td>\n",
              "      <td id=\"T_8bafe_row7_col1\" class=\"data row7 col1\" >0.8671</td>\n",
              "      <td id=\"T_8bafe_row7_col2\" class=\"data row7 col2\" >0.8182</td>\n",
              "      <td id=\"T_8bafe_row7_col3\" class=\"data row7 col3\" >0.6000</td>\n",
              "      <td id=\"T_8bafe_row7_col4\" class=\"data row7 col4\" >0.6923</td>\n",
              "      <td id=\"T_8bafe_row7_col5\" class=\"data row7 col5\" >0.3469</td>\n",
              "      <td id=\"T_8bafe_row7_col6\" class=\"data row7 col6\" >0.3671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8bafe_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_8bafe_row8_col0\" class=\"data row8 col0\" >0.8333</td>\n",
              "      <td id=\"T_8bafe_row8_col1\" class=\"data row8 col1\" >0.8951</td>\n",
              "      <td id=\"T_8bafe_row8_col2\" class=\"data row8 col2\" >0.7273</td>\n",
              "      <td id=\"T_8bafe_row8_col3\" class=\"data row8 col3\" >0.8889</td>\n",
              "      <td id=\"T_8bafe_row8_col4\" class=\"data row8 col4\" >0.8000</td>\n",
              "      <td id=\"T_8bafe_row8_col5\" class=\"data row8 col5\" >0.6596</td>\n",
              "      <td id=\"T_8bafe_row8_col6\" class=\"data row8 col6\" >0.6693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8bafe_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_8bafe_row9_col0\" class=\"data row9 col0\" >0.7500</td>\n",
              "      <td id=\"T_8bafe_row9_col1\" class=\"data row9 col1\" >0.7902</td>\n",
              "      <td id=\"T_8bafe_row9_col2\" class=\"data row9 col2\" >0.6364</td>\n",
              "      <td id=\"T_8bafe_row9_col3\" class=\"data row9 col3\" >0.7778</td>\n",
              "      <td id=\"T_8bafe_row9_col4\" class=\"data row9 col4\" >0.7000</td>\n",
              "      <td id=\"T_8bafe_row9_col5\" class=\"data row9 col5\" >0.4894</td>\n",
              "      <td id=\"T_8bafe_row9_col6\" class=\"data row9 col6\" >0.4966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8bafe_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
              "      <td id=\"T_8bafe_row10_col0\" class=\"data row10 col0\" >0.8257</td>\n",
              "      <td id=\"T_8bafe_row10_col1\" class=\"data row10 col1\" >0.8906</td>\n",
              "      <td id=\"T_8bafe_row10_col2\" class=\"data row10 col2\" >0.7659</td>\n",
              "      <td id=\"T_8bafe_row10_col3\" class=\"data row10 col3\" >0.8581</td>\n",
              "      <td id=\"T_8bafe_row10_col4\" class=\"data row10 col4\" >0.8037</td>\n",
              "      <td id=\"T_8bafe_row10_col5\" class=\"data row10 col5\" >0.6479</td>\n",
              "      <td id=\"T_8bafe_row10_col6\" class=\"data row10 col6\" >0.6578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8bafe_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
              "      <td id=\"T_8bafe_row11_col0\" class=\"data row11 col0\" >0.0876</td>\n",
              "      <td id=\"T_8bafe_row11_col1\" class=\"data row11 col1\" >0.0664</td>\n",
              "      <td id=\"T_8bafe_row11_col2\" class=\"data row11 col2\" >0.0921</td>\n",
              "      <td id=\"T_8bafe_row11_col3\" class=\"data row11 col3\" >0.1274</td>\n",
              "      <td id=\"T_8bafe_row11_col4\" class=\"data row11 col4\" >0.0895</td>\n",
              "      <td id=\"T_8bafe_row11_col5\" class=\"data row11 col5\" >0.1736</td>\n",
              "      <td id=\"T_8bafe_row11_col6\" class=\"data row11 col6\" >0.1727</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": "c6d6d63684aa402d8b5252a1e6c3da4c"
            }
          },
          "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": [
        "# tune model\n",
        "tuned_lda = tune_model(lda)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474,
          "referenced_widgets": [
            "d498a707b78d4be380dff061da003928",
            "bc29b666d99646f28f94c0a1652a60f3",
            "e9496617c2a94a52ab886780143e85a4",
            "4a97a71e7b114986a3be704e45596417",
            "d1d1baf81d564b759994c5faa393d738",
            "dfeb564f090c4fb68dac91a7462e8e74",
            "433a37852ca9460593889bf7ed24e5ac",
            "61f964ce6aaf45e6b27ebeb1c112f323",
            "6b5d0890b7574ce69e53fb797737f486",
            "2cff99211e9d4369b81cf653057456bf",
            "666b06a60faa40b984d8a3e358478659"
          ]
        },
        "id": "XPUBHV6ommYB",
        "outputId": "46fb6359-32ec-4727-ac2a-299ec9467730"
      },
      "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 0x7fb468e14670>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_d70ed_row10_col0, #T_d70ed_row10_col1, #T_d70ed_row10_col2, #T_d70ed_row10_col3, #T_d70ed_row10_col4, #T_d70ed_row10_col5, #T_d70ed_row10_col6 {\n",
              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_d70ed\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_d70ed_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
              "      <th id=\"T_d70ed_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
              "      <th id=\"T_d70ed_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
              "      <th id=\"T_d70ed_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
              "      <th id=\"T_d70ed_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
              "      <th id=\"T_d70ed_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
              "      <th id=\"T_d70ed_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_d70ed_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_d70ed_row0_col0\" class=\"data row0 col0\" >0.8800</td>\n",
              "      <td id=\"T_d70ed_row0_col1\" class=\"data row0 col1\" >0.9167</td>\n",
              "      <td id=\"T_d70ed_row0_col2\" class=\"data row0 col2\" >0.7500</td>\n",
              "      <td id=\"T_d70ed_row0_col3\" class=\"data row0 col3\" >1.0000</td>\n",
              "      <td id=\"T_d70ed_row0_col4\" class=\"data row0 col4\" >0.8571</td>\n",
              "      <td id=\"T_d70ed_row0_col5\" class=\"data row0 col5\" >0.7573</td>\n",
              "      <td id=\"T_d70ed_row0_col6\" class=\"data row0 col6\" >0.7806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d70ed_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_d70ed_row1_col0\" class=\"data row1 col0\" >0.9600</td>\n",
              "      <td id=\"T_d70ed_row1_col1\" class=\"data row1 col1\" >0.9870</td>\n",
              "      <td id=\"T_d70ed_row1_col2\" class=\"data row1 col2\" >0.9091</td>\n",
              "      <td id=\"T_d70ed_row1_col3\" class=\"data row1 col3\" >1.0000</td>\n",
              "      <td id=\"T_d70ed_row1_col4\" class=\"data row1 col4\" >0.9524</td>\n",
              "      <td id=\"T_d70ed_row1_col5\" class=\"data row1 col5\" >0.9180</td>\n",
              "      <td id=\"T_d70ed_row1_col6\" class=\"data row1 col6\" >0.9211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d70ed_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_d70ed_row2_col0\" class=\"data row2 col0\" >0.7500</td>\n",
              "      <td id=\"T_d70ed_row2_col1\" class=\"data row2 col1\" >0.7692</td>\n",
              "      <td id=\"T_d70ed_row2_col2\" class=\"data row2 col2\" >0.6364</td>\n",
              "      <td id=\"T_d70ed_row2_col3\" class=\"data row2 col3\" >0.7778</td>\n",
              "      <td id=\"T_d70ed_row2_col4\" class=\"data row2 col4\" >0.7000</td>\n",
              "      <td id=\"T_d70ed_row2_col5\" class=\"data row2 col5\" >0.4894</td>\n",
              "      <td id=\"T_d70ed_row2_col6\" class=\"data row2 col6\" >0.4966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d70ed_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_d70ed_row3_col0\" class=\"data row3 col0\" >0.8333</td>\n",
              "      <td id=\"T_d70ed_row3_col1\" class=\"data row3 col1\" >0.8811</td>\n",
              "      <td id=\"T_d70ed_row3_col2\" class=\"data row3 col2\" >0.8182</td>\n",
              "      <td id=\"T_d70ed_row3_col3\" class=\"data row3 col3\" >0.8182</td>\n",
              "      <td id=\"T_d70ed_row3_col4\" class=\"data row3 col4\" >0.8182</td>\n",
              "      <td id=\"T_d70ed_row3_col5\" class=\"data row3 col5\" >0.6643</td>\n",
              "      <td id=\"T_d70ed_row3_col6\" class=\"data row3 col6\" >0.6643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d70ed_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_d70ed_row4_col0\" class=\"data row4 col0\" >0.7917</td>\n",
              "      <td id=\"T_d70ed_row4_col1\" class=\"data row4 col1\" >0.9161</td>\n",
              "      <td id=\"T_d70ed_row4_col2\" class=\"data row4 col2\" >0.7273</td>\n",
              "      <td id=\"T_d70ed_row4_col3\" class=\"data row4 col3\" >0.8000</td>\n",
              "      <td id=\"T_d70ed_row4_col4\" class=\"data row4 col4\" >0.7619</td>\n",
              "      <td id=\"T_d70ed_row4_col5\" class=\"data row4 col5\" >0.5775</td>\n",
              "      <td id=\"T_d70ed_row4_col6\" class=\"data row4 col6\" >0.5795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d70ed_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_d70ed_row5_col0\" class=\"data row5 col0\" >0.9167</td>\n",
              "      <td id=\"T_d70ed_row5_col1\" class=\"data row5 col1\" >0.9371</td>\n",
              "      <td id=\"T_d70ed_row5_col2\" class=\"data row5 col2\" >0.9091</td>\n",
              "      <td id=\"T_d70ed_row5_col3\" class=\"data row5 col3\" >0.9091</td>\n",
              "      <td id=\"T_d70ed_row5_col4\" class=\"data row5 col4\" >0.9091</td>\n",
              "      <td id=\"T_d70ed_row5_col5\" class=\"data row5 col5\" >0.8322</td>\n",
              "      <td id=\"T_d70ed_row5_col6\" class=\"data row5 col6\" >0.8322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d70ed_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_d70ed_row6_col0\" class=\"data row6 col0\" >0.8333</td>\n",
              "      <td id=\"T_d70ed_row6_col1\" class=\"data row6 col1\" >0.9790</td>\n",
              "      <td id=\"T_d70ed_row6_col2\" class=\"data row6 col2\" >0.6364</td>\n",
              "      <td id=\"T_d70ed_row6_col3\" class=\"data row6 col3\" >1.0000</td>\n",
              "      <td id=\"T_d70ed_row6_col4\" class=\"data row6 col4\" >0.7778</td>\n",
              "      <td id=\"T_d70ed_row6_col5\" class=\"data row6 col5\" >0.6547</td>\n",
              "      <td id=\"T_d70ed_row6_col6\" class=\"data row6 col6\" >0.6976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d70ed_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_d70ed_row7_col0\" class=\"data row7 col0\" >0.7917</td>\n",
              "      <td id=\"T_d70ed_row7_col1\" class=\"data row7 col1\" >0.8881</td>\n",
              "      <td id=\"T_d70ed_row7_col2\" class=\"data row7 col2\" >0.9091</td>\n",
              "      <td id=\"T_d70ed_row7_col3\" class=\"data row7 col3\" >0.7143</td>\n",
              "      <td id=\"T_d70ed_row7_col4\" class=\"data row7 col4\" >0.8000</td>\n",
              "      <td id=\"T_d70ed_row7_col5\" class=\"data row7 col5\" >0.5890</td>\n",
              "      <td id=\"T_d70ed_row7_col6\" class=\"data row7 col6\" >0.6078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d70ed_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_d70ed_row8_col0\" class=\"data row8 col0\" >0.8333</td>\n",
              "      <td id=\"T_d70ed_row8_col1\" class=\"data row8 col1\" >0.9091</td>\n",
              "      <td id=\"T_d70ed_row8_col2\" class=\"data row8 col2\" >0.7273</td>\n",
              "      <td id=\"T_d70ed_row8_col3\" class=\"data row8 col3\" >0.8889</td>\n",
              "      <td id=\"T_d70ed_row8_col4\" class=\"data row8 col4\" >0.8000</td>\n",
              "      <td id=\"T_d70ed_row8_col5\" class=\"data row8 col5\" >0.6596</td>\n",
              "      <td id=\"T_d70ed_row8_col6\" class=\"data row8 col6\" >0.6693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d70ed_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_d70ed_row9_col0\" class=\"data row9 col0\" >0.7500</td>\n",
              "      <td id=\"T_d70ed_row9_col1\" class=\"data row9 col1\" >0.7902</td>\n",
              "      <td id=\"T_d70ed_row9_col2\" class=\"data row9 col2\" >0.6364</td>\n",
              "      <td id=\"T_d70ed_row9_col3\" class=\"data row9 col3\" >0.7778</td>\n",
              "      <td id=\"T_d70ed_row9_col4\" class=\"data row9 col4\" >0.7000</td>\n",
              "      <td id=\"T_d70ed_row9_col5\" class=\"data row9 col5\" >0.4894</td>\n",
              "      <td id=\"T_d70ed_row9_col6\" class=\"data row9 col6\" >0.4966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d70ed_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
              "      <td id=\"T_d70ed_row10_col0\" class=\"data row10 col0\" >0.8340</td>\n",
              "      <td id=\"T_d70ed_row10_col1\" class=\"data row10 col1\" >0.8974</td>\n",
              "      <td id=\"T_d70ed_row10_col2\" class=\"data row10 col2\" >0.7659</td>\n",
              "      <td id=\"T_d70ed_row10_col3\" class=\"data row10 col3\" >0.8686</td>\n",
              "      <td id=\"T_d70ed_row10_col4\" class=\"data row10 col4\" >0.8076</td>\n",
              "      <td id=\"T_d70ed_row10_col5\" class=\"data row10 col5\" >0.6631</td>\n",
              "      <td id=\"T_d70ed_row10_col6\" class=\"data row10 col6\" >0.6746</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d70ed_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
              "      <td id=\"T_d70ed_row11_col0\" class=\"data row11 col0\" >0.0652</td>\n",
              "      <td id=\"T_d70ed_row11_col1\" class=\"data row11 col1\" >0.0674</td>\n",
              "      <td id=\"T_d70ed_row11_col2\" class=\"data row11 col2\" >0.1085</td>\n",
              "      <td id=\"T_d70ed_row11_col3\" class=\"data row11 col3\" >0.1007</td>\n",
              "      <td id=\"T_d70ed_row11_col4\" class=\"data row11 col4\" >0.0775</td>\n",
              "      <td id=\"T_d70ed_row11_col5\" class=\"data row11 col5\" >0.1326</td>\n",
              "      <td id=\"T_d70ed_row11_col6\" class=\"data row11 col6\" >0.1320</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/7 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d498a707b78d4be380dff061da003928"
            }
          },
          "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": "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>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# display tuned model\n",
        "print(tuned_lda)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vXReBaG0pMh_",
        "outputId": "c6936088-0f8e-4433-a0d8-c0d183b09922"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LinearDiscriminantAnalysis(covariance_estimator=None, n_components=None,\n",
            "                           priors=None, shrinkage='auto', solver='lsqr',\n",
            "                           store_covariance=False, tol=0.0001)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict_model(tuned_lda)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "G8bhoYd-pRrA",
        "outputId": "b53dab09-f0d1-4df1-f9d9-25df4871b494"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fb46abea730>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_90a90\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_90a90_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_90a90_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th id=\"T_90a90_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
              "      <th id=\"T_90a90_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
              "      <th id=\"T_90a90_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
              "      <th id=\"T_90a90_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
              "      <th id=\"T_90a90_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th id=\"T_90a90_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_90a90_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_90a90_row0_col0\" class=\"data row0 col0\" >Linear Discriminant Analysis</td>\n",
              "      <td id=\"T_90a90_row0_col1\" class=\"data row0 col1\" >0.8525</td>\n",
              "      <td id=\"T_90a90_row0_col2\" class=\"data row0 col2\" >0.9524</td>\n",
              "      <td id=\"T_90a90_row0_col3\" class=\"data row0 col3\" >0.8929</td>\n",
              "      <td id=\"T_90a90_row0_col4\" class=\"data row0 col4\" >0.8065</td>\n",
              "      <td id=\"T_90a90_row0_col5\" class=\"data row0 col5\" >0.8475</td>\n",
              "      <td id=\"T_90a90_row0_col6\" class=\"data row0 col6\" >0.7053</td>\n",
              "      <td id=\"T_90a90_row0_col7\" class=\"data row0 col7\" >0.7087</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
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            "text/plain": [
              "      age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "219  59.0  1.0  4.0     138.0  271.0  0.0      2.0    182.0    0.0      0.0   \n",
              "271  66.0  1.0  4.0     160.0  228.0  0.0      2.0    138.0    0.0      2.3   \n",
              "89   51.0  0.0  3.0     130.0  256.0  0.0      2.0    149.0    0.0      0.5   \n",
              "101  34.0  1.0  1.0     118.0  182.0  0.0      2.0    174.0    0.0      0.0   \n",
              "67   54.0  1.0  3.0     150.0  232.0  0.0      2.0    165.0    0.0      1.6   \n",
              "..    ...  ...  ...       ...    ...  ...      ...      ...    ...      ...   \n",
              "285  58.0  1.0  4.0     114.0  318.0  0.0      1.0    140.0    0.0      4.4   \n",
              "243  61.0  1.0  1.0     134.0  234.0  0.0      0.0    145.0    0.0      2.6   \n",
              "94   63.0  0.0  3.0     135.0  252.0  0.0      2.0    172.0    0.0      0.0   \n",
              "291  55.0  0.0  2.0     132.0  342.0  0.0      0.0    166.0    0.0      1.2   \n",
              "155  70.0  1.0  4.0     130.0  322.0  0.0      2.0    109.0    0.0      2.4   \n",
              "\n",
              "     slope   ca  thal  num  prediction_label  prediction_score  \n",
              "219    1.0  0.0   3.0  0.0                 0            0.9149  \n",
              "271    1.0  0.0   6.0  0.0                 1            0.7858  \n",
              "89     1.0  0.0   3.0  0.0                 0            0.9802  \n",
              "101    1.0  0.0   3.0  0.0                 0            0.9937  \n",
              "67     1.0  0.0   7.0  0.0                 1            0.5419  \n",
              "..     ...  ...   ...  ...               ...               ...  \n",
              "285    3.0  3.0   6.0  1.0                 1            0.9980  \n",
              "243    2.0  2.0   3.0  1.0                 0            0.6571  \n",
              "94     1.0  0.0   3.0  0.0                 0            0.9883  \n",
              "291    1.0  0.0   3.0  0.0                 0            0.9940  \n",
              "155    2.0  3.0   3.0  1.0                 1            0.9874  \n",
              "\n",
              "[61 rows x 16 columns]"
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              "      <th>age</th>\n",
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              "      <th>271</th>\n",
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              "      <th>101</th>\n",
              "      <td>34.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>118.0</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>174.0</td>\n",
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              "      <th>67</th>\n",
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              "      <td>232.0</td>\n",
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              "      <td>0.0</td>\n",
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              "<p>61 rows × 16 columns</p>\n",
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          "metadata": {},
          "execution_count": 38
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}