auto_ml_h2o.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNwd8sCq3Eg3cwemOnHY2nu",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/miller00315/ia_studies/blob/main/auto_ml_h2o.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JA_MP3A_4zO_",
        "outputId": "54c3f02c-b612-420a-8c72-916711e2e3f2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting h2o\n",
            "  Downloading h2o-3.46.0.1-py2.py3-none-any.whl (265.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m265.6/265.6 MB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from h2o) (2.31.0)\n",
            "Requirement already satisfied: tabulate in /usr/local/lib/python3.10/dist-packages (from h2o) (0.9.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->h2o) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->h2o) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->h2o) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->h2o) (2024.2.2)\n",
            "Installing collected packages: h2o\n",
            "Successfully installed h2o-3.46.0.1\n"
          ]
        }
      ],
      "source": [
        "!pip install h2o"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import h2o\n",
        "from h2o.automl import H2OAutoML"
      ],
      "metadata": {
        "id": "UIhGK9mA7FuF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "h2o.init()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "yWIT66WX7yA4",
        "outputId": "51799768-8091-48bb-eaa8-e22d30cd9de0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Checking whether there is an H2O instance running at http://localhost:54321..... not found.\n",
            "Attempting to start a local H2O server...\n",
            "  Java Version: openjdk version \"11.0.22\" 2024-01-16; OpenJDK Runtime Environment (build 11.0.22+7-post-Ubuntu-0ubuntu222.04.1); OpenJDK 64-Bit Server VM (build 11.0.22+7-post-Ubuntu-0ubuntu222.04.1, mixed mode, sharing)\n",
            "  Starting server from /usr/local/lib/python3.10/dist-packages/h2o/backend/bin/h2o.jar\n",
            "  Ice root: /tmp/tmpzh3cfns0\n",
            "  JVM stdout: /tmp/tmpzh3cfns0/h2o_unknownUser_started_from_python.out\n",
            "  JVM stderr: /tmp/tmpzh3cfns0/h2o_unknownUser_started_from_python.err\n",
            "  Server is running at http://127.0.0.1:54321\n",
            "Connecting to H2O server at http://127.0.0.1:54321 ... successful.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "--------------------------  -----------------------------------------------------------------------------------------\n",
              "H2O_cluster_uptime:         03 secs\n",
              "H2O_cluster_timezone:       Etc/UTC\n",
              "H2O_data_parsing_timezone:  UTC\n",
              "H2O_cluster_version:        3.46.0.1\n",
              "H2O_cluster_version_age:    3 days\n",
              "H2O_cluster_name:           H2O_from_python_unknownUser_af051u\n",
              "H2O_cluster_total_nodes:    1\n",
              "H2O_cluster_free_memory:    3.170 Gb\n",
              "H2O_cluster_total_cores:    2\n",
              "H2O_cluster_allowed_cores:  2\n",
              "H2O_cluster_status:         locked, healthy\n",
              "H2O_connection_url:         http://127.0.0.1:54321\n",
              "H2O_connection_proxy:       {\"http\": null, \"https\": null, \"colab_language_server\": \"/usr/colab/bin/language_service\"}\n",
              "H2O_internal_security:      False\n",
              "Python_version:             3.10.12 final\n",
              "--------------------------  -----------------------------------------------------------------------------------------"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "\n",
              "#h2o-table-1.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-1 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-1 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-1 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-1 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-1 .h2o-table th,\n",
              "#h2o-table-1 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-1 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-1\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption></caption>\n",
              "    <thead></thead>\n",
              "    <tbody><tr><td>H2O_cluster_uptime:</td>\n",
              "<td>03 secs</td></tr>\n",
              "<tr><td>H2O_cluster_timezone:</td>\n",
              "<td>Etc/UTC</td></tr>\n",
              "<tr><td>H2O_data_parsing_timezone:</td>\n",
              "<td>UTC</td></tr>\n",
              "<tr><td>H2O_cluster_version:</td>\n",
              "<td>3.46.0.1</td></tr>\n",
              "<tr><td>H2O_cluster_version_age:</td>\n",
              "<td>3 days</td></tr>\n",
              "<tr><td>H2O_cluster_name:</td>\n",
              "<td>H2O_from_python_unknownUser_af051u</td></tr>\n",
              "<tr><td>H2O_cluster_total_nodes:</td>\n",
              "<td>1</td></tr>\n",
              "<tr><td>H2O_cluster_free_memory:</td>\n",
              "<td>3.170 Gb</td></tr>\n",
              "<tr><td>H2O_cluster_total_cores:</td>\n",
              "<td>2</td></tr>\n",
              "<tr><td>H2O_cluster_allowed_cores:</td>\n",
              "<td>2</td></tr>\n",
              "<tr><td>H2O_cluster_status:</td>\n",
              "<td>locked, healthy</td></tr>\n",
              "<tr><td>H2O_connection_url:</td>\n",
              "<td>http://127.0.0.1:54321</td></tr>\n",
              "<tr><td>H2O_connection_proxy:</td>\n",
              "<td>{\"http\": null, \"https\": null, \"colab_language_server\": \"/usr/colab/bin/language_service\"}</td></tr>\n",
              "<tr><td>H2O_internal_security:</td>\n",
              "<td>False</td></tr>\n",
              "<tr><td>Python_version:</td>\n",
              "<td>3.10.12 final</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = h2o.import_file('insurance.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_7ZNhas-8cd-",
        "outputId": "c0e98239-4f02-449c-914f-0407e8196050"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "id": "0GdP7hMO79jE",
        "outputId": "f9b6f730-939e-4288-e4b9-373e1849e5a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  C1  GoodStudent    Age         SocioEcon    RiskAversion    VehicleYear    ThisCarDam    RuggedAuto    Accident    MakeModel    DrivQuality    Mileage     Antilock    DrivingSkill    SeniorTrain    ThisCarCost    Theft    CarValue    HomeBase    AntiTheft    PropCost     OtherCarCost    OtherCar    MedCost    Cushioning    Airbag    ILiCost    DrivHist\n",
              "----  -------------  ----------  -----------  --------------  -------------  ------------  ------------  ----------  -----------  -------------  ----------  ----------  --------------  -------------  -------------  -------  ----------  ----------  -----------  -----------  --------------  ----------  ---------  ------------  --------  ---------  ----------\n",
              "   1  False          Adult       Prole        Adventurous     Older          Moderate      EggShell      Mild        Economy      Poor           TwentyThou  False       SubStandard     False          TenThou        False    FiveThou    City        False        TenThou      Thousand        True        Thousand   Poor          False     Thousand   Many\n",
              "   2  False          Senior      Prole        Cautious        Current        None          Football      None        Economy      Normal         TwentyThou  False       Normal          True           Thousand       False    TenThou     City        True         Thousand     Thousand        True        Thousand   Good          True      Thousand   Zero\n",
              "   3  False          Senior      UpperMiddle  Psychopath      Current        None          Football      None        FamilySedan  Excellent      Domino      True        Normal          False          Thousand       False    TwentyThou  City        False        Thousand     Thousand        False       Thousand   Good          True      Thousand   One\n",
              "   4  False          Adolescent  Middle       Normal          Older          None          EggShell      None        Economy      Normal         FiftyThou   False       Normal          False          Thousand       False    FiveThou    Suburb      False        Thousand     Thousand        True        Thousand   Fair          False     Thousand   Zero\n",
              "   5  False          Adolescent  Prole        Normal          Older          Moderate      Football      Moderate    Economy      Poor           FiftyThou   False       SubStandard     False          TenThou        False    FiveThou    City        False        TenThou      Thousand        False       Thousand   Fair          False     Thousand   Many\n",
              "   6  False          Adult       UpperMiddle  Normal          Current        Moderate      EggShell      Moderate    SportsCar    Poor           FiftyThou   True        SubStandard     False          HundredThou    False    TwentyThou  Suburb      True         HundredThou  HundredThou     True        TenThou    Poor          True      Thousand   Many\n",
              "   7  False          Senior      UpperMiddle  Normal          Current        Mild          Football      Mild        Economy      Poor           FiveThou    False       SubStandard     False          TenThou        False    TenThou     Secure      True         TenThou      Thousand        False       Thousand   Excellent     True      Thousand   Many\n",
              "   8  False          Adult       Prole        Normal          Older          Severe        EggShell      Severe      Economy      Poor           TwentyThou  False       SubStandard     False          TenThou        False    TenThou     City        False        Million      HundredThou     True        Thousand   Fair          False     Thousand   Many\n",
              "   9  False          Adult       Prole        Normal          Older          Severe        EggShell      Severe      Economy      Poor           TwentyThou  False       SubStandard     False          TenThou        False    FiveThou    City        False        HundredThou  TenThou         False       TenThou    Poor          False     Thousand   Many\n",
              "  10  True           Adolescent  Prole        Cautious        Older          Severe        Football      Severe      FamilySedan  Poor           TwentyThou  False       SubStandard     False          TenThou        False    FiveThou    City        True         HundredThou  HundredThou     True        Thousand   Good          False     Thousand   Many\n",
              "[16280 rows x 28 columns]\n"
            ],
            "text/html": [
              "<table class='dataframe'>\n",
              "<thead>\n",
              "<tr><th style=\"text-align: right;\">  C1</th><th>GoodStudent  </th><th>Age       </th><th>SocioEcon  </th><th>RiskAversion  </th><th>VehicleYear  </th><th>ThisCarDam  </th><th>RuggedAuto  </th><th>Accident  </th><th>MakeModel  </th><th>DrivQuality  </th><th>Mileage   </th><th>Antilock  </th><th>DrivingSkill  </th><th>SeniorTrain  </th><th>ThisCarCost  </th><th>Theft  </th><th>CarValue  </th><th>HomeBase  </th><th>AntiTheft  </th><th>PropCost   </th><th>OtherCarCost  </th><th>OtherCar  </th><th>MedCost  </th><th>Cushioning  </th><th>Airbag  </th><th>ILiCost  </th><th>DrivHist  </th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td style=\"text-align: right;\">   1</td><td>False        </td><td>Adult     </td><td>Prole      </td><td>Adventurous   </td><td>Older        </td><td>Moderate    </td><td>EggShell    </td><td>Mild      </td><td>Economy    </td><td>Poor         </td><td>TwentyThou</td><td>False     </td><td>SubStandard   </td><td>False        </td><td>TenThou      </td><td>False  </td><td>FiveThou  </td><td>City      </td><td>False      </td><td>TenThou    </td><td>Thousand      </td><td>True      </td><td>Thousand </td><td>Poor        </td><td>False   </td><td>Thousand </td><td>Many      </td></tr>\n",
              "<tr><td style=\"text-align: right;\">   2</td><td>False        </td><td>Senior    </td><td>Prole      </td><td>Cautious      </td><td>Current      </td><td>None        </td><td>Football    </td><td>None      </td><td>Economy    </td><td>Normal       </td><td>TwentyThou</td><td>False     </td><td>Normal        </td><td>True         </td><td>Thousand     </td><td>False  </td><td>TenThou   </td><td>City      </td><td>True       </td><td>Thousand   </td><td>Thousand      </td><td>True      </td><td>Thousand </td><td>Good        </td><td>True    </td><td>Thousand </td><td>Zero      </td></tr>\n",
              "<tr><td style=\"text-align: right;\">   3</td><td>False        </td><td>Senior    </td><td>UpperMiddle</td><td>Psychopath    </td><td>Current      </td><td>None        </td><td>Football    </td><td>None      </td><td>FamilySedan</td><td>Excellent    </td><td>Domino    </td><td>True      </td><td>Normal        </td><td>False        </td><td>Thousand     </td><td>False  </td><td>TwentyThou</td><td>City      </td><td>False      </td><td>Thousand   </td><td>Thousand      </td><td>False     </td><td>Thousand </td><td>Good        </td><td>True    </td><td>Thousand </td><td>One       </td></tr>\n",
              "<tr><td style=\"text-align: right;\">   4</td><td>False        </td><td>Adolescent</td><td>Middle     </td><td>Normal        </td><td>Older        </td><td>None        </td><td>EggShell    </td><td>None      </td><td>Economy    </td><td>Normal       </td><td>FiftyThou </td><td>False     </td><td>Normal        </td><td>False        </td><td>Thousand     </td><td>False  </td><td>FiveThou  </td><td>Suburb    </td><td>False      </td><td>Thousand   </td><td>Thousand      </td><td>True      </td><td>Thousand </td><td>Fair        </td><td>False   </td><td>Thousand </td><td>Zero      </td></tr>\n",
              "<tr><td style=\"text-align: right;\">   5</td><td>False        </td><td>Adolescent</td><td>Prole      </td><td>Normal        </td><td>Older        </td><td>Moderate    </td><td>Football    </td><td>Moderate  </td><td>Economy    </td><td>Poor         </td><td>FiftyThou </td><td>False     </td><td>SubStandard   </td><td>False        </td><td>TenThou      </td><td>False  </td><td>FiveThou  </td><td>City      </td><td>False      </td><td>TenThou    </td><td>Thousand      </td><td>False     </td><td>Thousand </td><td>Fair        </td><td>False   </td><td>Thousand </td><td>Many      </td></tr>\n",
              "<tr><td style=\"text-align: right;\">   6</td><td>False        </td><td>Adult     </td><td>UpperMiddle</td><td>Normal        </td><td>Current      </td><td>Moderate    </td><td>EggShell    </td><td>Moderate  </td><td>SportsCar  </td><td>Poor         </td><td>FiftyThou </td><td>True      </td><td>SubStandard   </td><td>False        </td><td>HundredThou  </td><td>False  </td><td>TwentyThou</td><td>Suburb    </td><td>True       </td><td>HundredThou</td><td>HundredThou   </td><td>True      </td><td>TenThou  </td><td>Poor        </td><td>True    </td><td>Thousand </td><td>Many      </td></tr>\n",
              "<tr><td style=\"text-align: right;\">   7</td><td>False        </td><td>Senior    </td><td>UpperMiddle</td><td>Normal        </td><td>Current      </td><td>Mild        </td><td>Football    </td><td>Mild      </td><td>Economy    </td><td>Poor         </td><td>FiveThou  </td><td>False     </td><td>SubStandard   </td><td>False        </td><td>TenThou      </td><td>False  </td><td>TenThou   </td><td>Secure    </td><td>True       </td><td>TenThou    </td><td>Thousand      </td><td>False     </td><td>Thousand </td><td>Excellent   </td><td>True    </td><td>Thousand </td><td>Many      </td></tr>\n",
              "<tr><td style=\"text-align: right;\">   8</td><td>False        </td><td>Adult     </td><td>Prole      </td><td>Normal        </td><td>Older        </td><td>Severe      </td><td>EggShell    </td><td>Severe    </td><td>Economy    </td><td>Poor         </td><td>TwentyThou</td><td>False     </td><td>SubStandard   </td><td>False        </td><td>TenThou      </td><td>False  </td><td>TenThou   </td><td>City      </td><td>False      </td><td>Million    </td><td>HundredThou   </td><td>True      </td><td>Thousand </td><td>Fair        </td><td>False   </td><td>Thousand </td><td>Many      </td></tr>\n",
              "<tr><td style=\"text-align: right;\">   9</td><td>False        </td><td>Adult     </td><td>Prole      </td><td>Normal        </td><td>Older        </td><td>Severe      </td><td>EggShell    </td><td>Severe    </td><td>Economy    </td><td>Poor         </td><td>TwentyThou</td><td>False     </td><td>SubStandard   </td><td>False        </td><td>TenThou      </td><td>False  </td><td>FiveThou  </td><td>City      </td><td>False      </td><td>HundredThou</td><td>TenThou       </td><td>False     </td><td>TenThou  </td><td>Poor        </td><td>False   </td><td>Thousand </td><td>Many      </td></tr>\n",
              "<tr><td style=\"text-align: right;\">  10</td><td>True         </td><td>Adolescent</td><td>Prole      </td><td>Cautious      </td><td>Older        </td><td>Severe      </td><td>Football    </td><td>Severe    </td><td>FamilySedan</td><td>Poor         </td><td>TwentyThou</td><td>False     </td><td>SubStandard   </td><td>False        </td><td>TenThou      </td><td>False  </td><td>FiveThou  </td><td>City      </td><td>True       </td><td>HundredThou</td><td>HundredThou   </td><td>True      </td><td>Thousand </td><td>Good        </td><td>False   </td><td>Thousand </td><td>Many      </td></tr>\n",
              "</tbody>\n",
              "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[16280 rows x 28 columns]</pre>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.drop('C1')"
      ],
      "metadata": {
        "id": "TdpRz2-w8qCN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train, test = data .split_frame(ratios=[0.7], seed= 0)"
      ],
      "metadata": {
        "id": "ouBk28Pl80pn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_auto_ml = H2OAutoML(max_runtime_secs=180, sort_metric='AUTO')\n",
        "\n",
        "model_auto_ml.train(y='Accident', training_frame=train)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "DSQToa2y9Lpk",
        "outputId": "0367fe92-9afe-4443-bf26-7227d6782cf0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AutoML progress: |████████████████\n",
            "01:22:17.809: GLM_1_AutoML_2_20240317_12133 [GLM def_1] failed: java.lang.ArrayIndexOutOfBoundsException: Index 83 out of bounds for length 83\n",
            "\n",
            "███████████████████████████████████████████████| (done) 100%\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Model Details\n",
              "=============\n",
              "H2OStackedEnsembleEstimator : Stacked Ensemble\n",
              "Model Key: StackedEnsemble_BestOfFamily_2_AutoML_2_20240317_12133\n",
              "\n",
              "\n",
              "Model Summary for Stacked Ensemble: \n",
              "key                                   value\n",
              "------------------------------------  ----------------\n",
              "Stacking strategy                     cross_validation\n",
              "Number of base models (used / total)  3/3\n",
              "# GBM base models (used / total)      1/1\n",
              "# XGBoost base models (used / total)  1/1\n",
              "# DRF base models (used / total)      1/1\n",
              "Metalearner algorithm                 GLM\n",
              "Metalearner fold assignment scheme    Random\n",
              "Metalearner nfolds                    5\n",
              "Metalearner fold_column\n",
              "Custom metalearner hyperparameters    None\n",
              "\n",
              "ModelMetricsMultinomialGLM: stackedensemble\n",
              "** Reported on train data. **\n",
              "\n",
              "MSE: 0.025970711791452904\n",
              "RMSE: 0.16115431049603637\n",
              "LogLoss: 0.09015686682938877\n",
              "Null degrees of freedom: 9983\n",
              "Residual degrees of freedom: 9955\n",
              "Null deviance: 17898.729095045834\n",
              "Residual deviance: 1800.252316849235\n",
              "AUC table was not computed: it is either disabled (model parameter 'auc_type' was set to AUTO or NONE) or the domain size exceeds the limit (maximum is 50 domains).\n",
              "AUCPR table was not computed: it is either disabled (model parameter 'auc_type' was set to AUTO or NONE) or the domain size exceeds the limit (maximum is 50 domains).\n",
              "\n",
              "Confusion Matrix: Row labels: Actual class; Column labels: Predicted class\n",
              "Mild    Moderate    None    Severe    Error       Rate\n",
              "------  ----------  ------  --------  ----------  -----------\n",
              "709     44          114     0         0.182238    158 / 867\n",
              "26      643         1       109       0.174583    136 / 779\n",
              "5       0           7185    0         0.00069541  5 / 7,190\n",
              "0       26          0       1122      0.0226481   26 / 1,148\n",
              "740     713         7300    1231      0.0325521   325 / 9,984\n",
              "\n",
              "Top-4 Hit Ratios: \n",
              "k    hit_ratio\n",
              "---  -----------\n",
              "1    0.967448\n",
              "2    0.999399\n",
              "3    1\n",
              "4    1\n",
              "\n",
              "ModelMetricsMultinomialGLM: stackedensemble\n",
              "** Reported on cross-validation data. **\n",
              "\n",
              "MSE: 0.04069521585102732\n",
              "RMSE: 0.2017305525968422\n",
              "LogLoss: 0.14293747831942732\n",
              "Null degrees of freedom: 11430\n",
              "Residual degrees of freedom: 11397\n",
              "Null deviance: 20585.852976322836\n",
              "Residual deviance: 3267.836629338748\n",
              "AUC table was not computed: it is either disabled (model parameter 'auc_type' was set to AUTO or NONE) or the domain size exceeds the limit (maximum is 50 domains).\n",
              "AUCPR table was not computed: it is either disabled (model parameter 'auc_type' was set to AUTO or NONE) or the domain size exceeds the limit (maximum is 50 domains).\n",
              "\n",
              "Confusion Matrix: Row labels: Actual class; Column labels: Predicted class\n",
              "Mild    Moderate    None    Severe    Error       Rate\n",
              "------  ----------  ------  --------  ----------  ------------\n",
              "746     80          162     3         0.247225    245 / 991\n",
              "56      675         3       173       0.255788    232 / 907\n",
              "17      0           8193    1         0.00219218  18 / 8,211\n",
              "0       70          0       1252      0.0529501   70 / 1,322\n",
              "819     825         8358    1429      0.049427    565 / 11,431\n",
              "\n",
              "Top-4 Hit Ratios: \n",
              "k    hit_ratio\n",
              "---  -----------\n",
              "1    0.950573\n",
              "2    0.997551\n",
              "3    0.999738\n",
              "4    1\n",
              "\n",
              "Cross-Validation Metrics Summary: \n",
              "                         mean       sd          cv_1_valid    cv_2_valid    cv_3_valid    cv_4_valid    cv_5_valid\n",
              "-----------------------  ---------  ----------  ------------  ------------  ------------  ------------  ------------\n",
              "accuracy                 0.950584   0.00229678  0.948344      0.950446      0.953043      0.948305      0.95278\n",
              "aic                      nan        0           nan           nan           nan           nan           nan\n",
              "auc                      nan        0           nan           nan           nan           nan           nan\n",
              "err                      0.0494161  0.00229678  0.0516556     0.0495536     0.0469565     0.0516949     0.0472198\n",
              "err_count                113        6.36396     117           111           108           122           107\n",
              "loglikelihood            0          0           0             0             0             0             0\n",
              "logloss                  0.142925   0.00434596  0.145643      0.144611      0.13652       0.147264      0.140586\n",
              "max_per_class_error      0.269849   0.0232374   0.287293      0.288557      0.23445       0.280702      0.258242\n",
              "mean_per_class_accuracy  0.860348   0.0119261   0.853625      0.857098      0.878476      0.847572      0.864967\n",
              "mean_per_class_error     0.139652   0.0119261   0.146375      0.142902      0.121524      0.152428      0.135033\n",
              "mse                      0.0406946  0.00173259  0.042839      0.0412338     0.0389566     0.0415728     0.0388707\n",
              "null_deviance            4117.17    131.899     4032.8        4027.66       4314.47       4192.81       4018.12\n",
              "pr_auc                   nan        0           nan           nan           nan           nan           nan\n",
              "r2                       0.922063   0.0044121   0.917113      0.922335      0.929145      0.920507      0.921216\n",
              "residual_deviance        653.567    26.0715     659.764       647.857       627.994       695.087       637.135\n",
              "rmse                     0.201692   0.00429694  0.206976      0.203061      0.197374      0.203894      0.197157\n",
              "\n",
              "[tips]\n",
              "Use `model.explain()` to inspect the model.\n",
              "--\n",
              "Use `h2o.display.toggle_user_tips()` to switch on/off this section."
            ],
            "text/html": [
              "<pre style='margin: 1em 0 1em 0;'>Model Details\n",
              "=============\n",
              "H2OStackedEnsembleEstimator : Stacked Ensemble\n",
              "Model Key: StackedEnsemble_BestOfFamily_2_AutoML_2_20240317_12133\n",
              "</pre>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-8.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-8 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-8 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-8 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-8 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-8 .h2o-table th,\n",
              "#h2o-table-8 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-8 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-8\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Model Summary for Stacked Ensemble: </caption>\n",
              "    <thead><tr><th>key</th>\n",
              "<th>value</th></tr></thead>\n",
              "    <tbody><tr><td>Stacking strategy</td>\n",
              "<td>cross_validation</td></tr>\n",
              "<tr><td>Number of base models (used / total)</td>\n",
              "<td>3/3</td></tr>\n",
              "<tr><td># GBM base models (used / total)</td>\n",
              "<td>1/1</td></tr>\n",
              "<tr><td># XGBoost base models (used / total)</td>\n",
              "<td>1/1</td></tr>\n",
              "<tr><td># DRF base models (used / total)</td>\n",
              "<td>1/1</td></tr>\n",
              "<tr><td>Metalearner algorithm</td>\n",
              "<td>GLM</td></tr>\n",
              "<tr><td>Metalearner fold assignment scheme</td>\n",
              "<td>Random</td></tr>\n",
              "<tr><td>Metalearner nfolds</td>\n",
              "<td>5</td></tr>\n",
              "<tr><td>Metalearner fold_column</td>\n",
              "<td>None</td></tr>\n",
              "<tr><td>Custom metalearner hyperparameters</td>\n",
              "<td>None</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div>\n",
              "<div style='margin: 1em 0 1em 0;'><pre style='margin: 1em 0 1em 0;'>ModelMetricsMultinomialGLM: stackedensemble\n",
              "** Reported on train data. **\n",
              "\n",
              "MSE: 0.025970711791452904\n",
              "RMSE: 0.16115431049603637\n",
              "LogLoss: 0.09015686682938877\n",
              "Null degrees of freedom: 9983\n",
              "Residual degrees of freedom: 9955\n",
              "Null deviance: 17898.729095045834\n",
              "Residual deviance: 1800.252316849235\n",
              "AUC table was not computed: it is either disabled (model parameter 'auc_type' was set to AUTO or NONE) or the domain size exceeds the limit (maximum is 50 domains).\n",
              "AUCPR table was not computed: it is either disabled (model parameter 'auc_type' was set to AUTO or NONE) or the domain size exceeds the limit (maximum is 50 domains).</pre>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-9.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-9 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-9 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-9 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-9 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-9 .h2o-table th,\n",
              "#h2o-table-9 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-9 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-9\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Confusion Matrix: Row labels: Actual class; Column labels: Predicted class</caption>\n",
              "    <thead><tr><th>Mild</th>\n",
              "<th>Moderate</th>\n",
              "<th>None</th>\n",
              "<th>Severe</th>\n",
              "<th>Error</th>\n",
              "<th>Rate</th></tr></thead>\n",
              "    <tbody><tr><td>709.0</td>\n",
              "<td>44.0</td>\n",
              "<td>114.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.1822376</td>\n",
              "<td>158 / 867</td></tr>\n",
              "<tr><td>26.0</td>\n",
              "<td>643.0</td>\n",
              "<td>1.0</td>\n",
              "<td>109.0</td>\n",
              "<td>0.1745828</td>\n",
              "<td>136 / 779</td></tr>\n",
              "<tr><td>5.0</td>\n",
              "<td>0.0</td>\n",
              "<td>7185.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0006954</td>\n",
              "<td>5 / 7,190</td></tr>\n",
              "<tr><td>0.0</td>\n",
              "<td>26.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1122.0</td>\n",
              "<td>0.0226481</td>\n",
              "<td>26 / 1,148</td></tr>\n",
              "<tr><td>740.0</td>\n",
              "<td>713.0</td>\n",
              "<td>7300.0</td>\n",
              "<td>1231.0</td>\n",
              "<td>0.0325521</td>\n",
              "<td>325 / 9,984</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-10.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-10 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-10 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-10 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-10 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-10 .h2o-table th,\n",
              "#h2o-table-10 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-10 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-10\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Top-4 Hit Ratios: </caption>\n",
              "    <thead><tr><th>k</th>\n",
              "<th>hit_ratio</th></tr></thead>\n",
              "    <tbody><tr><td>1</td>\n",
              "<td>0.9674479</td></tr>\n",
              "<tr><td>2</td>\n",
              "<td>0.9993991</td></tr>\n",
              "<tr><td>3</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td>4</td>\n",
              "<td>1.0</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div></div>\n",
              "<div style='margin: 1em 0 1em 0;'><pre style='margin: 1em 0 1em 0;'>ModelMetricsMultinomialGLM: stackedensemble\n",
              "** Reported on cross-validation data. **\n",
              "\n",
              "MSE: 0.04069521585102732\n",
              "RMSE: 0.2017305525968422\n",
              "LogLoss: 0.14293747831942732\n",
              "Null degrees of freedom: 11430\n",
              "Residual degrees of freedom: 11397\n",
              "Null deviance: 20585.852976322836\n",
              "Residual deviance: 3267.836629338748\n",
              "AUC table was not computed: it is either disabled (model parameter 'auc_type' was set to AUTO or NONE) or the domain size exceeds the limit (maximum is 50 domains).\n",
              "AUCPR table was not computed: it is either disabled (model parameter 'auc_type' was set to AUTO or NONE) or the domain size exceeds the limit (maximum is 50 domains).</pre>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-11.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-11 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-11 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-11 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-11 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-11 .h2o-table th,\n",
              "#h2o-table-11 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-11 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-11\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Confusion Matrix: Row labels: Actual class; Column labels: Predicted class</caption>\n",
              "    <thead><tr><th>Mild</th>\n",
              "<th>Moderate</th>\n",
              "<th>None</th>\n",
              "<th>Severe</th>\n",
              "<th>Error</th>\n",
              "<th>Rate</th></tr></thead>\n",
              "    <tbody><tr><td>746.0</td>\n",
              "<td>80.0</td>\n",
              "<td>162.0</td>\n",
              "<td>3.0</td>\n",
              "<td>0.2472250</td>\n",
              "<td>245 / 991</td></tr>\n",
              "<tr><td>56.0</td>\n",
              "<td>675.0</td>\n",
              "<td>3.0</td>\n",
              "<td>173.0</td>\n",
              "<td>0.2557883</td>\n",
              "<td>232 / 907</td></tr>\n",
              "<tr><td>17.0</td>\n",
              "<td>0.0</td>\n",
              "<td>8193.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0021922</td>\n",
              "<td>18 / 8,211</td></tr>\n",
              "<tr><td>0.0</td>\n",
              "<td>70.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1252.0</td>\n",
              "<td>0.0529501</td>\n",
              "<td>70 / 1,322</td></tr>\n",
              "<tr><td>819.0</td>\n",
              "<td>825.0</td>\n",
              "<td>8358.0</td>\n",
              "<td>1429.0</td>\n",
              "<td>0.0494270</td>\n",
              "<td>565 / 11,431</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-12.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-12 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-12 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-12 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-12 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-12 .h2o-table th,\n",
              "#h2o-table-12 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-12 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-12\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Top-4 Hit Ratios: </caption>\n",
              "    <thead><tr><th>k</th>\n",
              "<th>hit_ratio</th></tr></thead>\n",
              "    <tbody><tr><td>1</td>\n",
              "<td>0.950573</td></tr>\n",
              "<tr><td>2</td>\n",
              "<td>0.9975506</td></tr>\n",
              "<tr><td>3</td>\n",
              "<td>0.9997376</td></tr>\n",
              "<tr><td>4</td>\n",
              "<td>1.0</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div></div>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-13.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-13 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-13 .h2o-table caption {\n",
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              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-13\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Cross-Validation Metrics Summary: </caption>\n",
              "    <thead><tr><th></th>\n",
              "<th>mean</th>\n",
              "<th>sd</th>\n",
              "<th>cv_1_valid</th>\n",
              "<th>cv_2_valid</th>\n",
              "<th>cv_3_valid</th>\n",
              "<th>cv_4_valid</th>\n",
              "<th>cv_5_valid</th></tr></thead>\n",
              "    <tbody><tr><td>accuracy</td>\n",
              "<td>0.9505839</td>\n",
              "<td>0.0022968</td>\n",
              "<td>0.9483443</td>\n",
              "<td>0.9504464</td>\n",
              "<td>0.9530435</td>\n",
              "<td>0.9483051</td>\n",
              "<td>0.9527802</td></tr>\n",
              "<tr><td>aic</td>\n",
              "<td>nan</td>\n",
              "<td>0.0</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td></tr>\n",
              "<tr><td>auc</td>\n",
              "<td>nan</td>\n",
              "<td>0.0</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td></tr>\n",
              "<tr><td>err</td>\n",
              "<td>0.0494161</td>\n",
              "<td>0.0022968</td>\n",
              "<td>0.0516556</td>\n",
              "<td>0.0495536</td>\n",
              "<td>0.0469565</td>\n",
              "<td>0.0516949</td>\n",
              "<td>0.0472198</td></tr>\n",
              "<tr><td>err_count</td>\n",
              "<td>113.0</td>\n",
              "<td>6.363961</td>\n",
              "<td>117.0</td>\n",
              "<td>111.0</td>\n",
              "<td>108.0</td>\n",
              "<td>122.0</td>\n",
              "<td>107.0</td></tr>\n",
              "<tr><td>loglikelihood</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>logloss</td>\n",
              "<td>0.1429249</td>\n",
              "<td>0.0043460</td>\n",
              "<td>0.1456433</td>\n",
              "<td>0.1446108</td>\n",
              "<td>0.1365204</td>\n",
              "<td>0.1472642</td>\n",
              "<td>0.1405859</td></tr>\n",
              "<tr><td>max_per_class_error</td>\n",
              "<td>0.2698487</td>\n",
              "<td>0.0232374</td>\n",
              "<td>0.2872928</td>\n",
              "<td>0.2885572</td>\n",
              "<td>0.2344498</td>\n",
              "<td>0.2807018</td>\n",
              "<td>0.2582418</td></tr>\n",
              "<tr><td>mean_per_class_accuracy</td>\n",
              "<td>0.8603476</td>\n",
              "<td>0.0119261</td>\n",
              "<td>0.8536249</td>\n",
              "<td>0.8570982</td>\n",
              "<td>0.8784755</td>\n",
              "<td>0.8475721</td>\n",
              "<td>0.8649672</td></tr>\n",
              "<tr><td>mean_per_class_error</td>\n",
              "<td>0.1396524</td>\n",
              "<td>0.0119261</td>\n",
              "<td>0.1463751</td>\n",
              "<td>0.1429018</td>\n",
              "<td>0.1215245</td>\n",
              "<td>0.1524279</td>\n",
              "<td>0.1350328</td></tr>\n",
              "<tr><td>mse</td>\n",
              "<td>0.0406946</td>\n",
              "<td>0.0017326</td>\n",
              "<td>0.0428390</td>\n",
              "<td>0.0412338</td>\n",
              "<td>0.0389566</td>\n",
              "<td>0.0415728</td>\n",
              "<td>0.0388707</td></tr>\n",
              "<tr><td>null_deviance</td>\n",
              "<td>4117.1704</td>\n",
              "<td>131.89894</td>\n",
              "<td>4032.7952</td>\n",
              "<td>4027.6648</td>\n",
              "<td>4314.4653</td>\n",
              "<td>4192.8125</td>\n",
              "<td>4018.1155</td></tr>\n",
              "<tr><td>pr_auc</td>\n",
              "<td>nan</td>\n",
              "<td>0.0</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td></tr>\n",
              "<tr><td>r2</td>\n",
              "<td>0.9220632</td>\n",
              "<td>0.0044121</td>\n",
              "<td>0.9171126</td>\n",
              "<td>0.9223354</td>\n",
              "<td>0.9291446</td>\n",
              "<td>0.9205072</td>\n",
              "<td>0.9212162</td></tr>\n",
              "<tr><td>residual_deviance</td>\n",
              "<td>653.5673</td>\n",
              "<td>26.071493</td>\n",
              "<td>659.76416</td>\n",
              "<td>647.85657</td>\n",
              "<td>627.9939</td>\n",
              "<td>695.0868</td>\n",
              "<td>637.13525</td></tr>\n",
              "<tr><td>rmse</td>\n",
              "<td>0.2016924</td>\n",
              "<td>0.0042969</td>\n",
              "<td>0.2069758</td>\n",
              "<td>0.2030611</td>\n",
              "<td>0.1973743</td>\n",
              "<td>0.2038941</td>\n",
              "<td>0.1971566</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div><pre style=\"font-size: smaller; margin: 1em 0 0 0;\">\n",
              "\n",
              "[tips]\n",
              "Use `model.explain()` to inspect the model.\n",
              "--\n",
              "Use `h2o.display.toggle_user_tips()` to switch on/off this section.</pre>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ranking = model_auto_ml.leaderboard\n",
        "\n",
        "ranking = ranking.as_data_frame()\n",
        "\n",
        "ranking"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "wx9382ZE-psf",
        "outputId": "35dff33c-85df-42b3-d306-02ba3affdea4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using multi-thread, install datatable (for Python 3.9 or lower), or polars and pyarrow (for Python 3.10 or above) and activate it using:\n",
            "\n",
            "with h2o.utils.threading.local_context(polars_enabled=True, datatable_enabled=True):\n",
            "    pandas_df = h2o_df.as_data_frame()\n",
            "\n",
            "  warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using\"\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            model_id  auc  \\\n",
              "0  StackedEnsemble_BestOfFamily_1_AutoML_1_202403...  NaN   \n",
              "1                      GBM_3_AutoML_1_20240317_11145  NaN   \n",
              "2                      DRF_1_AutoML_1_20240317_11145  NaN   \n",
              "3  StackedEnsemble_AllModels_1_AutoML_1_20240317_...  NaN   \n",
              "4                  XGBoost_2_AutoML_1_20240317_11145  NaN   \n",
              "5                      GBM_4_AutoML_1_20240317_11145  NaN   \n",
              "6                  XGBoost_1_AutoML_1_20240317_11145  NaN   \n",
              "7                      GBM_1_AutoML_1_20240317_11145  NaN   \n",
              "8  StackedEnsemble_BestOfFamily_2_AutoML_1_202403...  NaN   \n",
              "9                      GBM_2_AutoML_1_20240317_11145  NaN   \n",
              "\n",
              "   mean_per_class_error   logloss      rmse       mse  \n",
              "0              0.141562  0.135241  0.197819  0.039132  \n",
              "1              0.148391  0.796523  0.547189  0.299416  \n",
              "2              0.167255  1.203897  0.233091  0.054331  \n",
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              "6              0.141578  0.140108  0.200586  0.040235  \n",
              "7              0.144322  0.136836  0.197569  0.039033  \n",
              "8              0.148665  0.183974  0.222828  0.049652  \n",
              "9              0.147150  0.840600  0.566218  0.320602  "
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    animation:\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-423fd6c4-5cce-48e9-9d1a-3f13f6559e4c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_2de2d985-37b4-4d1f-bb43-36c12f596318\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('ranking')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_2de2d985-37b4-4d1f-bb43-36c12f596318 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('ranking');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ranking",
              "summary": "{\n  \"name\": \"ranking\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"model_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"StackedEnsemble_BestOfFamily_2_AutoML_1_20240317_11145\",\n          \"GBM_3_AutoML_1_20240317_11145\",\n          \"GBM_4_AutoML_1_20240317_11145\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_per_class_error\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.010111644523937709,\n        \"min\": 0.1415617012274648,\n        \"max\": 0.1703608944297992,\n        \"num_unique_values\": 10,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"logloss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.38572727970526344,\n        \"min\": 0.1352407620629442,\n        \"max\": 1.2038971653260755,\n        \"num_unique_values\": 10,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rmse\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15951163893139395,\n        \"min\": 0.1975685473314548,\n        \"max\": 0.5662175728343289,\n        \"num_unique_values\": 10,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mse\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12021219932687004,\n        \"min\": 0.0390333308946613,\n        \"max\": 0.3206023397863986,\n        \"num_unique_values\": 10,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prever = model_auto_ml.leader.predict(test)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a7I_eRM7B0B5",
        "outputId": "7fe86709-cc48-405a-bd0e-26b7854f66d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "stackedensemble prediction progress: |███████████████████████████████████████████| (done) 100%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prever"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "id": "AnyASv7SB_UC",
        "outputId": "6e99f6d9-4c6a-4f59-fe0d-ec1f48ae0b9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "predict           Mild     Moderate         None       Severe\n",
              "---------  -----------  -----------  -----------  -----------\n",
              "None       0.00145487   0.000104174  0.998367     7.37279e-05\n",
              "Moderate   0.0031924    0.97725      0.000405898  0.0191518\n",
              "Severe     0.00468869   0.0682714    0.00115324   0.925887\n",
              "None       0.00237386   0.000172636  0.997343     0.000110785\n",
              "Severe     0.0120794    0.340624     0.00317079   0.644126\n",
              "None       0.00674291   0.00036855   0.992709     0.000179562\n",
              "Mild       0.959908     0.0236778    0.015881     0.000533327\n",
              "None       0.000498406  5.0646e-05   0.999416     3.53347e-05\n",
              "None       0.00218279   0.000212614  0.997505     9.91676e-05\n",
              "Severe     0.00404773   0.102283     0.00139538   0.892274\n",
              "[4849 rows x 5 columns]\n"
            ],
            "text/html": [
              "<table class='dataframe'>\n",
              "<thead>\n",
              "<tr><th>predict  </th><th style=\"text-align: right;\">       Mild</th><th style=\"text-align: right;\">   Moderate</th><th style=\"text-align: right;\">       None</th><th style=\"text-align: right;\">     Severe</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td>None     </td><td style=\"text-align: right;\">0.00145487 </td><td style=\"text-align: right;\">0.000104174</td><td style=\"text-align: right;\">0.998367   </td><td style=\"text-align: right;\">7.37279e-05</td></tr>\n",
              "<tr><td>Moderate </td><td style=\"text-align: right;\">0.0031924  </td><td style=\"text-align: right;\">0.97725    </td><td style=\"text-align: right;\">0.000405898</td><td style=\"text-align: right;\">0.0191518  </td></tr>\n",
              "<tr><td>Severe   </td><td style=\"text-align: right;\">0.00468869 </td><td style=\"text-align: right;\">0.0682714  </td><td style=\"text-align: right;\">0.00115324 </td><td style=\"text-align: right;\">0.925887   </td></tr>\n",
              "<tr><td>None     </td><td style=\"text-align: right;\">0.00237386 </td><td style=\"text-align: right;\">0.000172636</td><td style=\"text-align: right;\">0.997343   </td><td style=\"text-align: right;\">0.000110785</td></tr>\n",
              "<tr><td>Severe   </td><td style=\"text-align: right;\">0.0120794  </td><td style=\"text-align: right;\">0.340624   </td><td style=\"text-align: right;\">0.00317079 </td><td style=\"text-align: right;\">0.644126   </td></tr>\n",
              "<tr><td>None     </td><td style=\"text-align: right;\">0.00674291 </td><td style=\"text-align: right;\">0.00036855 </td><td style=\"text-align: right;\">0.992709   </td><td style=\"text-align: right;\">0.000179562</td></tr>\n",
              "<tr><td>Mild     </td><td style=\"text-align: right;\">0.959908   </td><td style=\"text-align: right;\">0.0236778  </td><td style=\"text-align: right;\">0.015881   </td><td style=\"text-align: right;\">0.000533327</td></tr>\n",
              "<tr><td>None     </td><td style=\"text-align: right;\">0.000498406</td><td style=\"text-align: right;\">5.0646e-05 </td><td style=\"text-align: right;\">0.999416   </td><td style=\"text-align: right;\">3.53347e-05</td></tr>\n",
              "<tr><td>None     </td><td style=\"text-align: right;\">0.00218279 </td><td style=\"text-align: right;\">0.000212614</td><td style=\"text-align: right;\">0.997505   </td><td style=\"text-align: right;\">9.91676e-05</td></tr>\n",
              "<tr><td>Severe   </td><td style=\"text-align: right;\">0.00404773 </td><td style=\"text-align: right;\">0.102283   </td><td style=\"text-align: right;\">0.00139538 </td><td style=\"text-align: right;\">0.892274   </td></tr>\n",
              "</tbody>\n",
              "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[4849 rows x 5 columns]</pre>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    }
  ]
}