lazypredict.ipynb

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  "metadata": {
    "colab": {
      "name": "lazypredict.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GIx4mcFnGi3B"
      },
      "source": [
        "# **How to quickly compare ML models using lazypredict**\n",
        "\n",
        "Chanin Nantasenamat\n",
        "\n",
        "[Data Professor](http://youtube.com/dataprofessor), http://youtube.com/dataprofessor\n",
        "\n",
        "**Reference:**\n",
        "- Author of lazypredict is Shankar Rao Pandala\n",
        "- [Documentation](https://lazypredict.readthedocs.io/) of lazypredict\n",
        "- [GitHub](https://github.com/shankarpandala/lazypredict) of lazypredict"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f7vrCrocIuDu"
      },
      "source": [
        "# **Install the lazypredict library**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xADnHVMnGdeV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90192603-e24e-4bd9-bf07-80438771970b"
      },
      "source": [
        "! pip install lazypredict"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: lazypredict in /usr/local/lib/python3.6/dist-packages (0.2.7)\n",
            "Requirement already satisfied: Click>=7.0 in /usr/local/lib/python3.6/dist-packages (from lazypredict) (7.1.2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8oIWpXkr4SSt"
      },
      "source": [
        "#! git clone https://github.com/shankarpandala/lazypredict.git\n",
        "#% cd lazypredict\n",
        "#! python setup.py install"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yQIXyWeGHoVB"
      },
      "source": [
        "# **Classification**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "X-YU5ZMCHNHc",
        "outputId": "48214ad7-1243-42c8-d124-626ef7e39b3a"
      },
      "source": [
        "# Import libraries\n",
        "import lazypredict\n",
        "from lazypredict.Supervised import LazyClassifier\n",
        "from sklearn.datasets import load_breast_cancer\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Load dataset\n",
        "data = load_breast_cancer()\n",
        "X = data.data\n",
        "y= data.target\n",
        "\n",
        "# Data split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=.2,random_state =42)\n",
        "\n",
        "# Defines and builds the lazyclassifier\n",
        "clf = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric=None)\n",
        "models_train,predictions_train = clf.fit(X_train, X_train, y_train, y_train)\n",
        "models_test,predictions_test = clf.fit(X_train, X_test, y_train, y_test)\n",
        "\n",
        "# Prints the model performance\n",
        "models_train"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.utils.testing module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.utils. Anything that cannot be imported from sklearn.utils is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n",
            "100%|██████████| 30/30 [00:01<00:00, 18.08it/s]\n",
            "100%|██████████| 30/30 [00:01<00:00, 21.78it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
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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>Balanced Accuracy</th>\n",
              "      <th>ROC AUC</th>\n",
              "      <th>F1 Score</th>\n",
              "      <th>Time Taken</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>AdaBoostClassifier</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreeClassifier</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGBClassifier</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForestClassifier</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LabelSpreading</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LabelPropagation</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreesClassifier</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LGBMClassifier</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DecisionTreeClassifier</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BaggingClassifier</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVC</th>\n",
              "      <td>0.99</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearSVC</th>\n",
              "      <td>0.99</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LogisticRegression</th>\n",
              "      <td>0.99</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassiveAggressiveClassifier</th>\n",
              "      <td>0.98</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SGDClassifier</th>\n",
              "      <td>0.98</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Perceptron</th>\n",
              "      <td>0.98</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNeighborsClassifier</th>\n",
              "      <td>0.98</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QuadraticDiscriminantAnalysis</th>\n",
              "      <td>0.98</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CalibratedClassifierCV</th>\n",
              "      <td>0.97</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeClassifier</th>\n",
              "      <td>0.97</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeClassifierCV</th>\n",
              "      <td>0.97</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearDiscriminantAnalysis</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NuSVC</th>\n",
              "      <td>0.95</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianNB</th>\n",
              "      <td>0.94</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BernoulliNB</th>\n",
              "      <td>0.93</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NearestCentroid</th>\n",
              "      <td>0.93</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DummyClassifier</th>\n",
              "      <td>0.56</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CheckingClassifier</th>\n",
              "      <td>0.37</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               Accuracy  ...  Time Taken\n",
              "Model                                    ...            \n",
              "AdaBoostClassifier                 1.00  ...        0.18\n",
              "ExtraTreeClassifier                1.00  ...        0.02\n",
              "XGBClassifier                      1.00  ...        0.15\n",
              "RandomForestClassifier             1.00  ...        0.27\n",
              "LabelSpreading                     1.00  ...        0.05\n",
              "LabelPropagation                   1.00  ...        0.05\n",
              "ExtraTreesClassifier               1.00  ...        0.15\n",
              "LGBMClassifier                     1.00  ...        0.18\n",
              "DecisionTreeClassifier             1.00  ...        0.02\n",
              "BaggingClassifier                  1.00  ...        0.07\n",
              "SVC                                0.99  ...        0.02\n",
              "LinearSVC                          0.99  ...        0.05\n",
              "LogisticRegression                 0.99  ...        0.06\n",
              "PassiveAggressiveClassifier        0.98  ...        0.03\n",
              "SGDClassifier                      0.98  ...        0.02\n",
              "Perceptron                         0.98  ...        0.02\n",
              "KNeighborsClassifier               0.98  ...        0.04\n",
              "QuadraticDiscriminantAnalysis      0.98  ...        0.02\n",
              "CalibratedClassifierCV             0.97  ...        0.05\n",
              "RidgeClassifier                    0.97  ...        0.02\n",
              "RidgeClassifierCV                  0.97  ...        0.02\n",
              "LinearDiscriminantAnalysis         0.96  ...        0.03\n",
              "NuSVC                              0.95  ...        0.04\n",
              "GaussianNB                         0.94  ...        0.02\n",
              "BernoulliNB                        0.93  ...        0.01\n",
              "NearestCentroid                    0.93  ...        0.02\n",
              "DummyClassifier                    0.56  ...        0.02\n",
              "CheckingClassifier                 0.37  ...        0.02\n",
              "\n",
              "[28 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 948
        },
        "id": "d4flfZcvMv4a",
        "outputId": "aa3fe6b6-529a-4cd1-bf67-2e778fb0106d"
      },
      "source": [
        "models_test"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Balanced Accuracy</th>\n",
              "      <th>ROC AUC</th>\n",
              "      <th>F1 Score</th>\n",
              "      <th>Time Taken</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>BernoulliNB</th>\n",
              "      <td>0.98</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassiveAggressiveClassifier</th>\n",
              "      <td>0.98</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVC</th>\n",
              "      <td>0.98</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Perceptron</th>\n",
              "      <td>0.97</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostClassifier</th>\n",
              "      <td>0.97</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LogisticRegression</th>\n",
              "      <td>0.97</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SGDClassifier</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreeClassifier</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CalibratedClassifierCV</th>\n",
              "      <td>0.97</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGBClassifier</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForestClassifier</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LGBMClassifier</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianNB</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreesClassifier</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QuadraticDiscriminantAnalysis</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearSVC</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BaggingClassifier</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearDiscriminantAnalysis</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NearestCentroid</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NuSVC</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeClassifier</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeClassifierCV</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNeighborsClassifier</th>\n",
              "      <td>0.95</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DecisionTreeClassifier</th>\n",
              "      <td>0.95</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LabelSpreading</th>\n",
              "      <td>0.94</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LabelPropagation</th>\n",
              "      <td>0.94</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DummyClassifier</th>\n",
              "      <td>0.57</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CheckingClassifier</th>\n",
              "      <td>0.38</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               Accuracy  ...  Time Taken\n",
              "Model                                    ...            \n",
              "BernoulliNB                        0.98  ...        0.01\n",
              "PassiveAggressiveClassifier        0.98  ...        0.02\n",
              "SVC                                0.98  ...        0.02\n",
              "Perceptron                         0.97  ...        0.01\n",
              "AdaBoostClassifier                 0.97  ...        0.17\n",
              "LogisticRegression                 0.97  ...        0.03\n",
              "SGDClassifier                      0.96  ...        0.01\n",
              "ExtraTreeClassifier                0.96  ...        0.01\n",
              "CalibratedClassifierCV             0.97  ...        0.04\n",
              "XGBClassifier                      0.96  ...        0.14\n",
              "RandomForestClassifier             0.96  ...        0.25\n",
              "LGBMClassifier                     0.96  ...        0.17\n",
              "GaussianNB                         0.96  ...        0.01\n",
              "ExtraTreesClassifier               0.96  ...        0.13\n",
              "QuadraticDiscriminantAnalysis      0.96  ...        0.02\n",
              "LinearSVC                          0.96  ...        0.03\n",
              "BaggingClassifier                  0.96  ...        0.06\n",
              "LinearDiscriminantAnalysis         0.96  ...        0.02\n",
              "NearestCentroid                    0.96  ...        0.01\n",
              "NuSVC                              0.96  ...        0.04\n",
              "RidgeClassifier                    0.96  ...        0.02\n",
              "RidgeClassifierCV                  0.96  ...        0.02\n",
              "KNeighborsClassifier               0.95  ...        0.02\n",
              "DecisionTreeClassifier             0.95  ...        0.02\n",
              "LabelSpreading                     0.94  ...        0.03\n",
              "LabelPropagation                   0.94  ...        0.03\n",
              "DummyClassifier                    0.57  ...        0.01\n",
              "CheckingClassifier                 0.38  ...        0.01\n",
              "\n",
              "[28 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "id": "lqz4ojulQtuE",
        "outputId": "574d1ca1-194d-4a77-82df-ea66b5cc87a2"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "plt.figure(figsize=(10, 5))\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "ax = sns.barplot(x=models_train.index, y=\"Accuracy\", data=models_train)\n",
        "plt.xticks(rotation=90)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
              "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]),\n",
              " <a list of 28 Text major ticklabel objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "id": "3FuaerPLRfhj",
        "outputId": "5ee3ed14-0893-420d-f105-03661f4c7a04"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "plt.figure(figsize=(5, 10))\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "ax = sns.barplot(y=models_train.index, x=\"Accuracy\", data=models_train)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8k_FHiZ7Htuk"
      },
      "source": [
        "# **Regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_9Nb22TeHn8i",
        "outputId": "1193311e-9885-4e0e-adf4-90a01f2984fc"
      },
      "source": [
        "# Import libraries\n",
        "from lazypredict.Supervised import LazyRegressor\n",
        "from sklearn import datasets\n",
        "from sklearn.utils import shuffle\n",
        "import numpy as np\n",
        "\n",
        "# Load dataset\n",
        "boston = datasets.load_boston()\n",
        "X, y = shuffle(boston.data, boston.target, random_state=42)\n",
        "\n",
        "# Data split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=.2,random_state =42)\n",
        "\n",
        "# Defines and builds the lazyclassifier\n",
        "reg = LazyRegressor(verbose=0,ignore_warnings=False, custom_metric=None)\n",
        "models_train,predictions_train = reg.fit(X_train, X_train, y_train, y_train)\n",
        "models_test,predictions_test = reg.fit(X_train, X_test, y_train, y_test)\n",
        "\n",
        "# Prints the model performance\n",
        "models_train"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 39/39 [00:03<00:00, 12.77it/s]\n",
            "  0%|          | 0/39 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "StackingRegressor model failed to execute\n",
            "__init__() missing 1 required positional argument: 'estimators'\n",
            "[04:15:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 39/39 [00:02<00:00, 13.04it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "StackingRegressor model failed to execute\n",
            "__init__() missing 1 required positional argument: 'estimators'\n",
            "[04:15:17] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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>R-Squared</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>Time Taken</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>DecisionTreeRegressor</th>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreeRegressor</th>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreesRegressor</th>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianProcessRegressor</th>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForestRegressor</th>\n",
              "      <td>0.98</td>\n",
              "      <td>1.27</td>\n",
              "      <td>0.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HistGradientBoostingRegressor</th>\n",
              "      <td>0.98</td>\n",
              "      <td>1.36</td>\n",
              "      <td>0.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GradientBoostingRegressor</th>\n",
              "      <td>0.98</td>\n",
              "      <td>1.38</td>\n",
              "      <td>0.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LGBMRegressor</th>\n",
              "      <td>0.98</td>\n",
              "      <td>1.39</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGBRegressor</th>\n",
              "      <td>0.98</td>\n",
              "      <td>1.44</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BaggingRegressor</th>\n",
              "      <td>0.97</td>\n",
              "      <td>1.49</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostRegressor</th>\n",
              "      <td>0.89</td>\n",
              "      <td>3.06</td>\n",
              "      <td>0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNeighborsRegressor</th>\n",
              "      <td>0.84</td>\n",
              "      <td>3.74</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearRegression</th>\n",
              "      <td>0.74</td>\n",
              "      <td>4.79</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TransformedTargetRegressor</th>\n",
              "      <td>0.74</td>\n",
              "      <td>4.79</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ridge</th>\n",
              "      <td>0.74</td>\n",
              "      <td>4.79</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsIC</th>\n",
              "      <td>0.74</td>\n",
              "      <td>4.80</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BayesianRidge</th>\n",
              "      <td>0.74</td>\n",
              "      <td>4.80</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeCV</th>\n",
              "      <td>0.73</td>\n",
              "      <td>4.80</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsCV</th>\n",
              "      <td>0.73</td>\n",
              "      <td>4.80</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoCV</th>\n",
              "      <td>0.73</td>\n",
              "      <td>4.80</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNetCV</th>\n",
              "      <td>0.73</td>\n",
              "      <td>4.81</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SGDRegressor</th>\n",
              "      <td>0.73</td>\n",
              "      <td>4.81</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MLPRegressor</th>\n",
              "      <td>0.71</td>\n",
              "      <td>5.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HuberRegressor</th>\n",
              "      <td>0.71</td>\n",
              "      <td>5.03</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuitCV</th>\n",
              "      <td>0.69</td>\n",
              "      <td>5.15</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearSVR</th>\n",
              "      <td>0.69</td>\n",
              "      <td>5.16</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVR</th>\n",
              "      <td>0.67</td>\n",
              "      <td>5.37</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NuSVR</th>\n",
              "      <td>0.67</td>\n",
              "      <td>5.38</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lasso</th>\n",
              "      <td>0.66</td>\n",
              "      <td>5.46</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNet</th>\n",
              "      <td>0.64</td>\n",
              "      <td>5.59</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LarsCV</th>\n",
              "      <td>0.62</td>\n",
              "      <td>5.74</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RANSACRegressor</th>\n",
              "      <td>0.55</td>\n",
              "      <td>6.23</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lars</th>\n",
              "      <td>0.55</td>\n",
              "      <td>6.24</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuit</th>\n",
              "      <td>0.54</td>\n",
              "      <td>6.33</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassiveAggressiveRegressor</th>\n",
              "      <td>0.33</td>\n",
              "      <td>7.62</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DummyRegressor</th>\n",
              "      <td>0.00</td>\n",
              "      <td>9.32</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLars</th>\n",
              "      <td>0.00</td>\n",
              "      <td>9.32</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KernelRidge</th>\n",
              "      <td>-5.16</td>\n",
              "      <td>23.13</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               R-Squared  RMSE  Time Taken\n",
              "Model                                                     \n",
              "DecisionTreeRegressor               1.00  0.00        0.01\n",
              "ExtraTreeRegressor                  1.00  0.00        0.01\n",
              "ExtraTreesRegressor                 1.00  0.00        0.21\n",
              "GaussianProcessRegressor            1.00  0.00        0.05\n",
              "RandomForestRegressor               0.98  1.27        0.34\n",
              "HistGradientBoostingRegressor       0.98  1.36        0.26\n",
              "GradientBoostingRegressor           0.98  1.38        0.17\n",
              "LGBMRegressor                       0.98  1.39        0.06\n",
              "XGBRegressor                        0.98  1.44        0.05\n",
              "BaggingRegressor                    0.97  1.49        0.05\n",
              "AdaBoostRegressor                   0.89  3.06        0.10\n",
              "KNeighborsRegressor                 0.84  3.74        0.02\n",
              "LinearRegression                    0.74  4.79        0.01\n",
              "TransformedTargetRegressor          0.74  4.79        0.01\n",
              "Ridge                               0.74  4.79        0.01\n",
              "LassoLarsIC                         0.74  4.80        0.01\n",
              "BayesianRidge                       0.74  4.80        0.01\n",
              "RidgeCV                             0.73  4.80        0.01\n",
              "LassoLarsCV                         0.73  4.80        0.03\n",
              "LassoCV                             0.73  4.80        0.08\n",
              "ElasticNetCV                        0.73  4.81        0.07\n",
              "SGDRegressor                        0.73  4.81        0.01\n",
              "MLPRegressor                        0.71  5.00        1.00\n",
              "HuberRegressor                      0.71  5.03        0.02\n",
              "OrthogonalMatchingPursuitCV         0.69  5.15        0.03\n",
              "LinearSVR                           0.69  5.16        0.01\n",
              "SVR                                 0.67  5.37        0.03\n",
              "NuSVR                               0.67  5.38        0.04\n",
              "Lasso                               0.66  5.46        0.01\n",
              "ElasticNet                          0.64  5.59        0.01\n",
              "LarsCV                              0.62  5.74        0.07\n",
              "RANSACRegressor                     0.55  6.23        0.07\n",
              "Lars                                0.55  6.24        0.04\n",
              "OrthogonalMatchingPursuit           0.54  6.33        0.02\n",
              "PassiveAggressiveRegressor          0.33  7.62        0.02\n",
              "DummyRegressor                      0.00  9.32        0.01\n",
              "LassoLars                           0.00  9.32        0.01\n",
              "KernelRidge                        -5.16 23.13        0.05"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "NDvTTe2AIO9n",
        "outputId": "af6f42c3-8801-4f41-9de3-b9dcd8540d14"
      },
      "source": [
        "models_test"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>R-Squared</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>Time Taken</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>GradientBoostingRegressor</th>\n",
              "      <td>0.90</td>\n",
              "      <td>2.79</td>\n",
              "      <td>0.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGBRegressor</th>\n",
              "      <td>0.89</td>\n",
              "      <td>2.85</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BaggingRegressor</th>\n",
              "      <td>0.89</td>\n",
              "      <td>2.91</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreesRegressor</th>\n",
              "      <td>0.88</td>\n",
              "      <td>2.98</td>\n",
              "      <td>0.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForestRegressor</th>\n",
              "      <td>0.88</td>\n",
              "      <td>3.01</td>\n",
              "      <td>0.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LGBMRegressor</th>\n",
              "      <td>0.87</td>\n",
              "      <td>3.09</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HistGradientBoostingRegressor</th>\n",
              "      <td>0.87</td>\n",
              "      <td>3.12</td>\n",
              "      <td>0.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostRegressor</th>\n",
              "      <td>0.79</td>\n",
              "      <td>3.95</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DecisionTreeRegressor</th>\n",
              "      <td>0.78</td>\n",
              "      <td>4.02</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsIC</th>\n",
              "      <td>0.75</td>\n",
              "      <td>4.32</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearRegression</th>\n",
              "      <td>0.75</td>\n",
              "      <td>4.32</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TransformedTargetRegressor</th>\n",
              "      <td>0.75</td>\n",
              "      <td>4.32</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ridge</th>\n",
              "      <td>0.75</td>\n",
              "      <td>4.32</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsCV</th>\n",
              "      <td>0.75</td>\n",
              "      <td>4.32</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoCV</th>\n",
              "      <td>0.75</td>\n",
              "      <td>4.32</td>\n",
              "      <td>0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BayesianRidge</th>\n",
              "      <td>0.75</td>\n",
              "      <td>4.33</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeCV</th>\n",
              "      <td>0.75</td>\n",
              "      <td>4.34</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNetCV</th>\n",
              "      <td>0.75</td>\n",
              "      <td>4.34</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SGDRegressor</th>\n",
              "      <td>0.74</td>\n",
              "      <td>4.36</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HuberRegressor</th>\n",
              "      <td>0.73</td>\n",
              "      <td>4.49</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuitCV</th>\n",
              "      <td>0.73</td>\n",
              "      <td>4.49</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreeRegressor</th>\n",
              "      <td>0.72</td>\n",
              "      <td>4.58</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearSVR</th>\n",
              "      <td>0.71</td>\n",
              "      <td>4.63</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MLPRegressor</th>\n",
              "      <td>0.68</td>\n",
              "      <td>4.85</td>\n",
              "      <td>1.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lasso</th>\n",
              "      <td>0.68</td>\n",
              "      <td>4.85</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVR</th>\n",
              "      <td>0.68</td>\n",
              "      <td>4.89</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NuSVR</th>\n",
              "      <td>0.66</td>\n",
              "      <td>4.99</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNeighborsRegressor</th>\n",
              "      <td>0.65</td>\n",
              "      <td>5.11</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNet</th>\n",
              "      <td>0.64</td>\n",
              "      <td>5.18</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RANSACRegressor</th>\n",
              "      <td>0.64</td>\n",
              "      <td>5.21</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuit</th>\n",
              "      <td>0.55</td>\n",
              "      <td>5.76</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LarsCV</th>\n",
              "      <td>0.46</td>\n",
              "      <td>6.36</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassiveAggressiveRegressor</th>\n",
              "      <td>0.43</td>\n",
              "      <td>6.53</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lars</th>\n",
              "      <td>0.29</td>\n",
              "      <td>7.29</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianProcessRegressor</th>\n",
              "      <td>0.02</td>\n",
              "      <td>8.54</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DummyRegressor</th>\n",
              "      <td>-0.00</td>\n",
              "      <td>8.64</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLars</th>\n",
              "      <td>-0.00</td>\n",
              "      <td>8.64</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KernelRidge</th>\n",
              "      <td>-5.93</td>\n",
              "      <td>22.69</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               R-Squared  RMSE  Time Taken\n",
              "Model                                                     \n",
              "GradientBoostingRegressor           0.90  2.79        0.17\n",
              "XGBRegressor                        0.89  2.85        0.05\n",
              "BaggingRegressor                    0.89  2.91        0.05\n",
              "ExtraTreesRegressor                 0.88  2.98        0.19\n",
              "RandomForestRegressor               0.88  3.01        0.36\n",
              "LGBMRegressor                       0.87  3.09        0.07\n",
              "HistGradientBoostingRegressor       0.87  3.12        0.22\n",
              "AdaBoostRegressor                   0.79  3.95        0.11\n",
              "DecisionTreeRegressor               0.78  4.02        0.01\n",
              "LassoLarsIC                         0.75  4.32        0.01\n",
              "LinearRegression                    0.75  4.32        0.01\n",
              "TransformedTargetRegressor          0.75  4.32        0.01\n",
              "Ridge                               0.75  4.32        0.01\n",
              "LassoLarsCV                         0.75  4.32        0.03\n",
              "LassoCV                             0.75  4.32        0.10\n",
              "BayesianRidge                       0.75  4.33        0.01\n",
              "RidgeCV                             0.75  4.34        0.01\n",
              "ElasticNetCV                        0.75  4.34        0.08\n",
              "SGDRegressor                        0.74  4.36        0.01\n",
              "HuberRegressor                      0.73  4.49        0.02\n",
              "OrthogonalMatchingPursuitCV         0.73  4.49        0.04\n",
              "ExtraTreeRegressor                  0.72  4.58        0.01\n",
              "LinearSVR                           0.71  4.63        0.01\n",
              "MLPRegressor                        0.68  4.85        1.03\n",
              "Lasso                               0.68  4.85        0.02\n",
              "SVR                                 0.68  4.89        0.02\n",
              "NuSVR                               0.66  4.99        0.07\n",
              "KNeighborsRegressor                 0.65  5.11        0.01\n",
              "ElasticNet                          0.64  5.18        0.01\n",
              "RANSACRegressor                     0.64  5.21        0.07\n",
              "OrthogonalMatchingPursuit           0.55  5.76        0.02\n",
              "LarsCV                              0.46  6.36        0.05\n",
              "PassiveAggressiveRegressor          0.43  6.53        0.01\n",
              "Lars                                0.29  7.29        0.01\n",
              "GaussianProcessRegressor            0.02  8.54        0.03\n",
              "DummyRegressor                     -0.00  8.64        0.01\n",
              "LassoLars                          -0.00  8.64        0.01\n",
              "KernelRidge                        -5.93 22.69        0.01"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6XCkHXerfp_V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b65862f-8dbe-4e37-c57b-b78f1ee9e6ca"
      },
      "source": [
        "[0 if i < 0 else i for i in models_train.iloc[:,0] ]"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.0,\n",
              " 1.0,\n",
              " 1.0,\n",
              " 1.0,\n",
              " 0.9814901063716168,\n",
              " 0.9788395855664961,\n",
              " 0.9779710698717267,\n",
              " 0.9778833682981953,\n",
              " 0.976012728668103,\n",
              " 0.9745882238522984,\n",
              " 0.8923919346103021,\n",
              " 0.8394978605616494,\n",
              " 0.7359824600988447,\n",
              " 0.7359824600988447,\n",
              " 0.7359614635220281,\n",
              " 0.7354373691130418,\n",
              " 0.7354159740929813,\n",
              " 0.7346832675764373,\n",
              " 0.7346648407614298,\n",
              " 0.7344055960164736,\n",
              " 0.7340743006109769,\n",
              " 0.734020701749627,\n",
              " 0.7128773000938735,\n",
              " 0.7084810170742463,\n",
              " 0.6943735351578431,\n",
              " 0.693437857526475,\n",
              " 0.6686593122745774,\n",
              " 0.666931197579143,\n",
              " 0.6569672127379181,\n",
              " 0.6405232520912076,\n",
              " 0.6204715239248304,\n",
              " 0.5529095592060107,\n",
              " 0.5514712354289272,\n",
              " 0.5396154409921331,\n",
              " 0.3328033887745512,\n",
              " 0.0,\n",
              " 0.0,\n",
              " 0]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "vrJRVP0qJGCd",
        "outputId": "77c535f2-1914-43e8-993a-1903998042fa"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "#train[\"R-Squared\"] = [0 if i < 0 else i for i in train.iloc[:,0] ]\n",
        "\n",
        "plt.figure(figsize=(10, 5))\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "ax = sns.barplot(x=models_train.index, y=\"R-Squared\", data=models_train)\n",
        "ax.set(ylim=(0, 1))\n",
        "plt.xticks(rotation=90)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
              "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
              "        34, 35, 36, 37]), <a list of 38 Text major ticklabel objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "_hvNgopydA-p",
        "outputId": "7c521086-8cfa-48b1-f777-5e5173eb34f9"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "models_train[\"R-Squared\"] = [0 if i < 0 else i for i in models_train.iloc[:,0] ]\n",
        "\n",
        "plt.figure(figsize=(5, 10))\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "ax = sns.barplot(y=models_train.index, x=\"R-Squared\", data=models_train)\n",
        "ax.set(xlim=(0, 1))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.0, 1.0)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bRkmkvxR8OG7"
      },
      "source": [
        ""
      ],
      "execution_count": 11,
      "outputs": []
    }
  ]
}