pycaret_tabular.ipynb

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                "text/html": "<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>Parameters</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>boosting_type</th>\n      <td>gbdt</td>\n    </tr>\n    <tr>\n      <th>class_weight</th>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>colsample_bytree</th>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>importance_type</th>\n      <td>split</td>\n    </tr>\n    <tr>\n      <th>learning_rate</th>\n      <td>0.1</td>\n    </tr>\n    <tr>\n      <th>max_depth</th>\n      <td>-1</td>\n    </tr>\n    <tr>\n      <th>min_child_samples</th>\n      <td>41</td>\n    </tr>\n    <tr>\n      <th>min_child_weight</th>\n      <td>0.001</td>\n    </tr>\n    <tr>\n      <th>min_split_gain</th>\n      <td>0.9</td>\n    </tr>\n    <tr>\n      <th>n_estimators</th>\n      <td>260</td>\n    </tr>\n    <tr>\n      <th>n_jobs</th>\n      <td>-1</td>\n    </tr>\n    <tr>\n      <th>num_leaves</th>\n      <td>70</td>\n    </tr>\n    <tr>\n      <th>objective</th>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>random_state</th>\n      <td>123</td>\n    </tr>\n    <tr>\n      <th>reg_alpha</th>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>reg_lambda</th>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>silent</th>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>subsample</th>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>subsample_for_bin</th>\n      <td>200000</td>\n    </tr>\n    <tr>\n      <th>subsample_freq</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>feature_fraction</th>\n      <td>0.4</td>\n    </tr>\n    <tr>\n      <th>bagging_freq</th>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>bagging_fraction</th>\n      <td>0.6</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tmtong/ai-library-exploration/blob/main/pycaret_tabular.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PzfsO40Foc2X",
        "outputId": "768bd617-45f5-467b-d2f9-44400900b372"
      },
      "source": [
        "!pip install pycaret\n",
        "!pip install -Uqq fastbook\n",
        "!pip install git+https://github.com/fastai/fastai.git"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Building wheels for collected packages: fastai\n",
            "  Building wheel for fastai (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fastai: filename=fastai-2.4.1-cp37-none-any.whl size=188188 sha256=7582a0cbbb9d46854bb06d0f7c51cedc68e4f911b8ca5a9f5662ed1d2f7844a5\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-kbtb16_d/wheels/cf/46/39/b2d08762125ed2376861976ab2c4ac30c029b86e375735d9b8\n",
            "Successfully built fastai\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473,
          "referenced_widgets": [
            "56b7c9ade1414696a61839b655f47b38",
            "4a1a3181d0254178affc57b277518b99",
            "2a4da053fa964841b632f8354f565c98",
            "5991dbe4ad0f41d7ac97f946c014f657",
            "4647a81dd1cc4b1fb4bc47cd31a208e0",
            "6abc8346e28b415d95519d46287c8656",
            "ab0a4ff0b3fd4fb5acd08a6040782e98",
            "7997503cbdf14dd19861cf044c8ef57f",
            "00779f7b6fc248e3b1d87498650efc27"
          ]
        },
        "id": "72jj1Be_p60H",
        "outputId": "d15b72cd-8e90-4728-a5d1-ee563a9f2385"
      },
      "source": [
        "from fastai.tabular.all import *\n",
        "import pandas as pd\n",
        "import pycaret\n",
        "from pycaret.classification import *\n",
        "path = untar_data(URLs.ADULT_SAMPLE)\n",
        "path.ls()\n",
        "data_path = '/root/.fastai/data/adult_sample/adult.csv'\n",
        "\n",
        "def load_pd():\n",
        "    dataframe = pd.read_csv(data_path)\n",
        "    return dataframe\n",
        "data = load_pd()\n",
        "exp_mclf101 = setup(data = data, target = 'salary', session_id=123)\n",
        "\n",
        "best = compare_models()\n",
        "print(\"x\")"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<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>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Prec.</th>\n",
              "      <th>F1</th>\n",
              "      <th>Kappa</th>\n",
              "      <th>MCC</th>\n",
              "      <th>TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>lightgbm</th>\n",
              "      <td>Light Gradient Boosting Machine</td>\n",
              "      <td>0.8716</td>\n",
              "      <td>0.9254</td>\n",
              "      <td>0.6477</td>\n",
              "      <td>0.7791</td>\n",
              "      <td>0.7071</td>\n",
              "      <td>0.6258</td>\n",
              "      <td>0.6303</td>\n",
              "      <td>0.384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gbc</th>\n",
              "      <td>Gradient Boosting Classifier</td>\n",
              "      <td>0.8643</td>\n",
              "      <td>0.9168</td>\n",
              "      <td>0.5802</td>\n",
              "      <td>0.7979</td>\n",
              "      <td>0.6716</td>\n",
              "      <td>0.5887</td>\n",
              "      <td>0.6008</td>\n",
              "      <td>4.217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ada</th>\n",
              "      <td>Ada Boost Classifier</td>\n",
              "      <td>0.8599</td>\n",
              "      <td>0.9117</td>\n",
              "      <td>0.6108</td>\n",
              "      <td>0.7576</td>\n",
              "      <td>0.6761</td>\n",
              "      <td>0.5880</td>\n",
              "      <td>0.5937</td>\n",
              "      <td>1.132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rf</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>0.8539</td>\n",
              "      <td>0.9019</td>\n",
              "      <td>0.6154</td>\n",
              "      <td>0.7318</td>\n",
              "      <td>0.6685</td>\n",
              "      <td>0.5757</td>\n",
              "      <td>0.5793</td>\n",
              "      <td>2.673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lda</th>\n",
              "      <td>Linear Discriminant Analysis</td>\n",
              "      <td>0.8403</td>\n",
              "      <td>0.8902</td>\n",
              "      <td>0.5542</td>\n",
              "      <td>0.7145</td>\n",
              "      <td>0.6241</td>\n",
              "      <td>0.5248</td>\n",
              "      <td>0.5317</td>\n",
              "      <td>0.609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ridge</th>\n",
              "      <td>Ridge Classifier</td>\n",
              "      <td>0.8386</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.4940</td>\n",
              "      <td>0.7461</td>\n",
              "      <td>0.5943</td>\n",
              "      <td>0.4987</td>\n",
              "      <td>0.5153</td>\n",
              "      <td>0.052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>et</th>\n",
              "      <td>Extra Trees Classifier</td>\n",
              "      <td>0.8335</td>\n",
              "      <td>0.8769</td>\n",
              "      <td>0.5934</td>\n",
              "      <td>0.6723</td>\n",
              "      <td>0.6303</td>\n",
              "      <td>0.5234</td>\n",
              "      <td>0.5251</td>\n",
              "      <td>3.137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dt</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>0.8101</td>\n",
              "      <td>0.7424</td>\n",
              "      <td>0.6127</td>\n",
              "      <td>0.6013</td>\n",
              "      <td>0.6069</td>\n",
              "      <td>0.4817</td>\n",
              "      <td>0.4818</td>\n",
              "      <td>0.255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lr</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.8008</td>\n",
              "      <td>0.6081</td>\n",
              "      <td>0.2823</td>\n",
              "      <td>0.7152</td>\n",
              "      <td>0.3991</td>\n",
              "      <td>0.3076</td>\n",
              "      <td>0.3577</td>\n",
              "      <td>0.602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nb</th>\n",
              "      <td>Naive Bayes</td>\n",
              "      <td>0.7958</td>\n",
              "      <td>0.8050</td>\n",
              "      <td>0.3052</td>\n",
              "      <td>0.6588</td>\n",
              "      <td>0.4170</td>\n",
              "      <td>0.3129</td>\n",
              "      <td>0.3470</td>\n",
              "      <td>0.057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>knn</th>\n",
              "      <td>K Neighbors Classifier</td>\n",
              "      <td>0.7749</td>\n",
              "      <td>0.6622</td>\n",
              "      <td>0.3010</td>\n",
              "      <td>0.5550</td>\n",
              "      <td>0.3902</td>\n",
              "      <td>0.2668</td>\n",
              "      <td>0.2857</td>\n",
              "      <td>0.743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>svm</th>\n",
              "      <td>SVM - Linear Kernel</td>\n",
              "      <td>0.7354</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2870</td>\n",
              "      <td>0.6938</td>\n",
              "      <td>0.3221</td>\n",
              "      <td>0.2133</td>\n",
              "      <td>0.2761</td>\n",
              "      <td>1.057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>qda</th>\n",
              "      <td>Quadratic Discriminant Analysis</td>\n",
              "      <td>0.6798</td>\n",
              "      <td>0.6065</td>\n",
              "      <td>0.4658</td>\n",
              "      <td>0.3888</td>\n",
              "      <td>0.4053</td>\n",
              "      <td>0.1979</td>\n",
              "      <td>0.2092</td>\n",
              "      <td>0.353</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                    Model  Accuracy     AUC  Recall   Prec.  \\\n",
              "lightgbm  Light Gradient Boosting Machine    0.8716  0.9254  0.6477  0.7791   \n",
              "gbc          Gradient Boosting Classifier    0.8643  0.9168  0.5802  0.7979   \n",
              "ada                  Ada Boost Classifier    0.8599  0.9117  0.6108  0.7576   \n",
              "rf               Random Forest Classifier    0.8539  0.9019  0.6154  0.7318   \n",
              "lda          Linear Discriminant Analysis    0.8403  0.8902  0.5542  0.7145   \n",
              "ridge                    Ridge Classifier    0.8386  0.0000  0.4940  0.7461   \n",
              "et                 Extra Trees Classifier    0.8335  0.8769  0.5934  0.6723   \n",
              "dt               Decision Tree Classifier    0.8101  0.7424  0.6127  0.6013   \n",
              "lr                    Logistic Regression    0.8008  0.6081  0.2823  0.7152   \n",
              "nb                            Naive Bayes    0.7958  0.8050  0.3052  0.6588   \n",
              "knn                K Neighbors Classifier    0.7749  0.6622  0.3010  0.5550   \n",
              "svm                   SVM - Linear Kernel    0.7354  0.0000  0.2870  0.6938   \n",
              "qda       Quadratic Discriminant Analysis    0.6798  0.6065  0.4658  0.3888   \n",
              "\n",
              "              F1   Kappa     MCC  TT (Sec)  \n",
              "lightgbm  0.7071  0.6258  0.6303     0.384  \n",
              "gbc       0.6716  0.5887  0.6008     4.217  \n",
              "ada       0.6761  0.5880  0.5937     1.132  \n",
              "rf        0.6685  0.5757  0.5793     2.673  \n",
              "lda       0.6241  0.5248  0.5317     0.609  \n",
              "ridge     0.5943  0.4987  0.5153     0.052  \n",
              "et        0.6303  0.5234  0.5251     3.137  \n",
              "dt        0.6069  0.4817  0.4818     0.255  \n",
              "lr        0.3991  0.3076  0.3577     0.602  \n",
              "nb        0.4170  0.3129  0.3470     0.057  \n",
              "knn       0.3902  0.2668  0.2857     0.743  \n",
              "svm       0.3221  0.2133  0.2761     1.057  \n",
              "qda       0.4053  0.1979  0.2092     0.353  "
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "x\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421,
          "referenced_widgets": [
            "a8ce769723c546159c0b4a27c17588ce",
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          ]
        },
        "id": "mnuXCMTQsszd",
        "outputId": "18e7d4c4-2f30-4cfa-fc4e-43f213015686"
      },
      "source": [
        "lightgbm = create_model('lightgbm')\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Prec.</th>\n",
              "      <th>F1</th>\n",
              "      <th>Kappa</th>\n",
              "      <th>MCC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.8851</td>\n",
              "      <td>0.9342</td>\n",
              "      <td>0.6703</td>\n",
              "      <td>0.8170</td>\n",
              "      <td>0.7364</td>\n",
              "      <td>0.6639</td>\n",
              "      <td>0.6692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.8684</td>\n",
              "      <td>0.9257</td>\n",
              "      <td>0.6703</td>\n",
              "      <td>0.7531</td>\n",
              "      <td>0.7093</td>\n",
              "      <td>0.6246</td>\n",
              "      <td>0.6264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.8846</td>\n",
              "      <td>0.9328</td>\n",
              "      <td>0.6752</td>\n",
              "      <td>0.8106</td>\n",
              "      <td>0.7367</td>\n",
              "      <td>0.6636</td>\n",
              "      <td>0.6682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.8587</td>\n",
              "      <td>0.9171</td>\n",
              "      <td>0.6294</td>\n",
              "      <td>0.7408</td>\n",
              "      <td>0.6806</td>\n",
              "      <td>0.5906</td>\n",
              "      <td>0.5939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.8653</td>\n",
              "      <td>0.9197</td>\n",
              "      <td>0.6385</td>\n",
              "      <td>0.7598</td>\n",
              "      <td>0.6939</td>\n",
              "      <td>0.6084</td>\n",
              "      <td>0.6122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.8600</td>\n",
              "      <td>0.9240</td>\n",
              "      <td>0.6092</td>\n",
              "      <td>0.7580</td>\n",
              "      <td>0.6755</td>\n",
              "      <td>0.5876</td>\n",
              "      <td>0.5933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.8732</td>\n",
              "      <td>0.9279</td>\n",
              "      <td>0.6330</td>\n",
              "      <td>0.7949</td>\n",
              "      <td>0.7048</td>\n",
              "      <td>0.6254</td>\n",
              "      <td>0.6319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.8732</td>\n",
              "      <td>0.9250</td>\n",
              "      <td>0.6575</td>\n",
              "      <td>0.7787</td>\n",
              "      <td>0.7130</td>\n",
              "      <td>0.6324</td>\n",
              "      <td>0.6361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.8741</td>\n",
              "      <td>0.9214</td>\n",
              "      <td>0.6337</td>\n",
              "      <td>0.7991</td>\n",
              "      <td>0.7068</td>\n",
              "      <td>0.6280</td>\n",
              "      <td>0.6349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.8736</td>\n",
              "      <td>0.9259</td>\n",
              "      <td>0.6593</td>\n",
              "      <td>0.7792</td>\n",
              "      <td>0.7143</td>\n",
              "      <td>0.6339</td>\n",
              "      <td>0.6375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>0.8716</td>\n",
              "      <td>0.9254</td>\n",
              "      <td>0.6477</td>\n",
              "      <td>0.7791</td>\n",
              "      <td>0.7071</td>\n",
              "      <td>0.6258</td>\n",
              "      <td>0.6303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>0.0085</td>\n",
              "      <td>0.0051</td>\n",
              "      <td>0.0208</td>\n",
              "      <td>0.0246</td>\n",
              "      <td>0.0193</td>\n",
              "      <td>0.0245</td>\n",
              "      <td>0.0247</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Accuracy     AUC  Recall   Prec.      F1   Kappa     MCC\n",
              "0       0.8851  0.9342  0.6703  0.8170  0.7364  0.6639  0.6692\n",
              "1       0.8684  0.9257  0.6703  0.7531  0.7093  0.6246  0.6264\n",
              "2       0.8846  0.9328  0.6752  0.8106  0.7367  0.6636  0.6682\n",
              "3       0.8587  0.9171  0.6294  0.7408  0.6806  0.5906  0.5939\n",
              "4       0.8653  0.9197  0.6385  0.7598  0.6939  0.6084  0.6122\n",
              "5       0.8600  0.9240  0.6092  0.7580  0.6755  0.5876  0.5933\n",
              "6       0.8732  0.9279  0.6330  0.7949  0.7048  0.6254  0.6319\n",
              "7       0.8732  0.9250  0.6575  0.7787  0.7130  0.6324  0.6361\n",
              "8       0.8741  0.9214  0.6337  0.7991  0.7068  0.6280  0.6349\n",
              "9       0.8736  0.9259  0.6593  0.7792  0.7143  0.6339  0.6375\n",
              "Mean    0.8716  0.9254  0.6477  0.7791  0.7071  0.6258  0.6303\n",
              "SD      0.0085  0.0051  0.0208  0.0246  0.0193  0.0245  0.0247"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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          "height": 421,
          "referenced_widgets": [
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        },
        "id": "ZeDUy0HPs2gh",
        "outputId": "eafe280f-af66-4f67-e926-11e95155e620"
      },
      "source": [
        "tuned_lightgbm = tune_model(lightgbm)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Prec.</th>\n",
              "      <th>F1</th>\n",
              "      <th>Kappa</th>\n",
              "      <th>MCC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.8715</td>\n",
              "      <td>0.9301</td>\n",
              "      <td>0.6374</td>\n",
              "      <td>0.7856</td>\n",
              "      <td>0.7037</td>\n",
              "      <td>0.6228</td>\n",
              "      <td>0.6284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.8689</td>\n",
              "      <td>0.9227</td>\n",
              "      <td>0.6703</td>\n",
              "      <td>0.7546</td>\n",
              "      <td>0.7100</td>\n",
              "      <td>0.6256</td>\n",
              "      <td>0.6275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.8798</td>\n",
              "      <td>0.9315</td>\n",
              "      <td>0.6642</td>\n",
              "      <td>0.7991</td>\n",
              "      <td>0.7255</td>\n",
              "      <td>0.6493</td>\n",
              "      <td>0.6539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.8605</td>\n",
              "      <td>0.9168</td>\n",
              "      <td>0.6275</td>\n",
              "      <td>0.7484</td>\n",
              "      <td>0.6826</td>\n",
              "      <td>0.5941</td>\n",
              "      <td>0.5979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.8719</td>\n",
              "      <td>0.9199</td>\n",
              "      <td>0.6550</td>\n",
              "      <td>0.7744</td>\n",
              "      <td>0.7097</td>\n",
              "      <td>0.6283</td>\n",
              "      <td>0.6319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.8574</td>\n",
              "      <td>0.9208</td>\n",
              "      <td>0.6037</td>\n",
              "      <td>0.7511</td>\n",
              "      <td>0.6694</td>\n",
              "      <td>0.5798</td>\n",
              "      <td>0.5855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.8745</td>\n",
              "      <td>0.9247</td>\n",
              "      <td>0.6385</td>\n",
              "      <td>0.7963</td>\n",
              "      <td>0.7088</td>\n",
              "      <td>0.6300</td>\n",
              "      <td>0.6362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.8701</td>\n",
              "      <td>0.9211</td>\n",
              "      <td>0.6465</td>\n",
              "      <td>0.7741</td>\n",
              "      <td>0.7046</td>\n",
              "      <td>0.6222</td>\n",
              "      <td>0.6264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.8684</td>\n",
              "      <td>0.9177</td>\n",
              "      <td>0.6172</td>\n",
              "      <td>0.7874</td>\n",
              "      <td>0.6920</td>\n",
              "      <td>0.6098</td>\n",
              "      <td>0.6172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.8627</td>\n",
              "      <td>0.9234</td>\n",
              "      <td>0.6429</td>\n",
              "      <td>0.7484</td>\n",
              "      <td>0.6916</td>\n",
              "      <td>0.6039</td>\n",
              "      <td>0.6068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>0.8686</td>\n",
              "      <td>0.9229</td>\n",
              "      <td>0.6403</td>\n",
              "      <td>0.7719</td>\n",
              "      <td>0.6998</td>\n",
              "      <td>0.6166</td>\n",
              "      <td>0.6212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>0.0064</td>\n",
              "      <td>0.0046</td>\n",
              "      <td>0.0195</td>\n",
              "      <td>0.0190</td>\n",
              "      <td>0.0153</td>\n",
              "      <td>0.0190</td>\n",
              "      <td>0.0189</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Accuracy     AUC  Recall   Prec.      F1   Kappa     MCC\n",
              "0       0.8715  0.9301  0.6374  0.7856  0.7037  0.6228  0.6284\n",
              "1       0.8689  0.9227  0.6703  0.7546  0.7100  0.6256  0.6275\n",
              "2       0.8798  0.9315  0.6642  0.7991  0.7255  0.6493  0.6539\n",
              "3       0.8605  0.9168  0.6275  0.7484  0.6826  0.5941  0.5979\n",
              "4       0.8719  0.9199  0.6550  0.7744  0.7097  0.6283  0.6319\n",
              "5       0.8574  0.9208  0.6037  0.7511  0.6694  0.5798  0.5855\n",
              "6       0.8745  0.9247  0.6385  0.7963  0.7088  0.6300  0.6362\n",
              "7       0.8701  0.9211  0.6465  0.7741  0.7046  0.6222  0.6264\n",
              "8       0.8684  0.9177  0.6172  0.7874  0.6920  0.6098  0.6172\n",
              "9       0.8627  0.9234  0.6429  0.7484  0.6916  0.6039  0.6068\n",
              "Mean    0.8686  0.9229  0.6403  0.7719  0.6998  0.6166  0.6212\n",
              "SD      0.0064  0.0046  0.0195  0.0190  0.0153  0.0190  0.0189"
            ]
          },
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            "tags": []
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      },
      "source": [
        "evaluate_model(tuned_lightgbm)\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b0c2aea6ecb34b2085ae87ac457c5100",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Hyperparameters', 'param…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cppaMhtgtOad"
      },
      "source": [
        "final_gbm = finalize_model(tuned_lightgbm)\n",
        "# save_model(final_gbm, 'Final GBM Model')"
      ],
      "execution_count": 13,
      "outputs": []
    }
  ]
}