diff --git "a/MiMIC_analysis_code.ipynb" "b/MiMIC_analysis_code.ipynb"
--- "a/MiMIC_analysis_code.ipynb"
+++ "b/MiMIC_analysis_code.ipynb"
@@ -3,7 +3,8 @@
   "nbformat_minor": 0,
   "metadata": {
     "colab": {
-      "provenance": []
+      "provenance": [],
+      "toc_visible": true
     },
     "kernelspec": {
       "name": "python3",
@@ -24,15 +25,15 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "vVD1FYhGf4un",
-        "outputId": "7c9c7e64-6dee-4351-b9c5-9c0e2cddd225"
+        "outputId": "ec1d8e56-24ac-4218-a76d-d7ebfc445c61"
       },
-      "execution_count": 3,
+      "execution_count": 1,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m265.9/265.9 MB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m265.9/265.9 MB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
             "\u001b[?25h"
           ]
         }
@@ -40,7 +41,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": 2,
       "metadata": {
         "id": "RhhfAVj2evwW"
       },
@@ -48,7 +49,7 @@
       "source": [
         "import pandas as pd\n",
         "import numpy as np\n",
-        "from sklearn.metrics import mean_absolute_error\n",
+        "from sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error\n",
         "import os\n",
         "import pickle\n",
         "\n",
@@ -65,7 +66,7 @@
       "metadata": {
         "id": "gdViY9EHjSIa"
       },
-      "execution_count": 5,
+      "execution_count": 3,
       "outputs": []
     },
     {
@@ -76,7 +77,7 @@
       "metadata": {
         "id": "Ei8yV-pMgi1p"
       },
-      "execution_count": 6,
+      "execution_count": 4,
       "outputs": []
     },
     {
@@ -88,12 +89,12 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 541
+          "height": 542
         },
         "id": "GUZ9TU6ygkS0",
-        "outputId": "d5e6b52e-8c08-41b0-f5d4-cab6d53f2441"
+        "outputId": "60b7fc5f-d220-4373-9cd0-bdca505a4c62"
       },
-      "execution_count": 7,
+      "execution_count": 5,
       "outputs": [
         {
           "output_type": "stream",
@@ -103,9 +104,9 @@
             "Attempting to start a local H2O server...\n",
             "  Java Version: openjdk version \"11.0.26\" 2025-01-21; OpenJDK Runtime Environment (build 11.0.26+4-post-Ubuntu-1ubuntu122.04); OpenJDK 64-Bit Server VM (build 11.0.26+4-post-Ubuntu-1ubuntu122.04, mixed mode, sharing)\n",
             "  Starting server from /usr/local/lib/python3.11/dist-packages/h2o/backend/bin/h2o.jar\n",
-            "  Ice root: /tmp/tmpl0jbddp_\n",
-            "  JVM stdout: /tmp/tmpl0jbddp_/h2o_unknownUser_started_from_python.out\n",
-            "  JVM stderr: /tmp/tmpl0jbddp_/h2o_unknownUser_started_from_python.err\n",
+            "  Ice root: /tmp/tmpr3sbymez\n",
+            "  JVM stdout: /tmp/tmpr3sbymez/h2o_unknownUser_started_from_python.out\n",
+            "  JVM stderr: /tmp/tmpr3sbymez/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"
           ]
@@ -120,7 +121,7 @@
               "H2O_data_parsing_timezone:  UTC\n",
               "H2O_cluster_version:        3.46.0.7\n",
               "H2O_cluster_version_age:    12 days\n",
-              "H2O_cluster_name:           H2O_from_python_unknownUser_jbqn49\n",
+              "H2O_cluster_name:           H2O_from_python_unknownUser_wkm3dv\n",
               "H2O_cluster_total_nodes:    1\n",
               "H2O_cluster_free_memory:    3.170 Gb\n",
               "H2O_cluster_total_cores:    2\n",
@@ -186,7 +187,7 @@
               "<tr><td>H2O_cluster_version_age:</td>\n",
               "<td>12 days</td></tr>\n",
               "<tr><td>H2O_cluster_name:</td>\n",
-              "<td>H2O_from_python_unknownUser_jbqn49</td></tr>\n",
+              "<td>H2O_from_python_unknownUser_wkm3dv</td></tr>\n",
               "<tr><td>H2O_cluster_total_nodes:</td>\n",
               "<td>1</td></tr>\n",
               "<tr><td>H2O_cluster_free_memory:</td>\n",
@@ -234,9 +235,9 @@
           "height": 481
         },
         "id": "C14dxhmjgtqd",
-        "outputId": "5699d67c-3448-4a66-dbe7-983d362779a7"
+        "outputId": "5c44e544-5f61-4869-c087-1070aba30f5a"
       },
-      "execution_count": 8,
+      "execution_count": 6,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -309,7 +310,7 @@
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-11fa73b5-6d89-4e57-b459-374b72acf561\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-868f1848-f1f8-4a01-9270-96484fadaee6\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -479,7 +480,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-11fa73b5-6d89-4e57-b459-374b72acf561')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-868f1848-f1f8-4a01-9270-96484fadaee6')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -531,12 +532,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-11fa73b5-6d89-4e57-b459-374b72acf561 button.colab-df-convert');\n",
+              "        document.querySelector('#df-868f1848-f1f8-4a01-9270-96484fadaee6 button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-11fa73b5-6d89-4e57-b459-374b72acf561');\n",
+              "        const element = document.querySelector('#df-868f1848-f1f8-4a01-9270-96484fadaee6');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -556,8 +557,8 @@
               "  </div>\n",
               "\n",
               "\n",
-              "<div id=\"df-45d8669b-b17f-4f18-bca1-9d8b13a1ecf4\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-45d8669b-b17f-4f18-bca1-9d8b13a1ecf4')\"\n",
+              "<div id=\"df-c77a018e-14bd-4c58-b8af-cdd370b1eaf2\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c77a018e-14bd-4c58-b8af-cdd370b1eaf2')\"\n",
               "            title=\"Suggest charts\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -676,7 +677,7 @@
               "    }\n",
               "    (() => {\n",
               "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-45d8669b-b17f-4f18-bca1-9d8b13a1ecf4 button');\n",
+              "        document.querySelector('#df-c77a018e-14bd-4c58-b8af-cdd370b1eaf2 button');\n",
               "      quickchartButtonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "    })();\n",
@@ -692,7 +693,7 @@
             }
           },
           "metadata": {},
-          "execution_count": 8
+          "execution_count": 6
         }
       ]
     },
@@ -706,9 +707,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "Kz6EcH3sg2CU",
-        "outputId": "a981c326-97e5-4537-ff12-1307e72f3d9d"
+        "outputId": "ca19761e-d732-4dee-d845-af9ec0bbb517"
       },
-      "execution_count": 10,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -718,7 +719,7 @@
             ]
           },
           "metadata": {},
-          "execution_count": 10
+          "execution_count": 7
         }
       ]
     },
@@ -732,9 +733,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "yP_eBTM9g30n",
-        "outputId": "a5623ba4-9c50-4662-99ff-37b45963794d"
+        "outputId": "5132e8f8-9121-4e45-ee08-097dea2cd375"
       },
-      "execution_count": 11,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -759,7 +760,7 @@
             ]
           },
           "metadata": {},
-          "execution_count": 11
+          "execution_count": 8
         }
       ]
     },
@@ -778,7 +779,18 @@
       "metadata": {
         "id": "7GZb9XbshP1h"
       },
-      "execution_count": 9,
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "final_with_funda['classification'] = final_with_funda['TARGET-2 REGRESSION NORMALIZED'].apply(lambda x : 1 if x > 0 else 0)"
+      ],
+      "metadata": {
+        "id": "7Fsu3U-AFnFU"
+      },
+      "execution_count": 19,
       "outputs": []
     },
     {
@@ -807,12 +819,14 @@
         "    # Root Mean Squared Error (RMSE)\n",
         "    metrics['RMSE'] = np.sqrt(np.mean((y_true - y_pred)**2))\n",
         "\n",
+        "    metrics['MAPE'] = mean_absolute_percentage_error(y_true, y_pred)\n",
+        "\n",
         "    return metrics"
       ],
       "metadata": {
         "id": "fWtWIxwvhisy"
       },
-      "execution_count": 10,
+      "execution_count": 8,
       "outputs": []
     },
     {
@@ -837,9 +851,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "_fe7WxHHiQn5",
-        "outputId": "12f227e7-0092-4fbd-d319-33d0eb11641e"
+        "outputId": "ef8ca706-cc75-44f6-f7b2-7caca898f94b"
       },
-      "execution_count": 14,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -866,7 +880,7 @@
         "                 'Deposits', 'Borrowing']\n",
         "\n",
         "\n",
-        "y_col =  'TARGET-2 REGRESSION NORMALIZED'\n",
+        "y_col =  'RESULT DATE+1 open price'#'TARGET-2 REGRESSION NORMALIZED'\n",
         "\n",
         "h2o_train = h2o.H2OFrame(train[x_cols_to_use])  # Convert train DataFrame to H2OFrame\n",
         "h2o_valid = h2o.H2OFrame(valid[x_cols_to_use])  # Convert valid DataFrame to H2OFrame\n",
@@ -889,9 +903,9 @@
           "height": 829
         },
         "id": "_W53tUk6h1Bm",
-        "outputId": "d7cdfd7d-c238-4874-b140-4c78f2f6ff08"
+        "outputId": "ef58baa5-d0e9-4fe2-c4f6-b4bae5060687"
       },
-      "execution_count": 17,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -910,62 +924,62 @@
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "model_id                                                  rmse         mse        mae      rmsle    mean_residual_deviance\n",
-              "---------------------------------------------------  ---------  ----------  ---------  ---------  ------------------------\n",
-              "GLM_1_AutoML_1_20250409_31401                        0.0364283  0.00132702  0.0269468  0.0368575                0.00132702\n",
-              "DeepLearning_1_AutoML_1_20250409_31401               0.0375302  0.00140852  0.0293691  0.0379352                0.00140852\n",
-              "DeepLearning_grid_3_AutoML_1_20250409_31401_model_1  0.0377829  0.00142755  0.0289105  0.0381493                0.00142755\n",
-              "GBM_1_AutoML_1_20250409_31401                        0.0381166  0.00145288  0.0297264  0.0385379                0.00145288\n",
-              "DeepLearning_grid_1_AutoML_1_20250409_31401_model_1  0.0385729  0.00148787  0.0299244  0.0389847                0.00148787\n",
-              "DeepLearning_grid_2_AutoML_1_20250409_31401_model_1  0.0395684  0.00156566  0.0304818  0.0400375                0.00156566\n",
-              "GBM_3_AutoML_1_20250409_31401                        0.0406758  0.00165452  0.0312224  0.0411611                0.00165452\n",
-              "GBM_4_AutoML_1_20250409_31401                        0.041794   0.00174674  0.0330789  0.0422628                0.00174674\n",
-              "GBM_2_AutoML_1_20250409_31401                        0.0420349  0.00176693  0.0331587  0.0424335                0.00176693\n",
-              "GBM_grid_1_AutoML_1_20250409_31401_model_2           0.0421178  0.00177391  0.0326075  0.0425669                0.00177391\n",
-              "GBM_grid_1_AutoML_1_20250409_31401_model_1           0.0434258  0.0018858   0.0337993  0.043943                 0.0018858\n",
-              "XGBoost_grid_1_AutoML_1_20250409_31401_model_1       0.0452302  0.00204577  0.0372165  0.0455846                0.00204577\n",
-              "XGBoost_grid_1_AutoML_1_20250409_31401_model_2       0.0467678  0.00218722  0.0366421  0.0468626                0.00218722\n",
-              "XGBoost_2_AutoML_1_20250409_31401                    0.0488428  0.00238562  0.0379358  0.0487249                0.00238562\n",
-              "DRF_1_AutoML_1_20250409_31401                        0.0494009  0.00244045  0.0409094  0.0493322                0.00244045\n",
-              "XGBoost_grid_1_AutoML_1_20250409_31401_model_3       0.0494407  0.00244439  0.0391488  0.0495143                0.00244439\n",
-              "XGBoost_1_AutoML_1_20250409_31401                    0.0496493  0.00246506  0.0400429  0.0496916                0.00246506\n",
-              "GBM_5_AutoML_1_20250409_31401                        0.0502199  0.00252204  0.0388021  0.0499862                0.00252204\n",
-              "XRT_1_AutoML_1_20250409_31401                        0.0510792  0.00260909  0.0404121  0.0506284                0.00260909\n",
-              "XGBoost_3_AutoML_1_20250409_31401                    0.0514596  0.00264809  0.0417151  0.0512724                0.00264809\n",
+              "model_id                                                  rmse               mse       mae      rmsle    mean_residual_deviance\n",
+              "----------------------------------------------------  --------  ----------------  --------  ---------  ------------------------\n",
+              "DeepLearning_grid_1_AutoML_1_20250409_125927_model_1   124.274   15444              92.95   0.186283            15444\n",
+              "DeepLearning_grid_2_AutoML_1_20250409_125927_model_1   148.476   22045.2           102.05   0.263675            22045.2\n",
+              "DeepLearning_grid_3_AutoML_1_20250409_125927_model_1   232.249   53939.5           138.35   0.21911             53939.5\n",
+              "GBM_5_AutoML_1_20250409_125927                         347.979  121089             116.301  0.055101           121089\n",
+              "XGBoost_3_AutoML_1_20250409_125927                     360.731  130127             142.876  0.12462            130127\n",
+              "XGBoost_grid_1_AutoML_1_20250409_125927_model_2        360.877  130232             132.802  0.0704741          130232\n",
+              "XGBoost_grid_1_AutoML_1_20250409_125927_model_3        362.277  131245             114.346  0.0618231          131245\n",
+              "GBM_grid_1_AutoML_1_20250409_125927_model_2            363.447  132094             115.479  0.0740894          132094\n",
+              "XGBoost_2_AutoML_1_20250409_125927                     373.977  139859             133.238  0.0886959          139859\n",
+              "GBM_3_AutoML_1_20250409_125927                         386.865  149664             115.389  0.0703277          149664\n",
+              "XGBoost_1_AutoML_1_20250409_125927                     389.489  151702             143.901  0.0940194          151702\n",
+              "GBM_2_AutoML_1_20250409_125927                         390.618  152582             116.687  0.0778867          152582\n",
+              "GBM_4_AutoML_1_20250409_125927                         406.879  165550             118.116  0.0858482          165550\n",
+              "DeepLearning_1_AutoML_1_20250409_125927                410.255  168309             226.432  0.447204           168309\n",
+              "XRT_1_AutoML_1_20250409_125927                         426.22   181663             153.543  0.0815366          181663\n",
+              "DRF_1_AutoML_1_20250409_125927                         426.821  182176             140.027  0.0642774          182176\n",
+              "XGBoost_grid_1_AutoML_1_20250409_125927_model_1        428.699  183783             179.014  0.115927           183783\n",
+              "GBM_grid_1_AutoML_1_20250409_125927_model_1            476.34   226900             211.895  0.227544           226900\n",
+              "GBM_1_AutoML_1_20250409_125927                        1160.2         1.34606e+06   465.625  0.278133                1.34606e+06\n",
+              "GLM_1_AutoML_1_20250409_125927                        2474.66        6.12395e+06  1514.8    1.18928                 6.12395e+06\n",
               "[20 rows x 6 columns]\n"
             ],
             "text/html": [
               "<table class='dataframe'>\n",
               "<thead>\n",
-              "<tr><th>model_id                                           </th><th style=\"text-align: right;\">     rmse</th><th style=\"text-align: right;\">       mse</th><th style=\"text-align: right;\">      mae</th><th style=\"text-align: right;\">    rmsle</th><th style=\"text-align: right;\">  mean_residual_deviance</th></tr>\n",
+              "<tr><th>model_id                                            </th><th style=\"text-align: right;\">    rmse</th><th style=\"text-align: right;\">             mse</th><th style=\"text-align: right;\">     mae</th><th style=\"text-align: right;\">    rmsle</th><th style=\"text-align: right;\">  mean_residual_deviance</th></tr>\n",
               "</thead>\n",
               "<tbody>\n",
-              "<tr><td>GLM_1_AutoML_1_20250409_31401                      </td><td style=\"text-align: right;\">0.0364283</td><td style=\"text-align: right;\">0.00132702</td><td style=\"text-align: right;\">0.0269468</td><td style=\"text-align: right;\">0.0368575</td><td style=\"text-align: right;\">              0.00132702</td></tr>\n",
-              "<tr><td>DeepLearning_1_AutoML_1_20250409_31401             </td><td style=\"text-align: right;\">0.0375302</td><td style=\"text-align: right;\">0.00140852</td><td style=\"text-align: right;\">0.0293691</td><td style=\"text-align: right;\">0.0379352</td><td style=\"text-align: right;\">              0.00140852</td></tr>\n",
-              "<tr><td>DeepLearning_grid_3_AutoML_1_20250409_31401_model_1</td><td style=\"text-align: right;\">0.0377829</td><td style=\"text-align: right;\">0.00142755</td><td style=\"text-align: right;\">0.0289105</td><td style=\"text-align: right;\">0.0381493</td><td style=\"text-align: right;\">              0.00142755</td></tr>\n",
-              "<tr><td>GBM_1_AutoML_1_20250409_31401                      </td><td style=\"text-align: right;\">0.0381166</td><td style=\"text-align: right;\">0.00145288</td><td style=\"text-align: right;\">0.0297264</td><td style=\"text-align: right;\">0.0385379</td><td style=\"text-align: right;\">              0.00145288</td></tr>\n",
-              "<tr><td>DeepLearning_grid_1_AutoML_1_20250409_31401_model_1</td><td style=\"text-align: right;\">0.0385729</td><td style=\"text-align: right;\">0.00148787</td><td style=\"text-align: right;\">0.0299244</td><td style=\"text-align: right;\">0.0389847</td><td style=\"text-align: right;\">              0.00148787</td></tr>\n",
-              "<tr><td>DeepLearning_grid_2_AutoML_1_20250409_31401_model_1</td><td style=\"text-align: right;\">0.0395684</td><td style=\"text-align: right;\">0.00156566</td><td style=\"text-align: right;\">0.0304818</td><td style=\"text-align: right;\">0.0400375</td><td style=\"text-align: right;\">              0.00156566</td></tr>\n",
-              "<tr><td>GBM_3_AutoML_1_20250409_31401                      </td><td style=\"text-align: right;\">0.0406758</td><td style=\"text-align: right;\">0.00165452</td><td style=\"text-align: right;\">0.0312224</td><td style=\"text-align: right;\">0.0411611</td><td style=\"text-align: right;\">              0.00165452</td></tr>\n",
-              "<tr><td>GBM_4_AutoML_1_20250409_31401                      </td><td style=\"text-align: right;\">0.041794 </td><td style=\"text-align: right;\">0.00174674</td><td style=\"text-align: right;\">0.0330789</td><td style=\"text-align: right;\">0.0422628</td><td style=\"text-align: right;\">              0.00174674</td></tr>\n",
-              "<tr><td>GBM_2_AutoML_1_20250409_31401                      </td><td style=\"text-align: right;\">0.0420349</td><td style=\"text-align: right;\">0.00176693</td><td style=\"text-align: right;\">0.0331587</td><td style=\"text-align: right;\">0.0424335</td><td style=\"text-align: right;\">              0.00176693</td></tr>\n",
-              "<tr><td>GBM_grid_1_AutoML_1_20250409_31401_model_2         </td><td style=\"text-align: right;\">0.0421178</td><td style=\"text-align: right;\">0.00177391</td><td style=\"text-align: right;\">0.0326075</td><td style=\"text-align: right;\">0.0425669</td><td style=\"text-align: right;\">              0.00177391</td></tr>\n",
-              "<tr><td>GBM_grid_1_AutoML_1_20250409_31401_model_1         </td><td style=\"text-align: right;\">0.0434258</td><td style=\"text-align: right;\">0.0018858 </td><td style=\"text-align: right;\">0.0337993</td><td style=\"text-align: right;\">0.043943 </td><td style=\"text-align: right;\">              0.0018858 </td></tr>\n",
-              "<tr><td>XGBoost_grid_1_AutoML_1_20250409_31401_model_1     </td><td style=\"text-align: right;\">0.0452302</td><td style=\"text-align: right;\">0.00204577</td><td style=\"text-align: right;\">0.0372165</td><td style=\"text-align: right;\">0.0455846</td><td style=\"text-align: right;\">              0.00204577</td></tr>\n",
-              "<tr><td>XGBoost_grid_1_AutoML_1_20250409_31401_model_2     </td><td style=\"text-align: right;\">0.0467678</td><td style=\"text-align: right;\">0.00218722</td><td style=\"text-align: right;\">0.0366421</td><td style=\"text-align: right;\">0.0468626</td><td style=\"text-align: right;\">              0.00218722</td></tr>\n",
-              "<tr><td>XGBoost_2_AutoML_1_20250409_31401                  </td><td style=\"text-align: right;\">0.0488428</td><td style=\"text-align: right;\">0.00238562</td><td style=\"text-align: right;\">0.0379358</td><td style=\"text-align: right;\">0.0487249</td><td style=\"text-align: right;\">              0.00238562</td></tr>\n",
-              "<tr><td>DRF_1_AutoML_1_20250409_31401                      </td><td style=\"text-align: right;\">0.0494009</td><td style=\"text-align: right;\">0.00244045</td><td style=\"text-align: right;\">0.0409094</td><td style=\"text-align: right;\">0.0493322</td><td style=\"text-align: right;\">              0.00244045</td></tr>\n",
-              "<tr><td>XGBoost_grid_1_AutoML_1_20250409_31401_model_3     </td><td style=\"text-align: right;\">0.0494407</td><td style=\"text-align: right;\">0.00244439</td><td style=\"text-align: right;\">0.0391488</td><td style=\"text-align: right;\">0.0495143</td><td style=\"text-align: right;\">              0.00244439</td></tr>\n",
-              "<tr><td>XGBoost_1_AutoML_1_20250409_31401                  </td><td style=\"text-align: right;\">0.0496493</td><td style=\"text-align: right;\">0.00246506</td><td style=\"text-align: right;\">0.0400429</td><td style=\"text-align: right;\">0.0496916</td><td style=\"text-align: right;\">              0.00246506</td></tr>\n",
-              "<tr><td>GBM_5_AutoML_1_20250409_31401                      </td><td style=\"text-align: right;\">0.0502199</td><td style=\"text-align: right;\">0.00252204</td><td style=\"text-align: right;\">0.0388021</td><td style=\"text-align: right;\">0.0499862</td><td style=\"text-align: right;\">              0.00252204</td></tr>\n",
-              "<tr><td>XRT_1_AutoML_1_20250409_31401                      </td><td style=\"text-align: right;\">0.0510792</td><td style=\"text-align: right;\">0.00260909</td><td style=\"text-align: right;\">0.0404121</td><td style=\"text-align: right;\">0.0506284</td><td style=\"text-align: right;\">              0.00260909</td></tr>\n",
-              "<tr><td>XGBoost_3_AutoML_1_20250409_31401                  </td><td style=\"text-align: right;\">0.0514596</td><td style=\"text-align: right;\">0.00264809</td><td style=\"text-align: right;\">0.0417151</td><td style=\"text-align: right;\">0.0512724</td><td style=\"text-align: right;\">              0.00264809</td></tr>\n",
+              "<tr><td>DeepLearning_grid_1_AutoML_1_20250409_125927_model_1</td><td style=\"text-align: right;\"> 124.274</td><td style=\"text-align: right;\"> 15444          </td><td style=\"text-align: right;\">  92.95 </td><td style=\"text-align: right;\">0.186283 </td><td style=\"text-align: right;\">         15444          </td></tr>\n",
+              "<tr><td>DeepLearning_grid_2_AutoML_1_20250409_125927_model_1</td><td style=\"text-align: right;\"> 148.476</td><td style=\"text-align: right;\"> 22045.2        </td><td style=\"text-align: right;\"> 102.05 </td><td style=\"text-align: right;\">0.263675 </td><td style=\"text-align: right;\">         22045.2        </td></tr>\n",
+              "<tr><td>DeepLearning_grid_3_AutoML_1_20250409_125927_model_1</td><td style=\"text-align: right;\"> 232.249</td><td style=\"text-align: right;\"> 53939.5        </td><td style=\"text-align: right;\"> 138.35 </td><td style=\"text-align: right;\">0.21911  </td><td style=\"text-align: right;\">         53939.5        </td></tr>\n",
+              "<tr><td>GBM_5_AutoML_1_20250409_125927                      </td><td style=\"text-align: right;\"> 347.979</td><td style=\"text-align: right;\">121089          </td><td style=\"text-align: right;\"> 116.301</td><td style=\"text-align: right;\">0.055101 </td><td style=\"text-align: right;\">        121089          </td></tr>\n",
+              "<tr><td>XGBoost_3_AutoML_1_20250409_125927                  </td><td style=\"text-align: right;\"> 360.731</td><td style=\"text-align: right;\">130127          </td><td style=\"text-align: right;\"> 142.876</td><td style=\"text-align: right;\">0.12462  </td><td style=\"text-align: right;\">        130127          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_1_20250409_125927_model_2     </td><td style=\"text-align: right;\"> 360.877</td><td style=\"text-align: right;\">130232          </td><td style=\"text-align: right;\"> 132.802</td><td style=\"text-align: right;\">0.0704741</td><td style=\"text-align: right;\">        130232          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_1_20250409_125927_model_3     </td><td style=\"text-align: right;\"> 362.277</td><td style=\"text-align: right;\">131245          </td><td style=\"text-align: right;\"> 114.346</td><td style=\"text-align: right;\">0.0618231</td><td style=\"text-align: right;\">        131245          </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_1_20250409_125927_model_2         </td><td style=\"text-align: right;\"> 363.447</td><td style=\"text-align: right;\">132094          </td><td style=\"text-align: right;\"> 115.479</td><td style=\"text-align: right;\">0.0740894</td><td style=\"text-align: right;\">        132094          </td></tr>\n",
+              "<tr><td>XGBoost_2_AutoML_1_20250409_125927                  </td><td style=\"text-align: right;\"> 373.977</td><td style=\"text-align: right;\">139859          </td><td style=\"text-align: right;\"> 133.238</td><td style=\"text-align: right;\">0.0886959</td><td style=\"text-align: right;\">        139859          </td></tr>\n",
+              "<tr><td>GBM_3_AutoML_1_20250409_125927                      </td><td style=\"text-align: right;\"> 386.865</td><td style=\"text-align: right;\">149664          </td><td style=\"text-align: right;\"> 115.389</td><td style=\"text-align: right;\">0.0703277</td><td style=\"text-align: right;\">        149664          </td></tr>\n",
+              "<tr><td>XGBoost_1_AutoML_1_20250409_125927                  </td><td style=\"text-align: right;\"> 389.489</td><td style=\"text-align: right;\">151702          </td><td style=\"text-align: right;\"> 143.901</td><td style=\"text-align: right;\">0.0940194</td><td style=\"text-align: right;\">        151702          </td></tr>\n",
+              "<tr><td>GBM_2_AutoML_1_20250409_125927                      </td><td style=\"text-align: right;\"> 390.618</td><td style=\"text-align: right;\">152582          </td><td style=\"text-align: right;\"> 116.687</td><td style=\"text-align: right;\">0.0778867</td><td style=\"text-align: right;\">        152582          </td></tr>\n",
+              "<tr><td>GBM_4_AutoML_1_20250409_125927                      </td><td style=\"text-align: right;\"> 406.879</td><td style=\"text-align: right;\">165550          </td><td style=\"text-align: right;\"> 118.116</td><td style=\"text-align: right;\">0.0858482</td><td style=\"text-align: right;\">        165550          </td></tr>\n",
+              "<tr><td>DeepLearning_1_AutoML_1_20250409_125927             </td><td style=\"text-align: right;\"> 410.255</td><td style=\"text-align: right;\">168309          </td><td style=\"text-align: right;\"> 226.432</td><td style=\"text-align: right;\">0.447204 </td><td style=\"text-align: right;\">        168309          </td></tr>\n",
+              "<tr><td>XRT_1_AutoML_1_20250409_125927                      </td><td style=\"text-align: right;\"> 426.22 </td><td style=\"text-align: right;\">181663          </td><td style=\"text-align: right;\"> 153.543</td><td style=\"text-align: right;\">0.0815366</td><td style=\"text-align: right;\">        181663          </td></tr>\n",
+              "<tr><td>DRF_1_AutoML_1_20250409_125927                      </td><td style=\"text-align: right;\"> 426.821</td><td style=\"text-align: right;\">182176          </td><td style=\"text-align: right;\"> 140.027</td><td style=\"text-align: right;\">0.0642774</td><td style=\"text-align: right;\">        182176          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_1_20250409_125927_model_1     </td><td style=\"text-align: right;\"> 428.699</td><td style=\"text-align: right;\">183783          </td><td style=\"text-align: right;\"> 179.014</td><td style=\"text-align: right;\">0.115927 </td><td style=\"text-align: right;\">        183783          </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_1_20250409_125927_model_1         </td><td style=\"text-align: right;\"> 476.34 </td><td style=\"text-align: right;\">226900          </td><td style=\"text-align: right;\"> 211.895</td><td style=\"text-align: right;\">0.227544 </td><td style=\"text-align: right;\">        226900          </td></tr>\n",
+              "<tr><td>GBM_1_AutoML_1_20250409_125927                      </td><td style=\"text-align: right;\">1160.2  </td><td style=\"text-align: right;\">     1.34606e+06</td><td style=\"text-align: right;\"> 465.625</td><td style=\"text-align: right;\">0.278133 </td><td style=\"text-align: right;\">             1.34606e+06</td></tr>\n",
+              "<tr><td>GLM_1_AutoML_1_20250409_125927                      </td><td style=\"text-align: right;\">2474.66 </td><td style=\"text-align: right;\">     6.12395e+06</td><td style=\"text-align: right;\">1514.8  </td><td style=\"text-align: right;\">1.18928  </td><td style=\"text-align: right;\">             6.12395e+06</td></tr>\n",
               "</tbody>\n",
               "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[20 rows x 6 columns]</pre>"
             ]
           },
           "metadata": {},
-          "execution_count": 17
+          "execution_count": 12
         }
       ]
     },
@@ -981,50 +995,50 @@
           "height": 409
         },
         "id": "yQRo3ds2ik4N",
-        "outputId": "73d56e08-e246-4e32-d092-647dbf744555"
+        "outputId": "e8dd5959-fe95-4296-d031-a586b4c2cf19"
       },
-      "execution_count": 18,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "model_id                                                  rmse         mse        mae      rmsle    mean_residual_deviance    training_time_ms    predict_time_per_row_ms  algo\n",
-              "---------------------------------------------------  ---------  ----------  ---------  ---------  ------------------------  ------------------  -------------------------  ------------\n",
-              "GLM_1_AutoML_1_20250409_31401                        0.0364283  0.00132702  0.0269468  0.0368575                0.00132702                 600                   0.04567   GLM\n",
-              "DeepLearning_1_AutoML_1_20250409_31401               0.0375302  0.00140852  0.0293691  0.0379352                0.00140852                 696                   0.028323  DeepLearning\n",
-              "DeepLearning_grid_3_AutoML_1_20250409_31401_model_1  0.0377829  0.00142755  0.0289105  0.0381493                0.00142755               28950                   0.060333  DeepLearning\n",
-              "GBM_1_AutoML_1_20250409_31401                        0.0381166  0.00145288  0.0297264  0.0385379                0.00145288                 966                   0.035659  GBM\n",
-              "DeepLearning_grid_1_AutoML_1_20250409_31401_model_1  0.0385729  0.00148787  0.0299244  0.0389847                0.00148787               27771                   0.051113  DeepLearning\n",
-              "DeepLearning_grid_2_AutoML_1_20250409_31401_model_1  0.0395684  0.00156566  0.0304818  0.0400375                0.00156566               28834                   0.070624  DeepLearning\n",
-              "GBM_3_AutoML_1_20250409_31401                        0.0406758  0.00165452  0.0312224  0.0411611                0.00165452                 647                   0.035962  GBM\n",
-              "GBM_4_AutoML_1_20250409_31401                        0.041794   0.00174674  0.0330789  0.0422628                0.00174674                 418                   0.041842  GBM\n",
-              "GBM_2_AutoML_1_20250409_31401                        0.0420349  0.00176693  0.0331587  0.0424335                0.00176693                 520                   0.092577  GBM\n",
-              "GBM_grid_1_AutoML_1_20250409_31401_model_2           0.0421178  0.00177391  0.0326075  0.0425669                0.00177391                 454                   0.026596  GBM\n",
+              "model_id                                                 rmse       mse      mae      rmsle    mean_residual_deviance    training_time_ms    predict_time_per_row_ms  algo\n",
+              "----------------------------------------------------  -------  --------  -------  ---------  ------------------------  ------------------  -------------------------  ------------\n",
+              "DeepLearning_grid_1_AutoML_1_20250409_125927_model_1  124.274   15444     92.95   0.186283                    15444                 90000                   0.130107  DeepLearning\n",
+              "DeepLearning_grid_2_AutoML_1_20250409_125927_model_1  148.476   22045.2  102.05   0.263675                    22045.2               66419                   0.209791  DeepLearning\n",
+              "DeepLearning_grid_3_AutoML_1_20250409_125927_model_1  232.249   53939.5  138.35   0.21911                     53939.5               51729                   0.152972  DeepLearning\n",
+              "GBM_5_AutoML_1_20250409_125927                        347.979  121089    116.301  0.055101                   121089                  3756                   0.215801  GBM\n",
+              "XGBoost_3_AutoML_1_20250409_125927                    360.731  130127    142.876  0.12462                    130127                  1275                   0.303849  XGBoost\n",
+              "XGBoost_grid_1_AutoML_1_20250409_125927_model_2       360.877  130232    132.802  0.0704741                  130232                  5185                   0.236808  XGBoost\n",
+              "XGBoost_grid_1_AutoML_1_20250409_125927_model_3       362.277  131245    114.346  0.0618231                  131245                  7255                   0.100218  XGBoost\n",
+              "GBM_grid_1_AutoML_1_20250409_125927_model_2           363.447  132094    115.479  0.0740894                  132094                  1154                   0.086248  GBM\n",
+              "XGBoost_2_AutoML_1_20250409_125927                    373.977  139859    133.238  0.0886959                  139859                  4713                   0.110586  XGBoost\n",
+              "GBM_3_AutoML_1_20250409_125927                        386.865  149664    115.389  0.0703277                  149664                  4115                   0.17295   GBM\n",
               "[20 rows x 9 columns]\n"
             ],
             "text/html": [
               "<table class='dataframe'>\n",
               "<thead>\n",
-              "<tr><th>model_id                                           </th><th style=\"text-align: right;\">     rmse</th><th style=\"text-align: right;\">       mse</th><th style=\"text-align: right;\">      mae</th><th style=\"text-align: right;\">    rmsle</th><th style=\"text-align: right;\">  mean_residual_deviance</th><th style=\"text-align: right;\">  training_time_ms</th><th style=\"text-align: right;\">  predict_time_per_row_ms</th><th>algo        </th></tr>\n",
+              "<tr><th>model_id                                            </th><th style=\"text-align: right;\">   rmse</th><th style=\"text-align: right;\">     mse</th><th style=\"text-align: right;\">    mae</th><th style=\"text-align: right;\">    rmsle</th><th style=\"text-align: right;\">  mean_residual_deviance</th><th style=\"text-align: right;\">  training_time_ms</th><th style=\"text-align: right;\">  predict_time_per_row_ms</th><th>algo        </th></tr>\n",
               "</thead>\n",
               "<tbody>\n",
-              "<tr><td>GLM_1_AutoML_1_20250409_31401                      </td><td style=\"text-align: right;\">0.0364283</td><td style=\"text-align: right;\">0.00132702</td><td style=\"text-align: right;\">0.0269468</td><td style=\"text-align: right;\">0.0368575</td><td style=\"text-align: right;\">              0.00132702</td><td style=\"text-align: right;\">               600</td><td style=\"text-align: right;\">                 0.04567 </td><td>GLM         </td></tr>\n",
-              "<tr><td>DeepLearning_1_AutoML_1_20250409_31401             </td><td style=\"text-align: right;\">0.0375302</td><td style=\"text-align: right;\">0.00140852</td><td style=\"text-align: right;\">0.0293691</td><td style=\"text-align: right;\">0.0379352</td><td style=\"text-align: right;\">              0.00140852</td><td style=\"text-align: right;\">               696</td><td style=\"text-align: right;\">                 0.028323</td><td>DeepLearning</td></tr>\n",
-              "<tr><td>DeepLearning_grid_3_AutoML_1_20250409_31401_model_1</td><td style=\"text-align: right;\">0.0377829</td><td style=\"text-align: right;\">0.00142755</td><td style=\"text-align: right;\">0.0289105</td><td style=\"text-align: right;\">0.0381493</td><td style=\"text-align: right;\">              0.00142755</td><td style=\"text-align: right;\">             28950</td><td style=\"text-align: right;\">                 0.060333</td><td>DeepLearning</td></tr>\n",
-              "<tr><td>GBM_1_AutoML_1_20250409_31401                      </td><td style=\"text-align: right;\">0.0381166</td><td style=\"text-align: right;\">0.00145288</td><td style=\"text-align: right;\">0.0297264</td><td style=\"text-align: right;\">0.0385379</td><td style=\"text-align: right;\">              0.00145288</td><td style=\"text-align: right;\">               966</td><td style=\"text-align: right;\">                 0.035659</td><td>GBM         </td></tr>\n",
-              "<tr><td>DeepLearning_grid_1_AutoML_1_20250409_31401_model_1</td><td style=\"text-align: right;\">0.0385729</td><td style=\"text-align: right;\">0.00148787</td><td style=\"text-align: right;\">0.0299244</td><td style=\"text-align: right;\">0.0389847</td><td style=\"text-align: right;\">              0.00148787</td><td style=\"text-align: right;\">             27771</td><td style=\"text-align: right;\">                 0.051113</td><td>DeepLearning</td></tr>\n",
-              "<tr><td>DeepLearning_grid_2_AutoML_1_20250409_31401_model_1</td><td style=\"text-align: right;\">0.0395684</td><td style=\"text-align: right;\">0.00156566</td><td style=\"text-align: right;\">0.0304818</td><td style=\"text-align: right;\">0.0400375</td><td style=\"text-align: right;\">              0.00156566</td><td style=\"text-align: right;\">             28834</td><td style=\"text-align: right;\">                 0.070624</td><td>DeepLearning</td></tr>\n",
-              "<tr><td>GBM_3_AutoML_1_20250409_31401                      </td><td style=\"text-align: right;\">0.0406758</td><td style=\"text-align: right;\">0.00165452</td><td style=\"text-align: right;\">0.0312224</td><td style=\"text-align: right;\">0.0411611</td><td style=\"text-align: right;\">              0.00165452</td><td style=\"text-align: right;\">               647</td><td style=\"text-align: right;\">                 0.035962</td><td>GBM         </td></tr>\n",
-              "<tr><td>GBM_4_AutoML_1_20250409_31401                      </td><td style=\"text-align: right;\">0.041794 </td><td style=\"text-align: right;\">0.00174674</td><td style=\"text-align: right;\">0.0330789</td><td style=\"text-align: right;\">0.0422628</td><td style=\"text-align: right;\">              0.00174674</td><td style=\"text-align: right;\">               418</td><td style=\"text-align: right;\">                 0.041842</td><td>GBM         </td></tr>\n",
-              "<tr><td>GBM_2_AutoML_1_20250409_31401                      </td><td style=\"text-align: right;\">0.0420349</td><td style=\"text-align: right;\">0.00176693</td><td style=\"text-align: right;\">0.0331587</td><td style=\"text-align: right;\">0.0424335</td><td style=\"text-align: right;\">              0.00176693</td><td style=\"text-align: right;\">               520</td><td style=\"text-align: right;\">                 0.092577</td><td>GBM         </td></tr>\n",
-              "<tr><td>GBM_grid_1_AutoML_1_20250409_31401_model_2         </td><td style=\"text-align: right;\">0.0421178</td><td style=\"text-align: right;\">0.00177391</td><td style=\"text-align: right;\">0.0326075</td><td style=\"text-align: right;\">0.0425669</td><td style=\"text-align: right;\">              0.00177391</td><td style=\"text-align: right;\">               454</td><td style=\"text-align: right;\">                 0.026596</td><td>GBM         </td></tr>\n",
+              "<tr><td>DeepLearning_grid_1_AutoML_1_20250409_125927_model_1</td><td style=\"text-align: right;\">124.274</td><td style=\"text-align: right;\"> 15444  </td><td style=\"text-align: right;\"> 92.95 </td><td style=\"text-align: right;\">0.186283 </td><td style=\"text-align: right;\">                 15444  </td><td style=\"text-align: right;\">             90000</td><td style=\"text-align: right;\">                 0.130107</td><td>DeepLearning</td></tr>\n",
+              "<tr><td>DeepLearning_grid_2_AutoML_1_20250409_125927_model_1</td><td style=\"text-align: right;\">148.476</td><td style=\"text-align: right;\"> 22045.2</td><td style=\"text-align: right;\">102.05 </td><td style=\"text-align: right;\">0.263675 </td><td style=\"text-align: right;\">                 22045.2</td><td style=\"text-align: right;\">             66419</td><td style=\"text-align: right;\">                 0.209791</td><td>DeepLearning</td></tr>\n",
+              "<tr><td>DeepLearning_grid_3_AutoML_1_20250409_125927_model_1</td><td style=\"text-align: right;\">232.249</td><td style=\"text-align: right;\"> 53939.5</td><td style=\"text-align: right;\">138.35 </td><td style=\"text-align: right;\">0.21911  </td><td style=\"text-align: right;\">                 53939.5</td><td style=\"text-align: right;\">             51729</td><td style=\"text-align: right;\">                 0.152972</td><td>DeepLearning</td></tr>\n",
+              "<tr><td>GBM_5_AutoML_1_20250409_125927                      </td><td style=\"text-align: right;\">347.979</td><td style=\"text-align: right;\">121089  </td><td style=\"text-align: right;\">116.301</td><td style=\"text-align: right;\">0.055101 </td><td style=\"text-align: right;\">                121089  </td><td style=\"text-align: right;\">              3756</td><td style=\"text-align: right;\">                 0.215801</td><td>GBM         </td></tr>\n",
+              "<tr><td>XGBoost_3_AutoML_1_20250409_125927                  </td><td style=\"text-align: right;\">360.731</td><td style=\"text-align: right;\">130127  </td><td style=\"text-align: right;\">142.876</td><td style=\"text-align: right;\">0.12462  </td><td style=\"text-align: right;\">                130127  </td><td style=\"text-align: right;\">              1275</td><td style=\"text-align: right;\">                 0.303849</td><td>XGBoost     </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_1_20250409_125927_model_2     </td><td style=\"text-align: right;\">360.877</td><td style=\"text-align: right;\">130232  </td><td style=\"text-align: right;\">132.802</td><td style=\"text-align: right;\">0.0704741</td><td style=\"text-align: right;\">                130232  </td><td style=\"text-align: right;\">              5185</td><td style=\"text-align: right;\">                 0.236808</td><td>XGBoost     </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_1_20250409_125927_model_3     </td><td style=\"text-align: right;\">362.277</td><td style=\"text-align: right;\">131245  </td><td style=\"text-align: right;\">114.346</td><td style=\"text-align: right;\">0.0618231</td><td style=\"text-align: right;\">                131245  </td><td style=\"text-align: right;\">              7255</td><td style=\"text-align: right;\">                 0.100218</td><td>XGBoost     </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_1_20250409_125927_model_2         </td><td style=\"text-align: right;\">363.447</td><td style=\"text-align: right;\">132094  </td><td style=\"text-align: right;\">115.479</td><td style=\"text-align: right;\">0.0740894</td><td style=\"text-align: right;\">                132094  </td><td style=\"text-align: right;\">              1154</td><td style=\"text-align: right;\">                 0.086248</td><td>GBM         </td></tr>\n",
+              "<tr><td>XGBoost_2_AutoML_1_20250409_125927                  </td><td style=\"text-align: right;\">373.977</td><td style=\"text-align: right;\">139859  </td><td style=\"text-align: right;\">133.238</td><td style=\"text-align: right;\">0.0886959</td><td style=\"text-align: right;\">                139859  </td><td style=\"text-align: right;\">              4713</td><td style=\"text-align: right;\">                 0.110586</td><td>XGBoost     </td></tr>\n",
+              "<tr><td>GBM_3_AutoML_1_20250409_125927                      </td><td style=\"text-align: right;\">386.865</td><td style=\"text-align: right;\">149664  </td><td style=\"text-align: right;\">115.389</td><td style=\"text-align: right;\">0.0703277</td><td style=\"text-align: right;\">                149664  </td><td style=\"text-align: right;\">              4115</td><td style=\"text-align: right;\">                 0.17295 </td><td>GBM         </td></tr>\n",
               "</tbody>\n",
               "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[20 rows x 9 columns]</pre>"
             ]
           },
           "metadata": {},
-          "execution_count": 18
+          "execution_count": 13
         }
       ]
     },
@@ -1040,9 +1054,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "L3qQRFo9isOh",
-        "outputId": "254ba345-239b-419f-e6b9-93cf2a4f22e1"
+        "outputId": "fe84fb62-d72a-4303-99ec-4a49d6b94617"
       },
-      "execution_count": 19,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -1050,97 +1064,82 @@
           "text": [
             "Model Details\n",
             "=============\n",
-            "H2OGeneralizedLinearEstimator : Generalized Linear Modeling\n",
-            "Model Key: GLM_1_AutoML_1_20250409_31401\n",
+            "H2ODeepLearningEstimator : Deep Learning\n",
+            "Model Key: DeepLearning_grid_1_AutoML_1_20250409_125927_model_1\n",
             "\n",
             "\n",
-            "GLM Model: summary\n",
-            "    family    link      regularization               lambda_search                                                                number_of_predictors_total    number_of_active_predictors    number_of_iterations    training_frame\n",
-            "--  --------  --------  ---------------------------  ---------------------------------------------------------------------------  ----------------------------  -----------------------------  ----------------------  ----------------------------------------------\n",
-            "    gaussian  identity  Ridge ( lambda = 9.745E-5 )  nlambda = 30, lambda.max = 0.9745, lambda.min = 9.745E-5, lambda.1se = -1.0  274                           274                            30                      AutoML_1_20250409_31401_training_py_1_sid_8560\n",
+            "Status of Neuron Layers: predicting RESULT DATE+1 open price, regression, gaussian distribution, Quadratic loss, 28,101 weights/biases, 345.4 KB, 509,954 training samples, mini-batch size 1\n",
+            "    layer    units    type              dropout    l1    l2    mean_rate              rate_rms               momentum    mean_weight             weight_rms           mean_bias             bias_rms\n",
+            "--  -------  -------  ----------------  ---------  ----  ----  ---------------------  ---------------------  ----------  ----------------------  -------------------  --------------------  -----------------------\n",
+            "    1        279      Input             15.0\n",
+            "    2        100      RectifierDropout  10.0       0.0   0.0   0.0469522964567144     0.1403437852859497     0.0         -0.0034428720702365887  0.0753689706325531   0.11255552116543806   0.12360426783561707\n",
+            "    3        1        Linear                       0.0   0.0   0.0022405613915179854  0.0017097294330596924  0.0         0.0212319300416857      0.10420235991477966  -0.25282336328233784  1.0971281125650402e-154\n",
             "\n",
-            "ModelMetricsRegressionGLM: glm\n",
+            "ModelMetricsRegression: deeplearning\n",
             "** Reported on train data. **\n",
             "\n",
-            "MSE: 0.0010580943167378097\n",
-            "RMSE: 0.03252836172846413\n",
-            "MAE: 0.024110780685911083\n",
-            "RMSLE: 0.03241020650634541\n",
-            "Mean Residual Deviance: 0.0010580943167378097\n",
-            "R^2: 0.25753895049458075\n",
-            "Null degrees of freedom: 831\n",
-            "Residual degrees of freedom: 557\n",
-            "Null deviance: 1.1856978519105883\n",
-            "Residual deviance: 0.8803344715258576\n",
-            "AIC: -2787.156069063324\n",
+            "MSE: 18621.99884420956\n",
+            "RMSE: 136.46244481251816\n",
+            "MAE: 103.43077946092374\n",
+            "RMSLE: NaN\n",
+            "Mean Residual Deviance: 18621.99884420956\n",
             "\n",
-            "ModelMetricsRegressionGLM: glm\n",
+            "ModelMetricsRegression: deeplearning\n",
             "** Reported on validation data. **\n",
             "\n",
-            "MSE: 0.0013270200694665854\n",
-            "RMSE: 0.03642828666663566\n",
-            "MAE: 0.026946774321347674\n",
-            "RMSLE: 0.036857486881133425\n",
-            "Mean Residual Deviance: 0.0013270200694665854\n",
-            "R^2: 0.14759572067993898\n",
-            "Null degrees of freedom: 102\n",
-            "Residual degrees of freedom: -172\n",
-            "Null deviance: 0.1620866949097968\n",
-            "Residual deviance: 0.1366830671550583\n",
-            "AIC: 161.94493965996077\n",
+            "MSE: 15443.961443829116\n",
+            "RMSE: 124.27373593736175\n",
+            "MAE: 92.95004899710531\n",
+            "RMSLE: 0.18628335185822448\n",
+            "Mean Residual Deviance: 15443.961443829116\n",
             "\n",
             "Scoring History: \n",
-            "     timestamp            duration    iteration    lambda    predictors    deviance_train         deviance_test          alpha    iterations    training_rmse        training_deviance      training_mae          training_r2          validation_rmse      validation_deviance    validation_mae        validation_r2\n",
-            "---  -------------------  ----------  -----------  --------  ------------  ---------------------  ---------------------  -------  ------------  -------------------  ---------------------  --------------------  -------------------  -------------------  ---------------------  --------------------  -------------------\n",
-            "     2025-04-09 03:14:04  0.000 sec   1            .97E0     275           0.0013143170253908818  0.0014668972619771203  0.0\n",
-            "     2025-04-09 03:14:04  0.012 sec   2            .71E0     275           0.0013026510754653006  0.0014597595689484302  0.0\n",
-            "     2025-04-09 03:14:04  0.020 sec   3            .52E0     275           0.0012919179627242602  0.0014533674486297142  0.0\n",
-            "     2025-04-09 03:14:04  0.031 sec   4            .38E0     275           0.0012823742209381152  0.0014490082774998843  0.0\n",
-            "     2025-04-09 03:14:04  0.038 sec   5            .27E0     275           0.0012738192872080816  0.0014462109550775293  0.0      5             0.03252836172846413  0.0010580943167378097  0.024110780685911083  0.25753895049458075  0.03642828666663566  0.0013270200694665854  0.026946774321347674  0.14759572067993898\n",
-            "     2025-04-09 03:14:04  0.046 sec   6            .2E0      275           0.0012658973200284059  0.0014449775021809143  0.0\n",
-            "     2025-04-09 03:14:04  0.056 sec   7            .14E0     275           0.0012580738468318943  0.0014445475579979554  0.0\n",
-            "     2025-04-09 03:14:04  0.063 sec   8            .11E0     275           0.001249021135623771   0.001444400495666479   0.0\n",
-            "     2025-04-09 03:14:04  0.070 sec   9            .77E-1    275           0.0012391471603972065  0.0014443316385074423  0.0\n",
-            "     2025-04-09 03:14:04  0.078 sec   10           .56E-1    275           0.0012281803781232072  0.001443969995714738   0.0\n",
-            "---  ---                  ---         ---          ---       ---           ---                    ---                    ---      ---           ---                  ---                    ---                   ---                  ---                  ---                    ---                   ---\n",
-            "     2025-04-09 03:14:04  0.239 sec   21           .17E-2    275           0.0010857101545965976  0.0013699239792485244  0.0\n",
-            "     2025-04-09 03:14:04  0.256 sec   22           .12E-2    275           0.0010788151914109903  0.0013608943042129659  0.0\n",
-            "     2025-04-09 03:14:04  0.277 sec   23           .9E-3     275           0.0010732409852355024  0.0013528110231003458  0.0\n",
-            "     2025-04-09 03:14:04  0.293 sec   24           .66E-3    275           0.001068839395335129   0.0013459525167893146  0.0\n",
-            "     2025-04-09 03:14:04  0.309 sec   25           .48E-3    275           0.0010654969656511352  0.0013404142035307646  0.0\n",
-            "     2025-04-09 03:14:04  0.325 sec   26           .35E-3    275           0.0010629086451908025  0.0013355660189144987  0.0\n",
-            "     2025-04-09 03:14:04  0.336 sec   27           .25E-3    275           0.001061150629767864   0.001332952683213218   0.0\n",
-            "     2025-04-09 03:14:04  0.346 sec   28           .18E-3    275           0.0010597694546588166  0.001330046483540754   0.0\n",
-            "     2025-04-09 03:14:04  0.355 sec   29           .13E-3    275           0.0010588143429529815  0.0013283005984833915  0.0\n",
-            "     2025-04-09 03:14:04  0.363 sec   30           .97E-4    275           0.0010580943166911606  0.0013270200959632262  0.0\n",
-            "[30 rows x 18 columns]\n",
-            "\n",
+            "    timestamp            duration          training_speed    epochs    iterations    samples    training_rmse    training_deviance    training_mae    training_r2    validation_rmse    validation_deviance    validation_mae    validation_r2\n",
+            "--  -------------------  ----------------  ----------------  --------  ------------  ---------  ---------------  -------------------  --------------  -------------  -----------------  ---------------------  ----------------  ---------------\n",
+            "    2025-04-09 13:00:31  0.000 sec                           0         0             0          nan              nan                  nan             nan            nan                nan                    nan               nan\n",
+            "    2025-04-09 13:00:34  4.094 sec         1088 obs/sec      3.96154   1             3296       329.146          108337               238.841         0.958791       326.899            106863                 254.702           0.981018\n",
+            "    2025-04-09 13:00:46  15.511 sec        919 obs/sec       15.8281   4             13169      265.303          70385.7              173.106         0.973227       264.344            69877.5                180.399           0.987588\n",
+            "    2025-04-09 13:00:52  20.781 sec        2530 obs/sec      59.3654   15            49392      293.627          86216.6              158.842         0.967205       238.41             56839.2                161.131           0.989904\n",
+            "    2025-04-09 13:00:57  26.630 sec        3128 obs/sec      94.9639   24            79010      261.137          68192.5              154.536         0.974061       241.418            58282.7                171.948           0.989647\n",
+            "    2025-04-09 13:01:02  31.644 sec        4029 obs/sec      146.369   37            121779     251.449          63226.5              152.988         0.97595        261.663            68467.4                181.197           0.987838\n",
+            "    2025-04-09 13:01:08  37.021 sec        4721 obs/sec      201.74    51            167848     253.406          64214.7              133.288         0.975574       226.851            51461.4                167.281           0.990859\n",
+            "    2025-04-09 13:01:13  42.760 sec        4714 obs/sec      233.382   59            194174     260.461          67840                146.583         0.974195       240.477            57829.3                170.298           0.989728\n",
+            "    2025-04-09 13:01:19  47.748 sec        4996 obs/sec      276.888   70            230371     155.77           24264.4              113.878         0.99077        154.865            23983.3                108.084           0.99574\n",
+            "    2025-04-09 13:01:24  52.900 sec        5334 obs/sec      328.294   83            273141     157.541          24819.2              113.043         0.990559       148.56             22070                  105.759           0.99608\n",
+            "    2025-04-09 13:01:29  58.604 sec        5271 obs/sec      359.929   91            299461     164.109          26931.9              113.719         0.989756       182.789            33411.8                132.871           0.994065\n",
+            "    2025-04-09 13:01:34  1 min  3.650 sec  5483 obs/sec      407.332   103           338900     118.9            14137.2              87.5482         0.994623       146.528            21470.3                106.611           0.996186\n",
+            "    2025-04-09 13:01:40  1 min  8.899 sec  5847 obs/sec      470.618   119           391554     147.334          21707.3              106.546         0.991743       165.375            27348.9                120.024           0.995142\n",
+            "    2025-04-09 13:01:45  1 min 14.389 sec  5771 obs/sec      502.234   127           417859     136.462          18622                103.431         0.992917       124.274            15444                  92.95             0.997257\n",
+            "    2025-04-09 13:01:51  1 min 19.835 sec  5838 obs/sec      545.746   138           454061     154.782          23957.4              108.395         0.990887       238.007            56647.3                171.409           0.989938\n",
+            "    2025-04-09 13:01:56  1 min 24.856 sec  5845 obs/sec      581.304   147           483645     125.729          15807.8              99.7501         0.993987       136.658            18675.4                103.144           0.996683\n",
+            "    2025-04-09 13:02:01  1 min 29.937 sec  5813 obs/sec      612.925   155           509954     138.101          19071.9              107.809         0.992746       155.658            24229.3                122.639           0.995696\n",
+            "    2025-04-09 13:02:01  1 min 30.040 sec  5811 obs/sec      612.925   155           509954     136.462          18622                103.431         0.992917       124.274            15444                  92.95             0.997257\n",
             "\n",
             "Variable Importances: \n",
-            "variable                 relative_importance     scaled_importance      percentage\n",
-            "-----------------------  ----------------------  ---------------------  ----------------------\n",
-            "NIFTY50_open             0.08282265067100525     1.0                    0.041469033073126174\n",
-            "NIFTY50_close            0.08248914033174515     0.9959731989189178     0.041302045525915876\n",
-            "SMA20                    0.060375671833753586    0.7289753629545456     0.030229903435856208\n",
-            "RESULT DATE open price   0.05250019580125809     0.6338869303978637     0.026286678081291443\n",
-            "Revenue.5968.0           0.03815320134162903     0.460661437813482      0.019103184400201142\n",
-            "Financing Profit.1090.0  0.03791666030883789     0.4578054433376381     0.01898474907082571\n",
-            "Borrowing.25373.0        0.037626370787620544    0.45430049005655493    0.018839402047292706\n",
-            "Profit before tax        0.024486001580953598    0.29564378080845116    0.012260061724209728\n",
-            "Borrowing.84879.0        0.020127175375819206    0.2430153492161218     0.010077611553920663\n",
-            "Financing Profit.3402.0  0.02012552134692669     0.24299537848493757    0.01007678338700869\n",
-            "---                      ---                     ---                    ---\n",
-            "Revenue.13670.0          0.0002703705686144531   0.0032644520119072336  0.00013537366844741436\n",
-            "Equity Capital           0.00024331695749424398  0.002937806934732952   0.00012182801293890021\n",
-            "Financing Profit.2579.0  0.0002423428959446028   0.002926046123629351   0.0001213403034742782\n",
-            "Borrowing.20393.0        0.00022058286413084716  0.002663315679270687   0.00011044512598785162\n",
-            "Borrowing.88449.0        0.00017022785323206335  0.0020553296936638267  8.523253504272353e-05\n",
-            "OPM %                    0.00014307854871731251  0.0017275292152343755  7.163896616134603e-05\n",
-            "Borrowing.40901.0        0.00013102241791784763  0.0015819635915578863  6.560250049879546e-05\n",
-            "Financing Profit.5040.0  0.0001278938725590706   0.0015441895607410689  6.403604796554757e-05\n",
-            "Revenue.10557.0          0.00010531242878641933  0.0012715413951764206  5.2729592170419995e-05\n",
-            "Financing Margin %.24.0  8.402803359786049e-05   0.0010145537835011265  4.207256440247351e-05\n",
-            "[274 rows x 4 columns]\n",
+            "variable                        relative_importance    scaled_importance    percentage\n",
+            "------------------------------  ---------------------  -------------------  ---------------------\n",
+            "RESULT DATE open price          1.0                    1.0                  0.010828321493908447\n",
+            "SMA20                           0.929417073726654      0.929417073726654    0.01006402687623982\n",
+            "SMA50                           0.8927490711212158     0.8927490711212158   0.009666973955488663\n",
+            "Tax %                           0.6537733674049377     0.6537733674049377   0.007079268206415791\n",
+            "Equity Capital                  0.46502426266670227    0.46502426266670227  0.00503543221862278\n",
+            "Financing Profit.3263.0         0.41729432344436646    0.41729432344436646  0.004518597091838617\n",
+            "CWIP                            0.41293013095855713    0.41293013095855713  0.004471340212540974\n",
+            "Revenue.45748.0                 0.399463027715683      0.399463027715683    0.004325514089035476\n",
+            "Deposits.384793.0               0.3986609876155853     0.3986609876155853   0.004316829340980612\n",
+            "Borrowing.206214.0              0.39752906560897827    0.39752906560897827  0.0043045725255870405\n",
+            "---                             ---                    ---                  ---\n",
+            "NIFTY50_close                   0.127555713057518      0.127555713057518    0.0013812142693715407\n",
+            "NIFTY50_open                    0.1233787015080452     0.1233787015080452   0.0013359842454300803\n",
+            "NIFTY50_volume                  0.12220636010169983    0.12220636010169983  0.001323289755781552\n",
+            "inflation                       0.11123748123645782    0.11123748123645782  0.0012045152090009738\n",
+            "gdp_growth                      0.10398408770561218    0.10398408770561218  0.0011259731319271415\n",
+            "Revenue.missing(NA)             0.0                    0.0                  0.0\n",
+            "Financing Profit.missing(NA)    0.0                    0.0                  0.0\n",
+            "Borrowing.missing(NA)           0.0                    0.0                  0.0\n",
+            "Financing Margin %.missing(NA)  0.0                    0.0                  0.0\n",
+            "Deposits.missing(NA)            0.0                    0.0                  0.0\n",
+            "[279 rows x 4 columns]\n",
             "\n"
           ]
         }
@@ -1158,9 +1157,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "Msn85zf7ivA7",
-        "outputId": "20f210df-3dca-40c2-8eb6-84243cabc252"
+        "outputId": "f5edfc17-cedf-4f13-9b32-589cfec09f47"
       },
-      "execution_count": 20,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -1168,97 +1167,82 @@
           "text": [
             "Model Details\n",
             "=============\n",
-            "H2OGeneralizedLinearEstimator : Generalized Linear Modeling\n",
-            "Model Key: GLM_1_AutoML_1_20250409_31401\n",
+            "H2ODeepLearningEstimator : Deep Learning\n",
+            "Model Key: DeepLearning_grid_1_AutoML_1_20250409_125927_model_1\n",
             "\n",
             "\n",
-            "GLM Model: summary\n",
-            "    family    link      regularization               lambda_search                                                                number_of_predictors_total    number_of_active_predictors    number_of_iterations    training_frame\n",
-            "--  --------  --------  ---------------------------  ---------------------------------------------------------------------------  ----------------------------  -----------------------------  ----------------------  ----------------------------------------------\n",
-            "    gaussian  identity  Ridge ( lambda = 9.745E-5 )  nlambda = 30, lambda.max = 0.9745, lambda.min = 9.745E-5, lambda.1se = -1.0  274                           274                            30                      AutoML_1_20250409_31401_training_py_1_sid_8560\n",
+            "Status of Neuron Layers: predicting RESULT DATE+1 open price, regression, gaussian distribution, Quadratic loss, 28,101 weights/biases, 345.4 KB, 509,954 training samples, mini-batch size 1\n",
+            "    layer    units    type              dropout    l1    l2    mean_rate              rate_rms               momentum    mean_weight             weight_rms           mean_bias             bias_rms\n",
+            "--  -------  -------  ----------------  ---------  ----  ----  ---------------------  ---------------------  ----------  ----------------------  -------------------  --------------------  -----------------------\n",
+            "    1        279      Input             15.0\n",
+            "    2        100      RectifierDropout  10.0       0.0   0.0   0.0469522964567144     0.1403437852859497     0.0         -0.0034428720702365887  0.0753689706325531   0.11255552116543806   0.12360426783561707\n",
+            "    3        1        Linear                       0.0   0.0   0.0022405613915179854  0.0017097294330596924  0.0         0.0212319300416857      0.10420235991477966  -0.25282336328233784  1.0971281125650402e-154\n",
             "\n",
-            "ModelMetricsRegressionGLM: glm\n",
+            "ModelMetricsRegression: deeplearning\n",
             "** Reported on train data. **\n",
             "\n",
-            "MSE: 0.0010580943167378097\n",
-            "RMSE: 0.03252836172846413\n",
-            "MAE: 0.024110780685911083\n",
-            "RMSLE: 0.03241020650634541\n",
-            "Mean Residual Deviance: 0.0010580943167378097\n",
-            "R^2: 0.25753895049458075\n",
-            "Null degrees of freedom: 831\n",
-            "Residual degrees of freedom: 557\n",
-            "Null deviance: 1.1856978519105883\n",
-            "Residual deviance: 0.8803344715258576\n",
-            "AIC: -2787.156069063324\n",
+            "MSE: 18621.99884420956\n",
+            "RMSE: 136.46244481251816\n",
+            "MAE: 103.43077946092374\n",
+            "RMSLE: NaN\n",
+            "Mean Residual Deviance: 18621.99884420956\n",
             "\n",
-            "ModelMetricsRegressionGLM: glm\n",
+            "ModelMetricsRegression: deeplearning\n",
             "** Reported on validation data. **\n",
             "\n",
-            "MSE: 0.0013270200694665854\n",
-            "RMSE: 0.03642828666663566\n",
-            "MAE: 0.026946774321347674\n",
-            "RMSLE: 0.036857486881133425\n",
-            "Mean Residual Deviance: 0.0013270200694665854\n",
-            "R^2: 0.14759572067993898\n",
-            "Null degrees of freedom: 102\n",
-            "Residual degrees of freedom: -172\n",
-            "Null deviance: 0.1620866949097968\n",
-            "Residual deviance: 0.1366830671550583\n",
-            "AIC: 161.94493965996077\n",
+            "MSE: 15443.961443829116\n",
+            "RMSE: 124.27373593736175\n",
+            "MAE: 92.95004899710531\n",
+            "RMSLE: 0.18628335185822448\n",
+            "Mean Residual Deviance: 15443.961443829116\n",
             "\n",
             "Scoring History: \n",
-            "     timestamp            duration    iteration    lambda    predictors    deviance_train         deviance_test          alpha    iterations    training_rmse        training_deviance      training_mae          training_r2          validation_rmse      validation_deviance    validation_mae        validation_r2\n",
-            "---  -------------------  ----------  -----------  --------  ------------  ---------------------  ---------------------  -------  ------------  -------------------  ---------------------  --------------------  -------------------  -------------------  ---------------------  --------------------  -------------------\n",
-            "     2025-04-09 03:14:04  0.000 sec   1            .97E0     275           0.0013143170253908818  0.0014668972619771203  0.0\n",
-            "     2025-04-09 03:14:04  0.012 sec   2            .71E0     275           0.0013026510754653006  0.0014597595689484302  0.0\n",
-            "     2025-04-09 03:14:04  0.020 sec   3            .52E0     275           0.0012919179627242602  0.0014533674486297142  0.0\n",
-            "     2025-04-09 03:14:04  0.031 sec   4            .38E0     275           0.0012823742209381152  0.0014490082774998843  0.0\n",
-            "     2025-04-09 03:14:04  0.038 sec   5            .27E0     275           0.0012738192872080816  0.0014462109550775293  0.0      5             0.03252836172846413  0.0010580943167378097  0.024110780685911083  0.25753895049458075  0.03642828666663566  0.0013270200694665854  0.026946774321347674  0.14759572067993898\n",
-            "     2025-04-09 03:14:04  0.046 sec   6            .2E0      275           0.0012658973200284059  0.0014449775021809143  0.0\n",
-            "     2025-04-09 03:14:04  0.056 sec   7            .14E0     275           0.0012580738468318943  0.0014445475579979554  0.0\n",
-            "     2025-04-09 03:14:04  0.063 sec   8            .11E0     275           0.001249021135623771   0.001444400495666479   0.0\n",
-            "     2025-04-09 03:14:04  0.070 sec   9            .77E-1    275           0.0012391471603972065  0.0014443316385074423  0.0\n",
-            "     2025-04-09 03:14:04  0.078 sec   10           .56E-1    275           0.0012281803781232072  0.001443969995714738   0.0\n",
-            "---  ---                  ---         ---          ---       ---           ---                    ---                    ---      ---           ---                  ---                    ---                   ---                  ---                  ---                    ---                   ---\n",
-            "     2025-04-09 03:14:04  0.239 sec   21           .17E-2    275           0.0010857101545965976  0.0013699239792485244  0.0\n",
-            "     2025-04-09 03:14:04  0.256 sec   22           .12E-2    275           0.0010788151914109903  0.0013608943042129659  0.0\n",
-            "     2025-04-09 03:14:04  0.277 sec   23           .9E-3     275           0.0010732409852355024  0.0013528110231003458  0.0\n",
-            "     2025-04-09 03:14:04  0.293 sec   24           .66E-3    275           0.001068839395335129   0.0013459525167893146  0.0\n",
-            "     2025-04-09 03:14:04  0.309 sec   25           .48E-3    275           0.0010654969656511352  0.0013404142035307646  0.0\n",
-            "     2025-04-09 03:14:04  0.325 sec   26           .35E-3    275           0.0010629086451908025  0.0013355660189144987  0.0\n",
-            "     2025-04-09 03:14:04  0.336 sec   27           .25E-3    275           0.001061150629767864   0.001332952683213218   0.0\n",
-            "     2025-04-09 03:14:04  0.346 sec   28           .18E-3    275           0.0010597694546588166  0.001330046483540754   0.0\n",
-            "     2025-04-09 03:14:04  0.355 sec   29           .13E-3    275           0.0010588143429529815  0.0013283005984833915  0.0\n",
-            "     2025-04-09 03:14:04  0.363 sec   30           .97E-4    275           0.0010580943166911606  0.0013270200959632262  0.0\n",
-            "[30 rows x 18 columns]\n",
-            "\n",
+            "    timestamp            duration          training_speed    epochs    iterations    samples    training_rmse    training_deviance    training_mae    training_r2    validation_rmse    validation_deviance    validation_mae    validation_r2\n",
+            "--  -------------------  ----------------  ----------------  --------  ------------  ---------  ---------------  -------------------  --------------  -------------  -----------------  ---------------------  ----------------  ---------------\n",
+            "    2025-04-09 13:00:31  0.000 sec                           0         0             0          nan              nan                  nan             nan            nan                nan                    nan               nan\n",
+            "    2025-04-09 13:00:34  4.094 sec         1088 obs/sec      3.96154   1             3296       329.146          108337               238.841         0.958791       326.899            106863                 254.702           0.981018\n",
+            "    2025-04-09 13:00:46  15.511 sec        919 obs/sec       15.8281   4             13169      265.303          70385.7              173.106         0.973227       264.344            69877.5                180.399           0.987588\n",
+            "    2025-04-09 13:00:52  20.781 sec        2530 obs/sec      59.3654   15            49392      293.627          86216.6              158.842         0.967205       238.41             56839.2                161.131           0.989904\n",
+            "    2025-04-09 13:00:57  26.630 sec        3128 obs/sec      94.9639   24            79010      261.137          68192.5              154.536         0.974061       241.418            58282.7                171.948           0.989647\n",
+            "    2025-04-09 13:01:02  31.644 sec        4029 obs/sec      146.369   37            121779     251.449          63226.5              152.988         0.97595        261.663            68467.4                181.197           0.987838\n",
+            "    2025-04-09 13:01:08  37.021 sec        4721 obs/sec      201.74    51            167848     253.406          64214.7              133.288         0.975574       226.851            51461.4                167.281           0.990859\n",
+            "    2025-04-09 13:01:13  42.760 sec        4714 obs/sec      233.382   59            194174     260.461          67840                146.583         0.974195       240.477            57829.3                170.298           0.989728\n",
+            "    2025-04-09 13:01:19  47.748 sec        4996 obs/sec      276.888   70            230371     155.77           24264.4              113.878         0.99077        154.865            23983.3                108.084           0.99574\n",
+            "    2025-04-09 13:01:24  52.900 sec        5334 obs/sec      328.294   83            273141     157.541          24819.2              113.043         0.990559       148.56             22070                  105.759           0.99608\n",
+            "    2025-04-09 13:01:29  58.604 sec        5271 obs/sec      359.929   91            299461     164.109          26931.9              113.719         0.989756       182.789            33411.8                132.871           0.994065\n",
+            "    2025-04-09 13:01:34  1 min  3.650 sec  5483 obs/sec      407.332   103           338900     118.9            14137.2              87.5482         0.994623       146.528            21470.3                106.611           0.996186\n",
+            "    2025-04-09 13:01:40  1 min  8.899 sec  5847 obs/sec      470.618   119           391554     147.334          21707.3              106.546         0.991743       165.375            27348.9                120.024           0.995142\n",
+            "    2025-04-09 13:01:45  1 min 14.389 sec  5771 obs/sec      502.234   127           417859     136.462          18622                103.431         0.992917       124.274            15444                  92.95             0.997257\n",
+            "    2025-04-09 13:01:51  1 min 19.835 sec  5838 obs/sec      545.746   138           454061     154.782          23957.4              108.395         0.990887       238.007            56647.3                171.409           0.989938\n",
+            "    2025-04-09 13:01:56  1 min 24.856 sec  5845 obs/sec      581.304   147           483645     125.729          15807.8              99.7501         0.993987       136.658            18675.4                103.144           0.996683\n",
+            "    2025-04-09 13:02:01  1 min 29.937 sec  5813 obs/sec      612.925   155           509954     138.101          19071.9              107.809         0.992746       155.658            24229.3                122.639           0.995696\n",
+            "    2025-04-09 13:02:01  1 min 30.040 sec  5811 obs/sec      612.925   155           509954     136.462          18622                103.431         0.992917       124.274            15444                  92.95             0.997257\n",
             "\n",
             "Variable Importances: \n",
-            "variable                 relative_importance     scaled_importance      percentage\n",
-            "-----------------------  ----------------------  ---------------------  ----------------------\n",
-            "NIFTY50_open             0.08282265067100525     1.0                    0.041469033073126174\n",
-            "NIFTY50_close            0.08248914033174515     0.9959731989189178     0.041302045525915876\n",
-            "SMA20                    0.060375671833753586    0.7289753629545456     0.030229903435856208\n",
-            "RESULT DATE open price   0.05250019580125809     0.6338869303978637     0.026286678081291443\n",
-            "Revenue.5968.0           0.03815320134162903     0.460661437813482      0.019103184400201142\n",
-            "Financing Profit.1090.0  0.03791666030883789     0.4578054433376381     0.01898474907082571\n",
-            "Borrowing.25373.0        0.037626370787620544    0.45430049005655493    0.018839402047292706\n",
-            "Profit before tax        0.024486001580953598    0.29564378080845116    0.012260061724209728\n",
-            "Borrowing.84879.0        0.020127175375819206    0.2430153492161218     0.010077611553920663\n",
-            "Financing Profit.3402.0  0.02012552134692669     0.24299537848493757    0.01007678338700869\n",
-            "---                      ---                     ---                    ---\n",
-            "Revenue.13670.0          0.0002703705686144531   0.0032644520119072336  0.00013537366844741436\n",
-            "Equity Capital           0.00024331695749424398  0.002937806934732952   0.00012182801293890021\n",
-            "Financing Profit.2579.0  0.0002423428959446028   0.002926046123629351   0.0001213403034742782\n",
-            "Borrowing.20393.0        0.00022058286413084716  0.002663315679270687   0.00011044512598785162\n",
-            "Borrowing.88449.0        0.00017022785323206335  0.0020553296936638267  8.523253504272353e-05\n",
-            "OPM %                    0.00014307854871731251  0.0017275292152343755  7.163896616134603e-05\n",
-            "Borrowing.40901.0        0.00013102241791784763  0.0015819635915578863  6.560250049879546e-05\n",
-            "Financing Profit.5040.0  0.0001278938725590706   0.0015441895607410689  6.403604796554757e-05\n",
-            "Revenue.10557.0          0.00010531242878641933  0.0012715413951764206  5.2729592170419995e-05\n",
-            "Financing Margin %.24.0  8.402803359786049e-05   0.0010145537835011265  4.207256440247351e-05\n",
-            "[274 rows x 4 columns]\n",
+            "variable                        relative_importance    scaled_importance    percentage\n",
+            "------------------------------  ---------------------  -------------------  ---------------------\n",
+            "RESULT DATE open price          1.0                    1.0                  0.010828321493908447\n",
+            "SMA20                           0.929417073726654      0.929417073726654    0.01006402687623982\n",
+            "SMA50                           0.8927490711212158     0.8927490711212158   0.009666973955488663\n",
+            "Tax %                           0.6537733674049377     0.6537733674049377   0.007079268206415791\n",
+            "Equity Capital                  0.46502426266670227    0.46502426266670227  0.00503543221862278\n",
+            "Financing Profit.3263.0         0.41729432344436646    0.41729432344436646  0.004518597091838617\n",
+            "CWIP                            0.41293013095855713    0.41293013095855713  0.004471340212540974\n",
+            "Revenue.45748.0                 0.399463027715683      0.399463027715683    0.004325514089035476\n",
+            "Deposits.384793.0               0.3986609876155853     0.3986609876155853   0.004316829340980612\n",
+            "Borrowing.206214.0              0.39752906560897827    0.39752906560897827  0.0043045725255870405\n",
+            "---                             ---                    ---                  ---\n",
+            "NIFTY50_close                   0.127555713057518      0.127555713057518    0.0013812142693715407\n",
+            "NIFTY50_open                    0.1233787015080452     0.1233787015080452   0.0013359842454300803\n",
+            "NIFTY50_volume                  0.12220636010169983    0.12220636010169983  0.001323289755781552\n",
+            "inflation                       0.11123748123645782    0.11123748123645782  0.0012045152090009738\n",
+            "gdp_growth                      0.10398408770561218    0.10398408770561218  0.0011259731319271415\n",
+            "Revenue.missing(NA)             0.0                    0.0                  0.0\n",
+            "Financing Profit.missing(NA)    0.0                    0.0                  0.0\n",
+            "Borrowing.missing(NA)           0.0                    0.0                  0.0\n",
+            "Financing Margin %.missing(NA)  0.0                    0.0                  0.0\n",
+            "Deposits.missing(NA)            0.0                    0.0                  0.0\n",
+            "[279 rows x 4 columns]\n",
             "\n"
           ]
         }
@@ -1276,9 +1260,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "unuAUt7zi3ur",
-        "outputId": "ac596f49-09b2-4e88-f2f6-920a1e390277"
+        "outputId": "75c878ff-60df-44cb-f5f8-eae96126e1f0"
       },
-      "execution_count": 21,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -1286,97 +1270,82 @@
           "text": [
             "Model Details\n",
             "=============\n",
-            "H2OGeneralizedLinearEstimator : Generalized Linear Modeling\n",
-            "Model Key: GLM_1_AutoML_1_20250409_31401\n",
+            "H2ODeepLearningEstimator : Deep Learning\n",
+            "Model Key: DeepLearning_grid_1_AutoML_1_20250409_125927_model_1\n",
             "\n",
             "\n",
-            "GLM Model: summary\n",
-            "    family    link      regularization               lambda_search                                                                number_of_predictors_total    number_of_active_predictors    number_of_iterations    training_frame\n",
-            "--  --------  --------  ---------------------------  ---------------------------------------------------------------------------  ----------------------------  -----------------------------  ----------------------  ----------------------------------------------\n",
-            "    gaussian  identity  Ridge ( lambda = 9.745E-5 )  nlambda = 30, lambda.max = 0.9745, lambda.min = 9.745E-5, lambda.1se = -1.0  274                           274                            30                      AutoML_1_20250409_31401_training_py_1_sid_8560\n",
+            "Status of Neuron Layers: predicting RESULT DATE+1 open price, regression, gaussian distribution, Quadratic loss, 28,101 weights/biases, 345.4 KB, 509,954 training samples, mini-batch size 1\n",
+            "    layer    units    type              dropout    l1    l2    mean_rate              rate_rms               momentum    mean_weight             weight_rms           mean_bias             bias_rms\n",
+            "--  -------  -------  ----------------  ---------  ----  ----  ---------------------  ---------------------  ----------  ----------------------  -------------------  --------------------  -----------------------\n",
+            "    1        279      Input             15.0\n",
+            "    2        100      RectifierDropout  10.0       0.0   0.0   0.0469522964567144     0.1403437852859497     0.0         -0.0034428720702365887  0.0753689706325531   0.11255552116543806   0.12360426783561707\n",
+            "    3        1        Linear                       0.0   0.0   0.0022405613915179854  0.0017097294330596924  0.0         0.0212319300416857      0.10420235991477966  -0.25282336328233784  1.0971281125650402e-154\n",
             "\n",
-            "ModelMetricsRegressionGLM: glm\n",
+            "ModelMetricsRegression: deeplearning\n",
             "** Reported on train data. **\n",
             "\n",
-            "MSE: 0.0010580943167378097\n",
-            "RMSE: 0.03252836172846413\n",
-            "MAE: 0.024110780685911083\n",
-            "RMSLE: 0.03241020650634541\n",
-            "Mean Residual Deviance: 0.0010580943167378097\n",
-            "R^2: 0.25753895049458075\n",
-            "Null degrees of freedom: 831\n",
-            "Residual degrees of freedom: 557\n",
-            "Null deviance: 1.1856978519105883\n",
-            "Residual deviance: 0.8803344715258576\n",
-            "AIC: -2787.156069063324\n",
+            "MSE: 18621.99884420956\n",
+            "RMSE: 136.46244481251816\n",
+            "MAE: 103.43077946092374\n",
+            "RMSLE: NaN\n",
+            "Mean Residual Deviance: 18621.99884420956\n",
             "\n",
-            "ModelMetricsRegressionGLM: glm\n",
+            "ModelMetricsRegression: deeplearning\n",
             "** Reported on validation data. **\n",
             "\n",
-            "MSE: 0.0013270200694665854\n",
-            "RMSE: 0.03642828666663566\n",
-            "MAE: 0.026946774321347674\n",
-            "RMSLE: 0.036857486881133425\n",
-            "Mean Residual Deviance: 0.0013270200694665854\n",
-            "R^2: 0.14759572067993898\n",
-            "Null degrees of freedom: 102\n",
-            "Residual degrees of freedom: -172\n",
-            "Null deviance: 0.1620866949097968\n",
-            "Residual deviance: 0.1366830671550583\n",
-            "AIC: 161.94493965996077\n",
+            "MSE: 15443.961443829116\n",
+            "RMSE: 124.27373593736175\n",
+            "MAE: 92.95004899710531\n",
+            "RMSLE: 0.18628335185822448\n",
+            "Mean Residual Deviance: 15443.961443829116\n",
             "\n",
             "Scoring History: \n",
-            "     timestamp            duration    iteration    lambda    predictors    deviance_train         deviance_test          alpha    iterations    training_rmse        training_deviance      training_mae          training_r2          validation_rmse      validation_deviance    validation_mae        validation_r2\n",
-            "---  -------------------  ----------  -----------  --------  ------------  ---------------------  ---------------------  -------  ------------  -------------------  ---------------------  --------------------  -------------------  -------------------  ---------------------  --------------------  -------------------\n",
-            "     2025-04-09 03:14:04  0.000 sec   1            .97E0     275           0.0013143170253908818  0.0014668972619771203  0.0\n",
-            "     2025-04-09 03:14:04  0.012 sec   2            .71E0     275           0.0013026510754653006  0.0014597595689484302  0.0\n",
-            "     2025-04-09 03:14:04  0.020 sec   3            .52E0     275           0.0012919179627242602  0.0014533674486297142  0.0\n",
-            "     2025-04-09 03:14:04  0.031 sec   4            .38E0     275           0.0012823742209381152  0.0014490082774998843  0.0\n",
-            "     2025-04-09 03:14:04  0.038 sec   5            .27E0     275           0.0012738192872080816  0.0014462109550775293  0.0      5             0.03252836172846413  0.0010580943167378097  0.024110780685911083  0.25753895049458075  0.03642828666663566  0.0013270200694665854  0.026946774321347674  0.14759572067993898\n",
-            "     2025-04-09 03:14:04  0.046 sec   6            .2E0      275           0.0012658973200284059  0.0014449775021809143  0.0\n",
-            "     2025-04-09 03:14:04  0.056 sec   7            .14E0     275           0.0012580738468318943  0.0014445475579979554  0.0\n",
-            "     2025-04-09 03:14:04  0.063 sec   8            .11E0     275           0.001249021135623771   0.001444400495666479   0.0\n",
-            "     2025-04-09 03:14:04  0.070 sec   9            .77E-1    275           0.0012391471603972065  0.0014443316385074423  0.0\n",
-            "     2025-04-09 03:14:04  0.078 sec   10           .56E-1    275           0.0012281803781232072  0.001443969995714738   0.0\n",
-            "---  ---                  ---         ---          ---       ---           ---                    ---                    ---      ---           ---                  ---                    ---                   ---                  ---                  ---                    ---                   ---\n",
-            "     2025-04-09 03:14:04  0.239 sec   21           .17E-2    275           0.0010857101545965976  0.0013699239792485244  0.0\n",
-            "     2025-04-09 03:14:04  0.256 sec   22           .12E-2    275           0.0010788151914109903  0.0013608943042129659  0.0\n",
-            "     2025-04-09 03:14:04  0.277 sec   23           .9E-3     275           0.0010732409852355024  0.0013528110231003458  0.0\n",
-            "     2025-04-09 03:14:04  0.293 sec   24           .66E-3    275           0.001068839395335129   0.0013459525167893146  0.0\n",
-            "     2025-04-09 03:14:04  0.309 sec   25           .48E-3    275           0.0010654969656511352  0.0013404142035307646  0.0\n",
-            "     2025-04-09 03:14:04  0.325 sec   26           .35E-3    275           0.0010629086451908025  0.0013355660189144987  0.0\n",
-            "     2025-04-09 03:14:04  0.336 sec   27           .25E-3    275           0.001061150629767864   0.001332952683213218   0.0\n",
-            "     2025-04-09 03:14:04  0.346 sec   28           .18E-3    275           0.0010597694546588166  0.001330046483540754   0.0\n",
-            "     2025-04-09 03:14:04  0.355 sec   29           .13E-3    275           0.0010588143429529815  0.0013283005984833915  0.0\n",
-            "     2025-04-09 03:14:04  0.363 sec   30           .97E-4    275           0.0010580943166911606  0.0013270200959632262  0.0\n",
-            "[30 rows x 18 columns]\n",
-            "\n",
+            "    timestamp            duration          training_speed    epochs    iterations    samples    training_rmse    training_deviance    training_mae    training_r2    validation_rmse    validation_deviance    validation_mae    validation_r2\n",
+            "--  -------------------  ----------------  ----------------  --------  ------------  ---------  ---------------  -------------------  --------------  -------------  -----------------  ---------------------  ----------------  ---------------\n",
+            "    2025-04-09 13:00:31  0.000 sec                           0         0             0          nan              nan                  nan             nan            nan                nan                    nan               nan\n",
+            "    2025-04-09 13:00:34  4.094 sec         1088 obs/sec      3.96154   1             3296       329.146          108337               238.841         0.958791       326.899            106863                 254.702           0.981018\n",
+            "    2025-04-09 13:00:46  15.511 sec        919 obs/sec       15.8281   4             13169      265.303          70385.7              173.106         0.973227       264.344            69877.5                180.399           0.987588\n",
+            "    2025-04-09 13:00:52  20.781 sec        2530 obs/sec      59.3654   15            49392      293.627          86216.6              158.842         0.967205       238.41             56839.2                161.131           0.989904\n",
+            "    2025-04-09 13:00:57  26.630 sec        3128 obs/sec      94.9639   24            79010      261.137          68192.5              154.536         0.974061       241.418            58282.7                171.948           0.989647\n",
+            "    2025-04-09 13:01:02  31.644 sec        4029 obs/sec      146.369   37            121779     251.449          63226.5              152.988         0.97595        261.663            68467.4                181.197           0.987838\n",
+            "    2025-04-09 13:01:08  37.021 sec        4721 obs/sec      201.74    51            167848     253.406          64214.7              133.288         0.975574       226.851            51461.4                167.281           0.990859\n",
+            "    2025-04-09 13:01:13  42.760 sec        4714 obs/sec      233.382   59            194174     260.461          67840                146.583         0.974195       240.477            57829.3                170.298           0.989728\n",
+            "    2025-04-09 13:01:19  47.748 sec        4996 obs/sec      276.888   70            230371     155.77           24264.4              113.878         0.99077        154.865            23983.3                108.084           0.99574\n",
+            "    2025-04-09 13:01:24  52.900 sec        5334 obs/sec      328.294   83            273141     157.541          24819.2              113.043         0.990559       148.56             22070                  105.759           0.99608\n",
+            "    2025-04-09 13:01:29  58.604 sec        5271 obs/sec      359.929   91            299461     164.109          26931.9              113.719         0.989756       182.789            33411.8                132.871           0.994065\n",
+            "    2025-04-09 13:01:34  1 min  3.650 sec  5483 obs/sec      407.332   103           338900     118.9            14137.2              87.5482         0.994623       146.528            21470.3                106.611           0.996186\n",
+            "    2025-04-09 13:01:40  1 min  8.899 sec  5847 obs/sec      470.618   119           391554     147.334          21707.3              106.546         0.991743       165.375            27348.9                120.024           0.995142\n",
+            "    2025-04-09 13:01:45  1 min 14.389 sec  5771 obs/sec      502.234   127           417859     136.462          18622                103.431         0.992917       124.274            15444                  92.95             0.997257\n",
+            "    2025-04-09 13:01:51  1 min 19.835 sec  5838 obs/sec      545.746   138           454061     154.782          23957.4              108.395         0.990887       238.007            56647.3                171.409           0.989938\n",
+            "    2025-04-09 13:01:56  1 min 24.856 sec  5845 obs/sec      581.304   147           483645     125.729          15807.8              99.7501         0.993987       136.658            18675.4                103.144           0.996683\n",
+            "    2025-04-09 13:02:01  1 min 29.937 sec  5813 obs/sec      612.925   155           509954     138.101          19071.9              107.809         0.992746       155.658            24229.3                122.639           0.995696\n",
+            "    2025-04-09 13:02:01  1 min 30.040 sec  5811 obs/sec      612.925   155           509954     136.462          18622                103.431         0.992917       124.274            15444                  92.95             0.997257\n",
             "\n",
             "Variable Importances: \n",
-            "variable                 relative_importance     scaled_importance      percentage\n",
-            "-----------------------  ----------------------  ---------------------  ----------------------\n",
-            "NIFTY50_open             0.08282265067100525     1.0                    0.041469033073126174\n",
-            "NIFTY50_close            0.08248914033174515     0.9959731989189178     0.041302045525915876\n",
-            "SMA20                    0.060375671833753586    0.7289753629545456     0.030229903435856208\n",
-            "RESULT DATE open price   0.05250019580125809     0.6338869303978637     0.026286678081291443\n",
-            "Revenue.5968.0           0.03815320134162903     0.460661437813482      0.019103184400201142\n",
-            "Financing Profit.1090.0  0.03791666030883789     0.4578054433376381     0.01898474907082571\n",
-            "Borrowing.25373.0        0.037626370787620544    0.45430049005655493    0.018839402047292706\n",
-            "Profit before tax        0.024486001580953598    0.29564378080845116    0.012260061724209728\n",
-            "Borrowing.84879.0        0.020127175375819206    0.2430153492161218     0.010077611553920663\n",
-            "Financing Profit.3402.0  0.02012552134692669     0.24299537848493757    0.01007678338700869\n",
-            "---                      ---                     ---                    ---\n",
-            "Revenue.13670.0          0.0002703705686144531   0.0032644520119072336  0.00013537366844741436\n",
-            "Equity Capital           0.00024331695749424398  0.002937806934732952   0.00012182801293890021\n",
-            "Financing Profit.2579.0  0.0002423428959446028   0.002926046123629351   0.0001213403034742782\n",
-            "Borrowing.20393.0        0.00022058286413084716  0.002663315679270687   0.00011044512598785162\n",
-            "Borrowing.88449.0        0.00017022785323206335  0.0020553296936638267  8.523253504272353e-05\n",
-            "OPM %                    0.00014307854871731251  0.0017275292152343755  7.163896616134603e-05\n",
-            "Borrowing.40901.0        0.00013102241791784763  0.0015819635915578863  6.560250049879546e-05\n",
-            "Financing Profit.5040.0  0.0001278938725590706   0.0015441895607410689  6.403604796554757e-05\n",
-            "Revenue.10557.0          0.00010531242878641933  0.0012715413951764206  5.2729592170419995e-05\n",
-            "Financing Margin %.24.0  8.402803359786049e-05   0.0010145537835011265  4.207256440247351e-05\n",
-            "[274 rows x 4 columns]\n",
+            "variable                        relative_importance    scaled_importance    percentage\n",
+            "------------------------------  ---------------------  -------------------  ---------------------\n",
+            "RESULT DATE open price          1.0                    1.0                  0.010828321493908447\n",
+            "SMA20                           0.929417073726654      0.929417073726654    0.01006402687623982\n",
+            "SMA50                           0.8927490711212158     0.8927490711212158   0.009666973955488663\n",
+            "Tax %                           0.6537733674049377     0.6537733674049377   0.007079268206415791\n",
+            "Equity Capital                  0.46502426266670227    0.46502426266670227  0.00503543221862278\n",
+            "Financing Profit.3263.0         0.41729432344436646    0.41729432344436646  0.004518597091838617\n",
+            "CWIP                            0.41293013095855713    0.41293013095855713  0.004471340212540974\n",
+            "Revenue.45748.0                 0.399463027715683      0.399463027715683    0.004325514089035476\n",
+            "Deposits.384793.0               0.3986609876155853     0.3986609876155853   0.004316829340980612\n",
+            "Borrowing.206214.0              0.39752906560897827    0.39752906560897827  0.0043045725255870405\n",
+            "---                             ---                    ---                  ---\n",
+            "NIFTY50_close                   0.127555713057518      0.127555713057518    0.0013812142693715407\n",
+            "NIFTY50_open                    0.1233787015080452     0.1233787015080452   0.0013359842454300803\n",
+            "NIFTY50_volume                  0.12220636010169983    0.12220636010169983  0.001323289755781552\n",
+            "inflation                       0.11123748123645782    0.11123748123645782  0.0012045152090009738\n",
+            "gdp_growth                      0.10398408770561218    0.10398408770561218  0.0011259731319271415\n",
+            "Revenue.missing(NA)             0.0                    0.0                  0.0\n",
+            "Financing Profit.missing(NA)    0.0                    0.0                  0.0\n",
+            "Borrowing.missing(NA)           0.0                    0.0                  0.0\n",
+            "Financing Margin %.missing(NA)  0.0                    0.0                  0.0\n",
+            "Deposits.missing(NA)            0.0                    0.0                  0.0\n",
+            "[279 rows x 4 columns]\n",
             "\n"
           ]
         }
@@ -1394,15 +1363,15 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "FGleWmKVi7ui",
-        "outputId": "7ffebd89-af98-48f6-812e-a76fca0daec8"
+        "outputId": "8fc8b9b4-1421-4016-a8d0-940d259dd262"
       },
-      "execution_count": 22,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "{'creation_epoch': '1744168441', 'start_epoch': '1744168441', 'start_XGBoost_def_2': '1744168441', 'start_GLM_def_1': '1744168444', 'start_GBM_def_5': '1744168445', 'start_XGBoost_def_1': '1744168446', 'start_DRF_def_1': '1744168447', 'start_GBM_def_2': '1744168450', 'start_GBM_def_3': '1744168451', 'start_GBM_def_4': '1744168451', 'start_XGBoost_def_3': '1744168452', 'start_DRF_XRT': '1744168453', 'start_GBM_def_1': '1744168457', 'start_DeepLearning_def_1': '1744168457', 'start_XGBoost_grid_1': '1744168458', 'start_GBM_grid_1': '1744168468', 'start_DeepLearning_grid_1': '1744168469', 'start_DeepLearning_grid_2': '1744168496', 'start_DeepLearning_grid_3': '1744168525', 'stop_epoch': '1744168554', 'duration_secs': '113'}\n"
+            "{'creation_epoch': '1744203567', 'start_epoch': '1744203567', 'start_XGBoost_def_2': '1744203567', 'start_GLM_def_1': '1744203574', 'start_GBM_def_5': '1744203575', 'start_XGBoost_def_1': '1744203579', 'start_DRF_def_1': '1744203584', 'start_GBM_def_2': '1744203588', 'start_GBM_def_3': '1744203593', 'start_GBM_def_4': '1744203598', 'start_XGBoost_def_3': '1744203600', 'start_DRF_XRT': '1744203602', 'start_GBM_def_1': '1744203606', 'start_DeepLearning_def_1': '1744203610', 'start_XGBoost_grid_1': '1744203612', 'start_GBM_grid_1': '1744203628', 'start_DeepLearning_grid_1': '1744203631', 'start_DeepLearning_grid_2': '1744203721', 'start_DeepLearning_grid_3': '1744203788', 'stop_epoch': '1744203840', 'duration_secs': '273'}\n"
           ]
         }
       ]
@@ -1418,22 +1387,22 @@
           "height": 36
         },
         "id": "ey78FPkhi-Fy",
-        "outputId": "009a179f-0cdb-4fba-f47f-b7fd06d45d9e"
+        "outputId": "529615bc-d692-4163-87b0-1e9111587af0"
       },
-      "execution_count": 23,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "'GLM_1_AutoML_1_20250409_31401'"
+              "'DeepLearning_grid_1_AutoML_1_20250409_125927_model_1'"
             ],
             "application/vnd.google.colaboratory.intrinsic+json": {
               "type": "string"
             }
           },
           "metadata": {},
-          "execution_count": 23
+          "execution_count": 18
         }
       ]
     },
@@ -1449,57 +1418,57 @@
           "height": 407
         },
         "id": "4rJxGSmxjNJj",
-        "outputId": "0d82ab97-f5a0-4ee2-9fb5-3bc4d3d07c0c"
+        "outputId": "34b95271-9853-417f-ace2-c969aeab251b"
       },
-      "execution_count": 25,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "glm prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n"
+            "deeplearning prediction progress: |██████████████████████████████████████████████| (done) 100%\n"
           ]
         },
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "    predict\n",
-              "-----------\n",
-              "-0.0160619\n",
-              " 0.0169515\n",
-              "-0.0344784\n",
-              "-0.0224662\n",
-              "-0.0194707\n",
-              "-0.00640431\n",
-              "-0.00632971\n",
-              "-0.0059503\n",
-              "-0.0136392\n",
-              "-0.0183579\n",
+              "  predict\n",
+              "---------\n",
+              "  43.8964\n",
+              " 875.223\n",
+              "1938.47\n",
+              " 139.043\n",
+              " 186.102\n",
+              "3058.43\n",
+              "4610.1\n",
+              " 670.982\n",
+              " 649.744\n",
+              "1009.34\n",
               "[107 rows x 1 column]\n"
             ],
             "text/html": [
               "<table class='dataframe'>\n",
               "<thead>\n",
-              "<tr><th style=\"text-align: right;\">    predict</th></tr>\n",
+              "<tr><th style=\"text-align: right;\">  predict</th></tr>\n",
               "</thead>\n",
               "<tbody>\n",
-              "<tr><td style=\"text-align: right;\">-0.0160619 </td></tr>\n",
-              "<tr><td style=\"text-align: right;\"> 0.0169515 </td></tr>\n",
-              "<tr><td style=\"text-align: right;\">-0.0344784 </td></tr>\n",
-              "<tr><td style=\"text-align: right;\">-0.0224662 </td></tr>\n",
-              "<tr><td style=\"text-align: right;\">-0.0194707 </td></tr>\n",
-              "<tr><td style=\"text-align: right;\">-0.00640431</td></tr>\n",
-              "<tr><td style=\"text-align: right;\">-0.00632971</td></tr>\n",
-              "<tr><td style=\"text-align: right;\">-0.0059503 </td></tr>\n",
-              "<tr><td style=\"text-align: right;\">-0.0136392 </td></tr>\n",
-              "<tr><td style=\"text-align: right;\">-0.0183579 </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">  43.8964</td></tr>\n",
+              "<tr><td style=\"text-align: right;\"> 875.223 </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">1938.47  </td></tr>\n",
+              "<tr><td style=\"text-align: right;\"> 139.043 </td></tr>\n",
+              "<tr><td style=\"text-align: right;\"> 186.102 </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">3058.43  </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">4610.1   </td></tr>\n",
+              "<tr><td style=\"text-align: right;\"> 670.982 </td></tr>\n",
+              "<tr><td style=\"text-align: right;\"> 649.744 </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">1009.34  </td></tr>\n",
               "</tbody>\n",
               "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[107 rows x 1 column]</pre>"
             ]
           },
           "metadata": {},
-          "execution_count": 25
+          "execution_count": 19
         }
       ]
     },
@@ -1525,9 +1494,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "ANnqbZMmjb4J",
-        "outputId": "5962f7a6-e3c1-4ad4-d8bd-5c598f14f1e3"
+        "outputId": "bbb35f38-416e-447f-df3a-6e84a9868b53"
       },
-      "execution_count": 26,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -1536,18 +1505,18 @@
             "\n",
             " TRAIN SET \n",
             "\n",
-            "glm prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n",
-            "{'MAE': 0.024110780659861258, 'RMSE': np.float64(0.03252836172774736)}\n",
+            "deeplearning prediction progress: |██████████████████████████████████████████████| (done) 100%\n",
+            "{'MAE': 103.43077778665219, 'RMSE': np.float64(136.46244339115717), 'MAPE': 0.4104183904685377}\n",
             "\n",
             " VALID SET \n",
             "\n",
-            "glm prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n",
-            "{'MAE': 0.02694677442679907, 'RMSE': np.float64(0.03642828703031789)}\n",
+            "deeplearning prediction progress: |██████████████████████████████████████████████| (done) 100%\n",
+            "{'MAE': 92.95005429027556, 'RMSE': np.float64(124.27373841117173), 'MAPE': 0.11078708574728854}\n",
             "\n",
             " TEST SET \n",
             "\n",
-            "glm prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n",
-            "{'MAE': 0.0323973781877883, 'RMSE': np.float64(0.0456419119128631)}\n"
+            "deeplearning prediction progress: |██████████████████████████████████████████████| (done) 100%\n",
+            "{'MAE': 150.76924019481663, 'RMSE': np.float64(269.1930438865622), 'MAPE': 0.28824375358902776}\n"
           ]
         }
       ]
@@ -1555,7 +1524,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "# Using Numeric and Text Data"
+        "# Using Numeric and Text Data Classifier Probabilities"
       ],
       "metadata": {
         "id": "nvgoFcmyjmaK"
@@ -1581,36 +1550,166 @@
         "valid = final_with_funda[final_with_funda['split'] == 'validation']\n",
         "test = final_with_funda[final_with_funda['split'] == 'test']\n",
         "\n",
-        "x_cols_to_use = ['RESULT DATE open price', 'SMA20', 'SMA50', 'RSI14',\n",
-        "                 'gdp_growth', 'inflation', 'NIFTY50_open', 'NIFTY50_close', 'NIFTY50_volume',\n",
-        "                 'Sales', 'Expenses', 'Operating Profit', 'OPM %', 'Other Income', 'Interest',\n",
-        "                 'Depreciation', 'Profit before tax', 'Tax %', 'Net Profit',\n",
-        "                 'EPS in Rs', 'Dividend Payout %', 'Equity Capital', 'Reserves',\n",
-        "                 'Borrowings', 'Other Liabilities', 'Total Liabilities',\n",
-        "                 'Fixed Assets', 'CWIP', 'Investments', 'Other Assets',\n",
-        "                 'Total Assets', 'Cash from Operating Activity',\n",
-        "                 'Cash from Investing Activity', 'Cash from Financing Activity',\n",
-        "                 'Net Cash Flow', 'Revenue', 'Financing Profit', 'Financing Margin %',\n",
-        "                 'Deposits', 'Borrowing'] + text_features"
+        "x_cols_to_use =  text_features"
       ],
       "metadata": {
-        "id": "3Iit31D4kI85"
+        "id": "3Iit31D4kI85",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "fb380641-a00a-46ad-e18d-b1d701382c92"
       },
-      "execution_count": 27,
-      "outputs": []
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "text_embeddings\n",
+            "text_embeddings_0\n",
+            "text_embeddings_1\n",
+            "text_embeddings_2\n",
+            "text_embeddings_3\n",
+            "text_embeddings_4\n",
+            "text_embeddings_5\n",
+            "text_embeddings_6\n",
+            "text_embeddings_7\n",
+            "text_embeddings_8\n",
+            "text_embeddings_9\n",
+            "text_embeddings_10\n",
+            "text_embeddings_11\n",
+            "text_embeddings_12\n",
+            "text_embeddings_13\n",
+            "text_embeddings_14\n",
+            "text_embeddings_15\n",
+            "text_embeddings_16\n",
+            "text_embeddings_17\n",
+            "text_embeddings_18\n",
+            "text_embeddings_19\n",
+            "text_embeddings_20\n",
+            "text_embeddings_21\n",
+            "text_embeddings_22\n",
+            "text_embeddings_23\n",
+            "text_embeddings_24\n",
+            "text_embeddings_25\n",
+            "text_embeddings_26\n",
+            "text_embeddings_27\n",
+            "text_embeddings_28\n",
+            "text_embeddings_29\n",
+            "text_embeddings_30\n",
+            "text_embeddings_31\n",
+            "text_embeddings_32\n",
+            "text_embeddings_33\n",
+            "text_embeddings_34\n",
+            "text_embeddings_35\n",
+            "text_embeddings_36\n",
+            "text_embeddings_37\n",
+            "text_embeddings_38\n",
+            "text_embeddings_39\n",
+            "text_embeddings_40\n",
+            "text_embeddings_41\n",
+            "text_embeddings_42\n",
+            "text_embeddings_43\n",
+            "text_embeddings_44\n",
+            "text_embeddings_45\n",
+            "text_embeddings_46\n",
+            "text_embeddings_47\n",
+            "text_embeddings_48\n",
+            "text_embeddings_49\n",
+            "text_embeddings_50\n",
+            "text_embeddings_51\n",
+            "text_embeddings_52\n",
+            "text_embeddings_53\n",
+            "text_embeddings_54\n",
+            "text_embeddings_55\n",
+            "text_embeddings_56\n",
+            "text_embeddings_57\n",
+            "text_embeddings_58\n",
+            "text_embeddings_59\n",
+            "text_embeddings_60\n",
+            "text_embeddings_61\n",
+            "text_embeddings_62\n",
+            "text_embeddings_63\n",
+            "text_embeddings_64\n",
+            "text_embeddings_65\n",
+            "text_embeddings_66\n",
+            "text_embeddings_67\n",
+            "text_embeddings_68\n",
+            "text_embeddings_69\n",
+            "text_embeddings_70\n",
+            "text_embeddings_71\n",
+            "text_embeddings_72\n",
+            "text_embeddings_73\n",
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+            "text_embeddings_78\n",
+            "text_embeddings_79\n",
+            "text_embeddings_80\n",
+            "text_embeddings_81\n",
+            "text_embeddings_82\n",
+            "text_embeddings_83\n",
+            "text_embeddings_84\n",
+            "text_embeddings_85\n",
+            "text_embeddings_86\n",
+            "text_embeddings_87\n",
+            "text_embeddings_88\n",
+            "text_embeddings_89\n",
+            "text_embeddings_90\n",
+            "text_embeddings_91\n",
+            "text_embeddings_92\n",
+            "text_embeddings_93\n",
+            "text_embeddings_94\n",
+            "text_embeddings_95\n",
+            "text_embeddings_96\n",
+            "text_embeddings_97\n",
+            "text_embeddings_98\n",
+            "text_embeddings_99\n",
+            "text_embeddings_100\n",
+            "text_embeddings_101\n",
+            "text_embeddings_102\n",
+            "text_embeddings_103\n",
+            "text_embeddings_104\n",
+            "text_embeddings_105\n",
+            "text_embeddings_106\n",
+            "text_embeddings_107\n",
+            "text_embeddings_108\n",
+            "text_embeddings_109\n",
+            "text_embeddings_110\n",
+            "text_embeddings_111\n",
+            "text_embeddings_112\n",
+            "text_embeddings_113\n",
+            "text_embeddings_114\n",
+            "text_embeddings_115\n",
+            "text_embeddings_116\n",
+            "text_embeddings_117\n",
+            "text_embeddings_118\n",
+            "text_embeddings_119\n",
+            "text_embeddings_120\n",
+            "text_embeddings_121\n",
+            "text_embeddings_122\n",
+            "text_embeddings_123\n",
+            "text_embeddings_124\n",
+            "text_embeddings_125\n",
+            "text_embeddings_126\n",
+            "text_embeddings_127\n"
+          ]
+        }
+      ]
     },
     {
       "cell_type": "code",
       "source": [
-        "y_col =  'TARGET-2 REGRESSION NORMALIZED'\n",
+        "y_col = 'classification' #'RESULT DATE+1 open price' #'TARGET-2 REGRESSION NORMALIZED'\n",
         "\n",
         "h2o_train = h2o.H2OFrame(train[x_cols_to_use])  # Convert train DataFrame to H2OFrame\n",
         "h2o_valid = h2o.H2OFrame(valid[x_cols_to_use])  # Convert valid DataFrame to H2OFrame\n",
         "h2o_test = h2o.H2OFrame(test[x_cols_to_use])  # Convert test DataFrame to H2OFrame\n",
         "\n",
-        "h2o_train[y_col] = h2o.H2OFrame(train[y_col].to_list())  # Convert Pandas Series to H2OFrame before assigning\n",
-        "h2o_valid[y_col] = h2o.H2OFrame(valid[y_col].to_list())  # Convert Pandas Series to H2OFrame before assigning\n",
-        "h2o_test[y_col] = h2o.H2OFrame(test[y_col].to_list())  # Convert Pandas Series to H2OFrame before assigning\n",
+        "h2o_train[y_col] = h2o.H2OFrame(train[y_col].to_list()).asfactor()  # Convert Pandas Series to H2OFrame before assigning\n",
+        "h2o_valid[y_col] = h2o.H2OFrame(valid[y_col].to_list()).asfactor()  # Convert Pandas Series to H2OFrame before assigning\n",
+        "h2o_test[y_col] = h2o.H2OFrame(test[y_col].to_list()).asfactor()  # Convert Pandas Series to H2OFrame before assigning\n",
         "\n",
         "aml = H2OAutoML(max_models=20, seed=1,nfolds = 0)\n",
         "aml.train(x=x_cols_to_use, y=y_col, training_frame=h2o_train,validation_frame=h2o_valid)\n",
@@ -1622,12 +1721,12 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 829
+          "height": 844
         },
         "id": "f38xcRbZkpjI",
-        "outputId": "e9837c88-a684-4930-f77d-8965efe27f0e"
+        "outputId": "d537b43e-d6a4-44f7-d742-bd626f4deb7b"
       },
-      "execution_count": 28,
+      "execution_count": 21,
       "outputs": [
         {
           "output_type": "stream",
@@ -1646,62 +1745,62 @@
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "model_id                                                  rmse         mse        mae      rmsle    mean_residual_deviance\n",
-              "---------------------------------------------------  ---------  ----------  ---------  ---------  ------------------------\n",
-              "XRT_1_AutoML_2_20250409_32324                        0.0372215  0.00138544  0.0293038  0.0376997                0.00138544\n",
-              "GBM_5_AutoML_2_20250409_32324                        0.0382062  0.00145972  0.0303241  0.0386563                0.00145972\n",
-              "GBM_1_AutoML_2_20250409_32324                        0.0382086  0.0014599   0.0298492  0.0386063                0.0014599\n",
-              "DRF_1_AutoML_2_20250409_32324                        0.0383193  0.00146837  0.0302116  0.0387911                0.00146837\n",
-              "GLM_1_AutoML_2_20250409_32324                        0.0387665  0.00150284  0.0302822  0.0391593                0.00150284\n",
-              "GBM_grid_1_AutoML_2_20250409_32324_model_1           0.0392069  0.00153718  0.0305875  0.0396521                0.00153718\n",
-              "XGBoost_grid_1_AutoML_2_20250409_32324_model_1       0.0393049  0.00154487  0.029986   0.0396462                0.00154487\n",
-              "GBM_grid_1_AutoML_2_20250409_32324_model_2           0.0398222  0.00158581  0.031068   0.040277                 0.00158581\n",
-              "GBM_2_AutoML_2_20250409_32324                        0.0398482  0.00158788  0.0307006  0.0403194                0.00158788\n",
-              "GBM_4_AutoML_2_20250409_32324                        0.040654   0.00165275  0.0307089  0.0411373                0.00165275\n",
-              "XGBoost_grid_1_AutoML_2_20250409_32324_model_2       0.0407116  0.00165744  0.032371   0.0410824                0.00165744\n",
-              "DeepLearning_grid_3_AutoML_2_20250409_32324_model_1  0.0411275  0.00169147  0.0325407  0.0413718                0.00169147\n",
-              "DeepLearning_grid_2_AutoML_2_20250409_32324_model_1  0.0411748  0.00169536  0.0311485  0.0414546                0.00169536\n",
-              "DeepLearning_grid_1_AutoML_2_20250409_32324_model_1  0.0412378  0.00170055  0.0318134  0.0415096                0.00170055\n",
-              "GBM_3_AutoML_2_20250409_32324                        0.0412724  0.00170341  0.0312335  0.0417604                0.00170341\n",
-              "DeepLearning_1_AutoML_2_20250409_32324               0.0415441  0.00172591  0.0324291  0.0419248                0.00172591\n",
-              "XGBoost_3_AutoML_2_20250409_32324                    0.0430785  0.00185576  0.0333741  0.0432922                0.00185576\n",
-              "XGBoost_1_AutoML_2_20250409_32324                    0.0443096  0.00196334  0.0346859  0.0446622                0.00196334\n",
-              "XGBoost_2_AutoML_2_20250409_32324                    0.0473856  0.00224539  0.0382751  0.0476432                0.00224539\n",
-              "XGBoost_grid_1_AutoML_2_20250409_32324_model_3       0.0474093  0.00224764  0.0363529  0.0475378                0.00224764\n",
-              "[20 rows x 6 columns]\n"
+              "model_id                                                   auc    logloss     aucpr    mean_per_class_error      rmse       mse\n",
+              "----------------------------------------------------  --------  ---------  --------  ----------------------  --------  --------\n",
+              "XGBoost_2_AutoML_2_20250409_152135                    0.521682   0.868195  0.519441                0.490196  0.55289   0.305688\n",
+              "DRF_1_AutoML_2_20250409_152135                        0.516968   0.726751  0.518918                0.5       0.514539  0.26475\n",
+              "XGBoost_3_AutoML_2_20250409_152135                    0.502451   0.893016  0.498844                0.5       0.561485  0.315265\n",
+              "XGBoost_1_AutoML_2_20250409_152135                    0.494532   0.847524  0.54791                 0.5       0.555468  0.308545\n",
+              "XGBoost_grid_1_AutoML_2_20250409_152135_model_2       0.486991   0.811853  0.492215                0.470023  0.543988  0.295923\n",
+              "DeepLearning_grid_2_AutoML_2_20250409_152135_model_1  0.483974   1.3188    0.466643                0.5       0.603914  0.364712\n",
+              "GBM_1_AutoML_2_20250409_152135                        0.48322    0.729637  0.481765                0.5       0.51663   0.266906\n",
+              "GBM_5_AutoML_2_20250409_152135                        0.478695   0.782243  0.491722                0.5       0.535926  0.287216\n",
+              "GBM_grid_1_AutoML_2_20250409_152135_model_2           0.467383   0.746436  0.482668                0.490196  0.523668  0.274228\n",
+              "XGBoost_grid_1_AutoML_2_20250409_152135_model_3       0.465875   0.950725  0.467881                0.490196  0.575634  0.331354\n",
+              "XGBoost_grid_1_AutoML_2_20250409_152135_model_1       0.463989   0.917712  0.465734                0.5       0.567556  0.32212\n",
+              "GBM_4_AutoML_2_20250409_152135                        0.463612   0.799223  0.464516                0.460407  0.542634  0.294452\n",
+              "GBM_3_AutoML_2_20250409_152135                        0.462104   0.803066  0.462236                0.5       0.542984  0.294832\n",
+              "DeepLearning_1_AutoML_2_20250409_152135               0.460219   0.884179  0.475068                0.5       0.56606   0.320424\n",
+              "GBM_2_AutoML_2_20250409_152135                        0.453808   0.804537  0.490487                0.5       0.545743  0.297836\n",
+              "XRT_1_AutoML_2_20250409_152135                        0.445701   0.753462  0.497824                0.5       0.527128  0.277864\n",
+              "DeepLearning_grid_1_AutoML_2_20250409_152135_model_1  0.437217   1.6811    0.460449                0.5       0.647546  0.419316\n",
+              "GLM_1_AutoML_2_20250409_152135                        0.434201   0.697165  0.460213                0.5       0.501997  0.252001\n",
+              "GBM_grid_1_AutoML_2_20250409_152135_model_1           0.432692   0.837016  0.44726                 0.5       0.5559    0.309025\n",
+              "DeepLearning_grid_3_AutoML_2_20250409_152135_model_1  0.428544   0.946621  0.449341                0.5       0.57934   0.335635\n",
+              "[20 rows x 7 columns]\n"
             ],
             "text/html": [
               "<table class='dataframe'>\n",
               "<thead>\n",
-              "<tr><th>model_id                                           </th><th style=\"text-align: right;\">     rmse</th><th style=\"text-align: right;\">       mse</th><th style=\"text-align: right;\">      mae</th><th style=\"text-align: right;\">    rmsle</th><th style=\"text-align: right;\">  mean_residual_deviance</th></tr>\n",
+              "<tr><th>model_id                                            </th><th style=\"text-align: right;\">     auc</th><th style=\"text-align: right;\">  logloss</th><th style=\"text-align: right;\">   aucpr</th><th style=\"text-align: right;\">  mean_per_class_error</th><th style=\"text-align: right;\">    rmse</th><th style=\"text-align: right;\">     mse</th></tr>\n",
               "</thead>\n",
               "<tbody>\n",
-              "<tr><td>XRT_1_AutoML_2_20250409_32324                      </td><td style=\"text-align: right;\">0.0372215</td><td style=\"text-align: right;\">0.00138544</td><td style=\"text-align: right;\">0.0293038</td><td style=\"text-align: right;\">0.0376997</td><td style=\"text-align: right;\">              0.00138544</td></tr>\n",
-              "<tr><td>GBM_5_AutoML_2_20250409_32324                      </td><td style=\"text-align: right;\">0.0382062</td><td style=\"text-align: right;\">0.00145972</td><td style=\"text-align: right;\">0.0303241</td><td style=\"text-align: right;\">0.0386563</td><td style=\"text-align: right;\">              0.00145972</td></tr>\n",
-              "<tr><td>GBM_1_AutoML_2_20250409_32324                      </td><td style=\"text-align: right;\">0.0382086</td><td style=\"text-align: right;\">0.0014599 </td><td style=\"text-align: right;\">0.0298492</td><td style=\"text-align: right;\">0.0386063</td><td style=\"text-align: right;\">              0.0014599 </td></tr>\n",
-              "<tr><td>DRF_1_AutoML_2_20250409_32324                      </td><td style=\"text-align: right;\">0.0383193</td><td style=\"text-align: right;\">0.00146837</td><td style=\"text-align: right;\">0.0302116</td><td style=\"text-align: right;\">0.0387911</td><td style=\"text-align: right;\">              0.00146837</td></tr>\n",
-              "<tr><td>GLM_1_AutoML_2_20250409_32324                      </td><td style=\"text-align: right;\">0.0387665</td><td style=\"text-align: right;\">0.00150284</td><td style=\"text-align: right;\">0.0302822</td><td style=\"text-align: right;\">0.0391593</td><td style=\"text-align: right;\">              0.00150284</td></tr>\n",
-              "<tr><td>GBM_grid_1_AutoML_2_20250409_32324_model_1         </td><td style=\"text-align: right;\">0.0392069</td><td style=\"text-align: right;\">0.00153718</td><td style=\"text-align: right;\">0.0305875</td><td style=\"text-align: right;\">0.0396521</td><td style=\"text-align: right;\">              0.00153718</td></tr>\n",
-              "<tr><td>XGBoost_grid_1_AutoML_2_20250409_32324_model_1     </td><td style=\"text-align: right;\">0.0393049</td><td style=\"text-align: right;\">0.00154487</td><td style=\"text-align: right;\">0.029986 </td><td style=\"text-align: right;\">0.0396462</td><td style=\"text-align: right;\">              0.00154487</td></tr>\n",
-              "<tr><td>GBM_grid_1_AutoML_2_20250409_32324_model_2         </td><td style=\"text-align: right;\">0.0398222</td><td style=\"text-align: right;\">0.00158581</td><td style=\"text-align: right;\">0.031068 </td><td style=\"text-align: right;\">0.040277 </td><td style=\"text-align: right;\">              0.00158581</td></tr>\n",
-              "<tr><td>GBM_2_AutoML_2_20250409_32324                      </td><td style=\"text-align: right;\">0.0398482</td><td style=\"text-align: right;\">0.00158788</td><td style=\"text-align: right;\">0.0307006</td><td style=\"text-align: right;\">0.0403194</td><td style=\"text-align: right;\">              0.00158788</td></tr>\n",
-              "<tr><td>GBM_4_AutoML_2_20250409_32324                      </td><td style=\"text-align: right;\">0.040654 </td><td style=\"text-align: right;\">0.00165275</td><td style=\"text-align: right;\">0.0307089</td><td style=\"text-align: right;\">0.0411373</td><td style=\"text-align: right;\">              0.00165275</td></tr>\n",
-              "<tr><td>XGBoost_grid_1_AutoML_2_20250409_32324_model_2     </td><td style=\"text-align: right;\">0.0407116</td><td style=\"text-align: right;\">0.00165744</td><td style=\"text-align: right;\">0.032371 </td><td style=\"text-align: right;\">0.0410824</td><td style=\"text-align: right;\">              0.00165744</td></tr>\n",
-              "<tr><td>DeepLearning_grid_3_AutoML_2_20250409_32324_model_1</td><td style=\"text-align: right;\">0.0411275</td><td style=\"text-align: right;\">0.00169147</td><td style=\"text-align: right;\">0.0325407</td><td style=\"text-align: right;\">0.0413718</td><td style=\"text-align: right;\">              0.00169147</td></tr>\n",
-              "<tr><td>DeepLearning_grid_2_AutoML_2_20250409_32324_model_1</td><td style=\"text-align: right;\">0.0411748</td><td style=\"text-align: right;\">0.00169536</td><td style=\"text-align: right;\">0.0311485</td><td style=\"text-align: right;\">0.0414546</td><td style=\"text-align: right;\">              0.00169536</td></tr>\n",
-              "<tr><td>DeepLearning_grid_1_AutoML_2_20250409_32324_model_1</td><td style=\"text-align: right;\">0.0412378</td><td style=\"text-align: right;\">0.00170055</td><td style=\"text-align: right;\">0.0318134</td><td style=\"text-align: right;\">0.0415096</td><td style=\"text-align: right;\">              0.00170055</td></tr>\n",
-              "<tr><td>GBM_3_AutoML_2_20250409_32324                      </td><td style=\"text-align: right;\">0.0412724</td><td style=\"text-align: right;\">0.00170341</td><td style=\"text-align: right;\">0.0312335</td><td style=\"text-align: right;\">0.0417604</td><td style=\"text-align: right;\">              0.00170341</td></tr>\n",
-              "<tr><td>DeepLearning_1_AutoML_2_20250409_32324             </td><td style=\"text-align: right;\">0.0415441</td><td style=\"text-align: right;\">0.00172591</td><td style=\"text-align: right;\">0.0324291</td><td style=\"text-align: right;\">0.0419248</td><td style=\"text-align: right;\">              0.00172591</td></tr>\n",
-              "<tr><td>XGBoost_3_AutoML_2_20250409_32324                  </td><td style=\"text-align: right;\">0.0430785</td><td style=\"text-align: right;\">0.00185576</td><td style=\"text-align: right;\">0.0333741</td><td style=\"text-align: right;\">0.0432922</td><td style=\"text-align: right;\">              0.00185576</td></tr>\n",
-              "<tr><td>XGBoost_1_AutoML_2_20250409_32324                  </td><td style=\"text-align: right;\">0.0443096</td><td style=\"text-align: right;\">0.00196334</td><td style=\"text-align: right;\">0.0346859</td><td style=\"text-align: right;\">0.0446622</td><td style=\"text-align: right;\">              0.00196334</td></tr>\n",
-              "<tr><td>XGBoost_2_AutoML_2_20250409_32324                  </td><td style=\"text-align: right;\">0.0473856</td><td style=\"text-align: right;\">0.00224539</td><td style=\"text-align: right;\">0.0382751</td><td style=\"text-align: right;\">0.0476432</td><td style=\"text-align: right;\">              0.00224539</td></tr>\n",
-              "<tr><td>XGBoost_grid_1_AutoML_2_20250409_32324_model_3     </td><td style=\"text-align: right;\">0.0474093</td><td style=\"text-align: right;\">0.00224764</td><td style=\"text-align: right;\">0.0363529</td><td style=\"text-align: right;\">0.0475378</td><td style=\"text-align: right;\">              0.00224764</td></tr>\n",
+              "<tr><td>XGBoost_2_AutoML_2_20250409_152135                  </td><td style=\"text-align: right;\">0.521682</td><td style=\"text-align: right;\"> 0.868195</td><td style=\"text-align: right;\">0.519441</td><td style=\"text-align: right;\">              0.490196</td><td style=\"text-align: right;\">0.55289 </td><td style=\"text-align: right;\">0.305688</td></tr>\n",
+              "<tr><td>DRF_1_AutoML_2_20250409_152135                      </td><td style=\"text-align: right;\">0.516968</td><td style=\"text-align: right;\"> 0.726751</td><td style=\"text-align: right;\">0.518918</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.514539</td><td style=\"text-align: right;\">0.26475 </td></tr>\n",
+              "<tr><td>XGBoost_3_AutoML_2_20250409_152135                  </td><td style=\"text-align: right;\">0.502451</td><td style=\"text-align: right;\"> 0.893016</td><td style=\"text-align: right;\">0.498844</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.561485</td><td style=\"text-align: right;\">0.315265</td></tr>\n",
+              "<tr><td>XGBoost_1_AutoML_2_20250409_152135                  </td><td style=\"text-align: right;\">0.494532</td><td style=\"text-align: right;\"> 0.847524</td><td style=\"text-align: right;\">0.54791 </td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.555468</td><td style=\"text-align: right;\">0.308545</td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_2_20250409_152135_model_2     </td><td style=\"text-align: right;\">0.486991</td><td style=\"text-align: right;\"> 0.811853</td><td style=\"text-align: right;\">0.492215</td><td style=\"text-align: right;\">              0.470023</td><td style=\"text-align: right;\">0.543988</td><td style=\"text-align: right;\">0.295923</td></tr>\n",
+              "<tr><td>DeepLearning_grid_2_AutoML_2_20250409_152135_model_1</td><td style=\"text-align: right;\">0.483974</td><td style=\"text-align: right;\"> 1.3188  </td><td style=\"text-align: right;\">0.466643</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.603914</td><td style=\"text-align: right;\">0.364712</td></tr>\n",
+              "<tr><td>GBM_1_AutoML_2_20250409_152135                      </td><td style=\"text-align: right;\">0.48322 </td><td style=\"text-align: right;\"> 0.729637</td><td style=\"text-align: right;\">0.481765</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.51663 </td><td style=\"text-align: right;\">0.266906</td></tr>\n",
+              "<tr><td>GBM_5_AutoML_2_20250409_152135                      </td><td style=\"text-align: right;\">0.478695</td><td style=\"text-align: right;\"> 0.782243</td><td style=\"text-align: right;\">0.491722</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.535926</td><td style=\"text-align: right;\">0.287216</td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_2_20250409_152135_model_2         </td><td style=\"text-align: right;\">0.467383</td><td style=\"text-align: right;\"> 0.746436</td><td style=\"text-align: right;\">0.482668</td><td style=\"text-align: right;\">              0.490196</td><td style=\"text-align: right;\">0.523668</td><td style=\"text-align: right;\">0.274228</td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_2_20250409_152135_model_3     </td><td style=\"text-align: right;\">0.465875</td><td style=\"text-align: right;\"> 0.950725</td><td style=\"text-align: right;\">0.467881</td><td style=\"text-align: right;\">              0.490196</td><td style=\"text-align: right;\">0.575634</td><td style=\"text-align: right;\">0.331354</td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_2_20250409_152135_model_1     </td><td style=\"text-align: right;\">0.463989</td><td style=\"text-align: right;\"> 0.917712</td><td style=\"text-align: right;\">0.465734</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.567556</td><td style=\"text-align: right;\">0.32212 </td></tr>\n",
+              "<tr><td>GBM_4_AutoML_2_20250409_152135                      </td><td style=\"text-align: right;\">0.463612</td><td style=\"text-align: right;\"> 0.799223</td><td style=\"text-align: right;\">0.464516</td><td style=\"text-align: right;\">              0.460407</td><td style=\"text-align: right;\">0.542634</td><td style=\"text-align: right;\">0.294452</td></tr>\n",
+              "<tr><td>GBM_3_AutoML_2_20250409_152135                      </td><td style=\"text-align: right;\">0.462104</td><td style=\"text-align: right;\"> 0.803066</td><td style=\"text-align: right;\">0.462236</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.542984</td><td style=\"text-align: right;\">0.294832</td></tr>\n",
+              "<tr><td>DeepLearning_1_AutoML_2_20250409_152135             </td><td style=\"text-align: right;\">0.460219</td><td style=\"text-align: right;\"> 0.884179</td><td style=\"text-align: right;\">0.475068</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.56606 </td><td style=\"text-align: right;\">0.320424</td></tr>\n",
+              "<tr><td>GBM_2_AutoML_2_20250409_152135                      </td><td style=\"text-align: right;\">0.453808</td><td style=\"text-align: right;\"> 0.804537</td><td style=\"text-align: right;\">0.490487</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.545743</td><td style=\"text-align: right;\">0.297836</td></tr>\n",
+              "<tr><td>XRT_1_AutoML_2_20250409_152135                      </td><td style=\"text-align: right;\">0.445701</td><td style=\"text-align: right;\"> 0.753462</td><td style=\"text-align: right;\">0.497824</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.527128</td><td style=\"text-align: right;\">0.277864</td></tr>\n",
+              "<tr><td>DeepLearning_grid_1_AutoML_2_20250409_152135_model_1</td><td style=\"text-align: right;\">0.437217</td><td style=\"text-align: right;\"> 1.6811  </td><td style=\"text-align: right;\">0.460449</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.647546</td><td style=\"text-align: right;\">0.419316</td></tr>\n",
+              "<tr><td>GLM_1_AutoML_2_20250409_152135                      </td><td style=\"text-align: right;\">0.434201</td><td style=\"text-align: right;\"> 0.697165</td><td style=\"text-align: right;\">0.460213</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.501997</td><td style=\"text-align: right;\">0.252001</td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_2_20250409_152135_model_1         </td><td style=\"text-align: right;\">0.432692</td><td style=\"text-align: right;\"> 0.837016</td><td style=\"text-align: right;\">0.44726 </td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.5559  </td><td style=\"text-align: right;\">0.309025</td></tr>\n",
+              "<tr><td>DeepLearning_grid_3_AutoML_2_20250409_152135_model_1</td><td style=\"text-align: right;\">0.428544</td><td style=\"text-align: right;\"> 0.946621</td><td style=\"text-align: right;\">0.449341</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.57934 </td><td style=\"text-align: right;\">0.335635</td></tr>\n",
               "</tbody>\n",
-              "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[20 rows x 6 columns]</pre>"
+              "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[20 rows x 7 columns]</pre>"
             ]
           },
           "metadata": {},
-          "execution_count": 28
+          "execution_count": 21
         }
       ]
     },
@@ -1717,50 +1816,50 @@
           "height": 409
         },
         "id": "157N21YhkzBr",
-        "outputId": "e7aea365-6d30-41b7-b232-00a82332c810"
+        "outputId": "a2bbeb88-9abe-4337-c78a-9335acd55cf1"
       },
-      "execution_count": 29,
+      "execution_count": 22,
       "outputs": [
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "model_id                                             rmse         mse        mae      rmsle    mean_residual_deviance    training_time_ms    predict_time_per_row_ms  algo\n",
-              "----------------------------------------------  ---------  ----------  ---------  ---------  ------------------------  ------------------  -------------------------  -------\n",
-              "XRT_1_AutoML_2_20250409_32324                   0.0372215  0.00138544  0.0293038  0.0376997                0.00138544                6145                   0.102317  DRF\n",
-              "GBM_5_AutoML_2_20250409_32324                   0.0382062  0.00145972  0.0303241  0.0386563                0.00145972                 908                   0.076164  GBM\n",
-              "GBM_1_AutoML_2_20250409_32324                   0.0382086  0.0014599   0.0298492  0.0386063                0.0014599                  796                   0.038502  GBM\n",
-              "DRF_1_AutoML_2_20250409_32324                   0.0383193  0.00146837  0.0302116  0.0387911                0.00146837                3021                   0.047204  DRF\n",
-              "GLM_1_AutoML_2_20250409_32324                   0.0387665  0.00150284  0.0302822  0.0391593                0.00150284                 297                   0.076823  GLM\n",
-              "GBM_grid_1_AutoML_2_20250409_32324_model_1      0.0392069  0.00153718  0.0305875  0.0396521                0.00153718                 602                   0.056674  GBM\n",
-              "XGBoost_grid_1_AutoML_2_20250409_32324_model_1  0.0393049  0.00154487  0.029986   0.0396462                0.00154487                1142                   0.045873  XGBoost\n",
-              "GBM_grid_1_AutoML_2_20250409_32324_model_2      0.0398222  0.00158581  0.031068   0.040277                 0.00158581                 553                   0.041423  GBM\n",
-              "GBM_2_AutoML_2_20250409_32324                   0.0398482  0.00158788  0.0307006  0.0403194                0.00158788                 857                   0.039507  GBM\n",
-              "GBM_4_AutoML_2_20250409_32324                   0.040654   0.00165275  0.0307089  0.0411373                0.00165275                 714                   0.030504  GBM\n",
-              "[20 rows x 9 columns]\n"
+              "model_id                                                   auc    logloss     aucpr    mean_per_class_error      rmse       mse    training_time_ms    predict_time_per_row_ms  algo\n",
+              "----------------------------------------------------  --------  ---------  --------  ----------------------  --------  --------  ------------------  -------------------------  ------------\n",
+              "XGBoost_2_AutoML_2_20250409_152135                    0.521682   0.868195  0.519441                0.490196  0.55289   0.305688                2467                   0.275334  XGBoost\n",
+              "DRF_1_AutoML_2_20250409_152135                        0.516968   0.726751  0.518918                0.5       0.514539  0.26475                 3142                   0.166365  DRF\n",
+              "XGBoost_3_AutoML_2_20250409_152135                    0.502451   0.893016  0.498844                0.5       0.561485  0.315265                3038                   0.202713  XGBoost\n",
+              "XGBoost_1_AutoML_2_20250409_152135                    0.494532   0.847524  0.54791                 0.5       0.555468  0.308545                2641                   0.203214  XGBoost\n",
+              "XGBoost_grid_1_AutoML_2_20250409_152135_model_2       0.486991   0.811853  0.492215                0.470023  0.543988  0.295923                6373                   0.162094  XGBoost\n",
+              "DeepLearning_grid_2_AutoML_2_20250409_152135_model_1  0.483974   1.3188    0.466643                0.5       0.603914  0.364712               33017                   0.198407  DeepLearning\n",
+              "GBM_1_AutoML_2_20250409_152135                        0.48322    0.729637  0.481765                0.5       0.51663   0.266906                3467                   0.126336  GBM\n",
+              "GBM_5_AutoML_2_20250409_152135                        0.478695   0.782243  0.491722                0.5       0.535926  0.287216                1649                   0.113597  GBM\n",
+              "GBM_grid_1_AutoML_2_20250409_152135_model_2           0.467383   0.746436  0.482668                0.490196  0.523668  0.274228                1230                   0.10611   GBM\n",
+              "XGBoost_grid_1_AutoML_2_20250409_152135_model_3       0.465875   0.950725  0.467881                0.490196  0.575634  0.331354                2592                   0.142199  XGBoost\n",
+              "[20 rows x 10 columns]\n"
             ],
             "text/html": [
               "<table class='dataframe'>\n",
               "<thead>\n",
-              "<tr><th>model_id                                      </th><th style=\"text-align: right;\">     rmse</th><th style=\"text-align: right;\">       mse</th><th style=\"text-align: right;\">      mae</th><th style=\"text-align: right;\">    rmsle</th><th style=\"text-align: right;\">  mean_residual_deviance</th><th style=\"text-align: right;\">  training_time_ms</th><th style=\"text-align: right;\">  predict_time_per_row_ms</th><th>algo   </th></tr>\n",
+              "<tr><th>model_id                                            </th><th style=\"text-align: right;\">     auc</th><th style=\"text-align: right;\">  logloss</th><th style=\"text-align: right;\">   aucpr</th><th style=\"text-align: right;\">  mean_per_class_error</th><th style=\"text-align: right;\">    rmse</th><th style=\"text-align: right;\">     mse</th><th style=\"text-align: right;\">  training_time_ms</th><th style=\"text-align: right;\">  predict_time_per_row_ms</th><th>algo        </th></tr>\n",
               "</thead>\n",
               "<tbody>\n",
-              "<tr><td>XRT_1_AutoML_2_20250409_32324                 </td><td style=\"text-align: right;\">0.0372215</td><td style=\"text-align: right;\">0.00138544</td><td style=\"text-align: right;\">0.0293038</td><td style=\"text-align: right;\">0.0376997</td><td style=\"text-align: right;\">              0.00138544</td><td style=\"text-align: right;\">              6145</td><td style=\"text-align: right;\">                 0.102317</td><td>DRF    </td></tr>\n",
-              "<tr><td>GBM_5_AutoML_2_20250409_32324                 </td><td style=\"text-align: right;\">0.0382062</td><td style=\"text-align: right;\">0.00145972</td><td style=\"text-align: right;\">0.0303241</td><td style=\"text-align: right;\">0.0386563</td><td style=\"text-align: right;\">              0.00145972</td><td style=\"text-align: right;\">               908</td><td style=\"text-align: right;\">                 0.076164</td><td>GBM    </td></tr>\n",
-              "<tr><td>GBM_1_AutoML_2_20250409_32324                 </td><td style=\"text-align: right;\">0.0382086</td><td style=\"text-align: right;\">0.0014599 </td><td style=\"text-align: right;\">0.0298492</td><td style=\"text-align: right;\">0.0386063</td><td style=\"text-align: right;\">              0.0014599 </td><td style=\"text-align: right;\">               796</td><td style=\"text-align: right;\">                 0.038502</td><td>GBM    </td></tr>\n",
-              "<tr><td>DRF_1_AutoML_2_20250409_32324                 </td><td style=\"text-align: right;\">0.0383193</td><td style=\"text-align: right;\">0.00146837</td><td style=\"text-align: right;\">0.0302116</td><td style=\"text-align: right;\">0.0387911</td><td style=\"text-align: right;\">              0.00146837</td><td style=\"text-align: right;\">              3021</td><td style=\"text-align: right;\">                 0.047204</td><td>DRF    </td></tr>\n",
-              "<tr><td>GLM_1_AutoML_2_20250409_32324                 </td><td style=\"text-align: right;\">0.0387665</td><td style=\"text-align: right;\">0.00150284</td><td style=\"text-align: right;\">0.0302822</td><td style=\"text-align: right;\">0.0391593</td><td style=\"text-align: right;\">              0.00150284</td><td style=\"text-align: right;\">               297</td><td style=\"text-align: right;\">                 0.076823</td><td>GLM    </td></tr>\n",
-              "<tr><td>GBM_grid_1_AutoML_2_20250409_32324_model_1    </td><td style=\"text-align: right;\">0.0392069</td><td style=\"text-align: right;\">0.00153718</td><td style=\"text-align: right;\">0.0305875</td><td style=\"text-align: right;\">0.0396521</td><td style=\"text-align: right;\">              0.00153718</td><td style=\"text-align: right;\">               602</td><td style=\"text-align: right;\">                 0.056674</td><td>GBM    </td></tr>\n",
-              "<tr><td>XGBoost_grid_1_AutoML_2_20250409_32324_model_1</td><td style=\"text-align: right;\">0.0393049</td><td style=\"text-align: right;\">0.00154487</td><td style=\"text-align: right;\">0.029986 </td><td style=\"text-align: right;\">0.0396462</td><td style=\"text-align: right;\">              0.00154487</td><td style=\"text-align: right;\">              1142</td><td style=\"text-align: right;\">                 0.045873</td><td>XGBoost</td></tr>\n",
-              "<tr><td>GBM_grid_1_AutoML_2_20250409_32324_model_2    </td><td style=\"text-align: right;\">0.0398222</td><td style=\"text-align: right;\">0.00158581</td><td style=\"text-align: right;\">0.031068 </td><td style=\"text-align: right;\">0.040277 </td><td style=\"text-align: right;\">              0.00158581</td><td style=\"text-align: right;\">               553</td><td style=\"text-align: right;\">                 0.041423</td><td>GBM    </td></tr>\n",
-              "<tr><td>GBM_2_AutoML_2_20250409_32324                 </td><td style=\"text-align: right;\">0.0398482</td><td style=\"text-align: right;\">0.00158788</td><td style=\"text-align: right;\">0.0307006</td><td style=\"text-align: right;\">0.0403194</td><td style=\"text-align: right;\">              0.00158788</td><td style=\"text-align: right;\">               857</td><td style=\"text-align: right;\">                 0.039507</td><td>GBM    </td></tr>\n",
-              "<tr><td>GBM_4_AutoML_2_20250409_32324                 </td><td style=\"text-align: right;\">0.040654 </td><td style=\"text-align: right;\">0.00165275</td><td style=\"text-align: right;\">0.0307089</td><td style=\"text-align: right;\">0.0411373</td><td style=\"text-align: right;\">              0.00165275</td><td style=\"text-align: right;\">               714</td><td style=\"text-align: right;\">                 0.030504</td><td>GBM    </td></tr>\n",
+              "<tr><td>XGBoost_2_AutoML_2_20250409_152135                  </td><td style=\"text-align: right;\">0.521682</td><td style=\"text-align: right;\"> 0.868195</td><td style=\"text-align: right;\">0.519441</td><td style=\"text-align: right;\">              0.490196</td><td style=\"text-align: right;\">0.55289 </td><td style=\"text-align: right;\">0.305688</td><td style=\"text-align: right;\">              2467</td><td style=\"text-align: right;\">                 0.275334</td><td>XGBoost     </td></tr>\n",
+              "<tr><td>DRF_1_AutoML_2_20250409_152135                      </td><td style=\"text-align: right;\">0.516968</td><td style=\"text-align: right;\"> 0.726751</td><td style=\"text-align: right;\">0.518918</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.514539</td><td style=\"text-align: right;\">0.26475 </td><td style=\"text-align: right;\">              3142</td><td style=\"text-align: right;\">                 0.166365</td><td>DRF         </td></tr>\n",
+              "<tr><td>XGBoost_3_AutoML_2_20250409_152135                  </td><td style=\"text-align: right;\">0.502451</td><td style=\"text-align: right;\"> 0.893016</td><td style=\"text-align: right;\">0.498844</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.561485</td><td style=\"text-align: right;\">0.315265</td><td style=\"text-align: right;\">              3038</td><td style=\"text-align: right;\">                 0.202713</td><td>XGBoost     </td></tr>\n",
+              "<tr><td>XGBoost_1_AutoML_2_20250409_152135                  </td><td style=\"text-align: right;\">0.494532</td><td style=\"text-align: right;\"> 0.847524</td><td style=\"text-align: right;\">0.54791 </td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.555468</td><td style=\"text-align: right;\">0.308545</td><td style=\"text-align: right;\">              2641</td><td style=\"text-align: right;\">                 0.203214</td><td>XGBoost     </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_2_20250409_152135_model_2     </td><td style=\"text-align: right;\">0.486991</td><td style=\"text-align: right;\"> 0.811853</td><td style=\"text-align: right;\">0.492215</td><td style=\"text-align: right;\">              0.470023</td><td style=\"text-align: right;\">0.543988</td><td style=\"text-align: right;\">0.295923</td><td style=\"text-align: right;\">              6373</td><td style=\"text-align: right;\">                 0.162094</td><td>XGBoost     </td></tr>\n",
+              "<tr><td>DeepLearning_grid_2_AutoML_2_20250409_152135_model_1</td><td style=\"text-align: right;\">0.483974</td><td style=\"text-align: right;\"> 1.3188  </td><td style=\"text-align: right;\">0.466643</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.603914</td><td style=\"text-align: right;\">0.364712</td><td style=\"text-align: right;\">             33017</td><td style=\"text-align: right;\">                 0.198407</td><td>DeepLearning</td></tr>\n",
+              "<tr><td>GBM_1_AutoML_2_20250409_152135                      </td><td style=\"text-align: right;\">0.48322 </td><td style=\"text-align: right;\"> 0.729637</td><td style=\"text-align: right;\">0.481765</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.51663 </td><td style=\"text-align: right;\">0.266906</td><td style=\"text-align: right;\">              3467</td><td style=\"text-align: right;\">                 0.126336</td><td>GBM         </td></tr>\n",
+              "<tr><td>GBM_5_AutoML_2_20250409_152135                      </td><td style=\"text-align: right;\">0.478695</td><td style=\"text-align: right;\"> 0.782243</td><td style=\"text-align: right;\">0.491722</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.535926</td><td style=\"text-align: right;\">0.287216</td><td style=\"text-align: right;\">              1649</td><td style=\"text-align: right;\">                 0.113597</td><td>GBM         </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_2_20250409_152135_model_2         </td><td style=\"text-align: right;\">0.467383</td><td style=\"text-align: right;\"> 0.746436</td><td style=\"text-align: right;\">0.482668</td><td style=\"text-align: right;\">              0.490196</td><td style=\"text-align: right;\">0.523668</td><td style=\"text-align: right;\">0.274228</td><td style=\"text-align: right;\">              1230</td><td style=\"text-align: right;\">                 0.10611 </td><td>GBM         </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_2_20250409_152135_model_3     </td><td style=\"text-align: right;\">0.465875</td><td style=\"text-align: right;\"> 0.950725</td><td style=\"text-align: right;\">0.467881</td><td style=\"text-align: right;\">              0.490196</td><td style=\"text-align: right;\">0.575634</td><td style=\"text-align: right;\">0.331354</td><td style=\"text-align: right;\">              2592</td><td style=\"text-align: right;\">                 0.142199</td><td>XGBoost     </td></tr>\n",
               "</tbody>\n",
-              "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[20 rows x 9 columns]</pre>"
+              "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[20 rows x 10 columns]</pre>"
             ]
           },
           "metadata": {},
-          "execution_count": 29
+          "execution_count": 22
         }
       ]
     },
@@ -1776,9 +1875,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "6evHF-tokzjX",
-        "outputId": "373036c0-f3d3-45b1-afd4-d3dc39b627e4"
+        "outputId": "fad90f1d-3e6a-45e6-ea1b-7739e2157d2a"
       },
-      "execution_count": 30,
+      "execution_count": 23,
       "outputs": [
         {
           "output_type": "stream",
@@ -1786,72 +1885,171 @@
           "text": [
             "Model Details\n",
             "=============\n",
-            "H2ORandomForestEstimator : Distributed Random Forest\n",
-            "Model Key: XRT_1_AutoML_2_20250409_32324\n",
+            "H2OXGBoostEstimator : XGBoost\n",
+            "Model Key: XGBoost_2_AutoML_2_20250409_152135\n",
             "\n",
             "\n",
             "Model Summary: \n",
-            "    number_of_trees    number_of_internal_trees    model_size_in_bytes    min_depth    max_depth    mean_depth    min_leaves    max_leaves    mean_leaves\n",
-            "--  -----------------  --------------------------  ---------------------  -----------  -----------  ------------  ------------  ------------  -------------\n",
-            "    45                 45                          224181                 20           20           20            121           521           384.333\n",
+            "    number_of_trees\n",
+            "--  -----------------\n",
+            "    30\n",
             "\n",
-            "ModelMetricsRegression: drf\n",
+            "ModelMetricsBinomial: xgboost\n",
             "** Reported on train data. **\n",
             "\n",
-            "MSE: 0.0014280151758824398\n",
-            "RMSE: 0.03778908805306685\n",
-            "MAE: 0.02824063408464411\n",
-            "RMSLE: 0.037517547053815714\n",
-            "Mean Residual Deviance: 0.0014280151758824398\n",
+            "MSE: 0.04856159627892655\n",
+            "RMSE: 0.22036695822860228\n",
+            "LogLoss: 0.21811686278860418\n",
+            "Mean Per-Class Error: 0.0168651023003121\n",
+            "AUC: 0.9983354525488383\n",
+            "AUCPR: 0.9983430345133928\n",
+            "Gini: 0.9966709050976765\n",
+            "\n",
+            "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.4844781955083211\n",
+            "       0    1    Error    Rate\n",
+            "-----  ---  ---  -------  ------------\n",
+            "0      402  8    0.0195   (8.0/410.0)\n",
+            "1      6    416  0.0142   (6.0/422.0)\n",
+            "Total  408  424  0.0168   (14.0/832.0)\n",
+            "\n",
+            "Maximum Metrics: Maximum metrics at their respective thresholds\n",
+            "metric                       threshold    value     idx\n",
+            "---------------------------  -----------  --------  -----\n",
+            "max f1                       0.484478     0.983452  201\n",
+            "max f2                       0.445234     0.989657  212\n",
+            "max f0point5                 0.551076     0.986395  192\n",
+            "max accuracy                 0.484478     0.983173  201\n",
+            "max precision                0.982302     1         0\n",
+            "max recall                   0.336748     1         234\n",
+            "max specificity              0.982302     1         0\n",
+            "max absolute_mcc             0.484478     0.966348  201\n",
+            "max min_per_class_accuracy   0.488712     0.981043  200\n",
+            "max mean_per_class_accuracy  0.484478     0.983135  201\n",
+            "max tns                      0.982302     410       0\n",
+            "max fns                      0.982302     421       0\n",
+            "max fps                      0.00968025   410       399\n",
+            "max tps                      0.336748     422       234\n",
+            "max tnr                      0.982302     1         0\n",
+            "max fnr                      0.982302     0.99763   0\n",
+            "max fpr                      0.00968025   1         399\n",
+            "max tpr                      0.336748     1         234\n",
             "\n",
-            "ModelMetricsRegression: drf\n",
+            "Gains/Lift Table: Avg response rate: 50.72 %, avg score: 51.05 %\n",
+            "group    cumulative_data_fraction    lower_threshold    lift      cumulative_lift    response_rate    score      cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain      cumulative_gain    kolmogorov_smirnov\n",
+            "-------  --------------------------  -----------------  --------  -----------------  ---------------  ---------  --------------------------  ------------------  --------------  -------------------------  --------  -----------------  --------------------\n",
+            "1        0.0108173                   0.972137           1.97156   1.97156            1                0.976887   1                           0.976887            0.021327        0.021327                   97.1564   97.1564            0.021327\n",
+            "2        0.0204327                   0.963878           1.97156   1.97156            1                0.968609   1                           0.972991            0.0189573       0.0402844                  97.1564   97.1564            0.0402844\n",
+            "3        0.0300481                   0.958543           1.97156   1.97156            1                0.961398   1                           0.969281            0.0189573       0.0592417                  97.1564   97.1564            0.0592417\n",
+            "4        0.0408654                   0.951325           1.97156   1.97156            1                0.954896   1                           0.965473            0.021327        0.0805687                  97.1564   97.1564            0.0805687\n",
+            "5        0.0504808                   0.945304           1.97156   1.97156            1                0.949574   1                           0.962445            0.0189573       0.0995261                  97.1564   97.1564            0.0995261\n",
+            "6        0.100962                    0.919195           1.97156   1.97156            1                0.932157   1                           0.947301            0.0995261       0.199052                   97.1564   97.1564            0.199052\n",
+            "7        0.15024                     0.900065           1.97156   1.97156            1                0.909711   1                           0.934972            0.0971564       0.296209                   97.1564   97.1564            0.296209\n",
+            "8        0.200721                    0.876327           1.97156   1.97156            1                0.889046   1                           0.923421            0.0995261       0.395735                   97.1564   97.1564            0.395735\n",
+            "9        0.300481                    0.820207           1.97156   1.97156            1                0.850731   1                           0.899288            0.196682        0.592417                   97.1564   97.1564            0.592417\n",
+            "10       0.40024                     0.745953           1.97156   1.97156            1                0.782398   1                           0.870153            0.196682        0.7891                     97.1564   97.1564            0.7891\n",
+            "11       0.501202                    0.510782           1.83074   1.9432             0.928571         0.652121   0.985612                    0.826233            0.184834        0.973934                   83.0738   94.3196            0.9593\n",
+            "12       0.59976                     0.284746           0.264478  1.66733            0.134146         0.377337   0.845691                    0.752467            0.0260664       1                          -73.5522  66.7335            0.812195\n",
+            "13       0.699519                    0.202861           0         1.42955            0                0.24154    0.725086                    0.679603            0               1                          -100      42.9553            0.609756\n",
+            "14       0.799279                    0.138646           0         1.25113            0                0.170537   0.634586                    0.616065            0               1                          -100      25.1128            0.407317\n",
+            "15       0.899038                    0.0952722          0         1.1123             0                0.117953   0.564171                    0.560793            0               1                          -100      11.2299            0.204878\n",
+            "16       1                           0.00968025         0         1                  0                0.0629315  0.507212                    0.510528            0               1                          -100      0                  0\n",
+            "\n",
+            "ModelMetricsBinomial: xgboost\n",
             "** Reported on validation data. **\n",
             "\n",
-            "MSE: 0.0013854433734110827\n",
-            "RMSE: 0.037221544479119656\n",
-            "MAE: 0.02930384278875654\n",
-            "RMSLE: 0.037699716489869806\n",
-            "Mean Residual Deviance: 0.0013854433734110827\n",
+            "MSE: 0.30568781210268564\n",
+            "RMSE: 0.5528904159982208\n",
+            "LogLoss: 0.8681948644613136\n",
+            "Mean Per-Class Error: 0.49019607843137253\n",
+            "AUC: 0.5216817496229261\n",
+            "AUCPR: 0.5194412079480231\n",
+            "Gini: 0.043363499245852255\n",
+            "\n",
+            "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.06307325512170792\n",
+            "       0    1    Error    Rate\n",
+            "-----  ---  ---  -------  ------------\n",
+            "0      1    50   0.9804   (50.0/51.0)\n",
+            "1      0    52   0        (0.0/52.0)\n",
+            "Total  1    102  0.4854   (50.0/103.0)\n",
+            "\n",
+            "Maximum Metrics: Maximum metrics at their respective thresholds\n",
+            "metric                       threshold    value     idx\n",
+            "---------------------------  -----------  --------  -----\n",
+            "max f1                       0.0630733    0.675325  100\n",
+            "max f2                       0.0630733    0.83871   100\n",
+            "max f0point5                 0.58902      0.57377   46\n",
+            "max accuracy                 0.655599     0.572816  38\n",
+            "max precision                0.918544     0.666667  7\n",
+            "max recall                   0.0630733    1         100\n",
+            "max specificity              0.960617     0.980392  0\n",
+            "max absolute_mcc             0.655599     0.151635  38\n",
+            "max min_per_class_accuracy   0.572145     0.54902   50\n",
+            "max mean_per_class_accuracy  0.655599     0.573906  38\n",
+            "max tns                      0.960617     50        0\n",
+            "max fns                      0.960617     52        0\n",
+            "max fps                      0.0345234    51        101\n",
+            "max tps                      0.0630733    52        100\n",
+            "max tnr                      0.960617     0.980392  0\n",
+            "max fnr                      0.960617     1         0\n",
+            "max fpr                      0.0345234    1         101\n",
+            "max tpr                      0.0630733    1         100\n",
+            "\n",
+            "Gains/Lift Table: Avg response rate: 50.49 %, avg score: 54.79 %\n",
+            "group    cumulative_data_fraction    lower_threshold    lift      cumulative_lift    response_rate    score     cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain       cumulative_gain    kolmogorov_smirnov\n",
+            "-------  --------------------------  -----------------  --------  -----------------  ---------------  --------  --------------------------  ------------------  --------------  -------------------------  ---------  -----------------  --------------------\n",
+            "1        0.0194175                   0.953524           0         0                  0                0.957244  0                           0.957244            0               0                          -100       -100               -0.0392157\n",
+            "2        0.0291262                   0.93633            1.98077   0.660256           1                0.936525  0.333333                    0.950338            0.0192308       0.0192308                  98.0769    -33.9744           -0.0199849\n",
+            "3        0.0485437                   0.931649           0.990385  0.792308           0.5              0.931649  0.4                         0.942862            0.0192308       0.0384615                  -0.961538  -20.7692           -0.020362\n",
+            "4        0.0485437                   0.9311             0         0.792308           0                0         0.4                         0.942862            0               0.0384615                  -100       -20.7692           -0.020362\n",
+            "5        0.0582524                   0.924741           1.98077   0.990385           1                0.924782  0.5                         0.939849            0.0192308       0.0576923                  98.0769    -0.961538          -0.00113122\n",
+            "6        0.106796                    0.848649           1.18846   1.08042            0.6              0.89536   0.545455                    0.919626            0.0576923       0.115385                   18.8462    8.04196            0.0173454\n",
+            "7        0.15534                     0.816536           0.792308  0.990385           0.4              0.832529  0.5                         0.892409            0.0384615       0.153846                   -20.7692   -0.961538          -0.00301659\n",
+            "8        0.203883                    0.801701           1.18846   1.03755            0.6              0.808636  0.52381                     0.872463            0.0576923       0.211538                   18.8462    3.75458            0.01546\n",
+            "9        0.300971                    0.720564           0.990385  1.02233            0.5              0.777296  0.516129                    0.841764            0.0961538       0.307692                   -0.961538  2.23325            0.0135747\n",
+            "10       0.398058                    0.641569           1.58462   1.15947            0.8              0.679651  0.585366                    0.802224            0.153846        0.461538                   58.4615    15.9475            0.128205\n",
+            "11       0.504854                    0.572145           0.90035   1.10466            0.454545         0.601161  0.557692                    0.759692            0.0961538       0.557692                   -9.96503   10.466             0.106712\n",
+            "12       0.601942                    0.482146           0.792308  1.05428            0.4              0.51586   0.532258                    0.720364            0.0769231       0.634615                   -20.7692   5.42804            0.0659879\n",
+            "13       0.699029                    0.397178           0.792308  1.0179             0.4              0.438624  0.513889                    0.681233            0.0769231       0.711538                   -20.7692   1.78953            0.025264\n",
+            "14       0.796117                    0.291779           0.396154  0.942073           0.2              0.346037  0.47561                     0.640356            0.0384615       0.75                       -60.3846   -5.79268           -0.0931373\n",
+            "15       0.893204                    0.217459           1.18846   0.968855           0.6              0.252101  0.48913                     0.598154            0.115385        0.865385                   18.8462    -3.11455           -0.056184\n",
+            "16       1                           0.0345234          1.26049   1                  0.636364         0.127751  0.504854                    0.547917            0.134615        1                          26.049     0                  0\n",
             "\n",
             "Scoring History: \n",
-            "    timestamp            duration    number_of_trees    training_rmse    training_mae    training_deviance    validation_rmse    validation_mae    validation_deviance\n",
-            "--  -------------------  ----------  -----------------  ---------------  --------------  -------------------  -----------------  ----------------  ---------------------\n",
-            "    2025-04-09 03:23:38  0.003 sec   0                  nan              nan             nan                  nan                nan               nan\n",
-            "    2025-04-09 03:23:39  0.513 sec   5                  0.0472362        0.0355093       0.00223126           0.0439236          0.0356622         0.00192929\n",
-            "    2025-04-09 03:23:39  0.986 sec   10                 0.0439708        0.0331847       0.00193343           0.0404349          0.0324082         0.00163498\n",
-            "    2025-04-09 03:23:40  1.413 sec   15                 0.0410159        0.03078         0.00168231           0.0387639          0.0307172         0.00150264\n",
-            "    2025-04-09 03:23:40  1.841 sec   20                 0.0394821        0.0294457       0.00155884           0.0386431          0.0308515         0.00149329\n",
-            "    2025-04-09 03:23:40  2.238 sec   25                 0.0388611        0.0292513       0.00151019           0.0381057          0.0304304         0.00145204\n",
-            "    2025-04-09 03:23:41  2.611 sec   30                 0.0384766        0.0287862       0.00148045           0.037782           0.0299922         0.00142748\n",
-            "    2025-04-09 03:23:41  3.247 sec   35                 0.0382054        0.0286711       0.00145965           0.0377527          0.0297221         0.00142527\n",
-            "    2025-04-09 03:23:43  4.568 sec   40                 0.0379805        0.0284005       0.00144252           0.0375729          0.0296136         0.00141172\n",
-            "    2025-04-09 03:23:44  6.136 sec   45                 0.0377891        0.0282406       0.00142802           0.0372215          0.0293038         0.00138544\n",
+            "    timestamp            duration    number_of_trees    training_rmse    training_logloss    training_auc    training_pr_auc    training_lift    training_classification_error    validation_rmse    validation_logloss    validation_auc    validation_pr_auc    validation_lift    validation_classification_error\n",
+            "--  -------------------  ----------  -----------------  ---------------  ------------------  --------------  -----------------  ---------------  -------------------------------  -----------------  --------------------  ----------------  -------------------  -----------------  ---------------------------------\n",
+            "    2025-04-09 15:21:43  0.006 sec   0                  0.5              0.693147            0.5             0.507212           1                0.492788                         0.5                0.693147              0.5               0.504854             1                  0.495146\n",
+            "    2025-04-09 15:21:44  0.696 sec   5                  0.416868         0.530792            0.863614        0.862843           1.97156          0.210337                         0.56344            0.841232              0.344834          0.419535             0.990385           0.495146\n",
+            "    2025-04-09 15:21:45  1.189 sec   10                 0.356775         0.422749            0.946723        0.949857           1.97156          0.135817                         0.582213           0.902885              0.364819          0.427561             0.990385           0.485437\n",
+            "    2025-04-09 15:21:45  1.383 sec   15                 0.31032          0.34687             0.978676        0.978871           1.97156          0.0721154                        0.575048           0.893553              0.421757          0.455104             0.990385           0.466019\n",
+            "    2025-04-09 15:21:45  1.540 sec   20                 0.274289         0.291499            0.990984        0.991582           1.97156          0.0432692                        0.577017           0.910416              0.454563          0.496729             0.990385           0.485437\n",
+            "    2025-04-09 15:21:45  2.077 sec   25                 0.246853         0.252942            0.995284        0.995437           1.97156          0.0264423                        0.564181           0.894831              0.486991          0.498042             0                  0.485437\n",
+            "    2025-04-09 15:21:46  2.354 sec   30                 0.220367         0.218117            0.998335        0.998343           1.97156          0.0168269                        0.55289            0.868195              0.521682          0.519441             0                  0.485437\n",
             "\n",
             "Variable Importances: \n",
-            "variable                      relative_importance    scaled_importance     percentage\n",
-            "----------------------------  ---------------------  --------------------  ---------------------\n",
-            "RSI14                         2.7104482650756836     1.0                   0.07067964405500003\n",
-            "Financing Profit              2.217656135559082      0.818187959583563     0.057829233753452995\n",
-            "Revenue                       2.1343894004821777     0.7874673086308029    0.05565790907897401\n",
-            "Borrowing                     1.6832706928253174     0.6210303714386947    0.043894205600631404\n",
-            "Financing Margin %            1.6046725511550903     0.5920321637684102    0.04184462260426272\n",
-            "text_embeddings_118           0.6243953704833984     0.23036609055733573   0.016282193282934395\n",
-            "text_embeddings_92            0.6216288208961487     0.2293453923861524    0.016210050699507567\n",
-            "text_embeddings_40            0.44357430934906006    0.16365348679203592   0.01156697019482075\n",
-            "text_embeddings_14            0.43026837706565857    0.15874436070582745   0.01121999491042642\n",
-            "text_embeddings_117           0.4229094684123993     0.1560293453528029    0.011028098591670784\n",
-            "---                           ---                    ---                   ---\n",
-            "Interest                      0.05429164692759514    0.02003050477928202   0.0014157489480416303\n",
-            "Total Assets                  0.053345050662755966   0.019681265032840026  0.0013910648070732518\n",
-            "Other Income                  0.04873652383685112    0.01798098287461327   0.0012708894693367174\n",
-            "CWIP                          0.04434225335717201    0.01635974902326123   0.0011563012377932384\n",
-            "Cash from Operating Activity  0.03819295018911362    0.014091008738750879  0.0009959474820308073\n",
-            "Expenses                      0.036367498338222504   0.013417521672271069  0.000948345655896368\n",
-            "Fixed Assets                  0.03502538800239563    0.012922359911347586  0.0009133477988846492\n",
-            "Other Assets                  0.03407517820596695    0.012571786978939245  0.0008885694288067102\n",
-            "Total Liabilities             0.03146154060959816    0.01160750456482875   0.0008204142910088841\n",
-            "Depreciation                  0.021280160173773766   0.007851158956977753  0.0005549171204984129\n",
-            "[168 rows x 4 columns]\n",
+            "variable             relative_importance    scaled_importance     percentage\n",
+            "-------------------  ---------------------  --------------------  ---------------------\n",
+            "text_embeddings_40   44.77016830444336      1.0                   0.02502580203949166\n",
+            "text_embeddings_53   40.09682846069336      0.8956148698845481    0.022413480437355783\n",
+            "text_embeddings_123  38.98078155517578      0.8706865091527254    0.02178962821651215\n",
+            "text_embeddings_50   38.95652770996094      0.8701447679412582    0.021776070708197336\n",
+            "text_embeddings_10   37.253414154052734     0.8321035092100693    0.02082405769785752\n",
+            "text_embeddings_104  34.9406623840332       0.780445187215555     0.019531266757930488\n",
+            "text_embeddings_54   34.83060836791992      0.7779869874749398    0.01946974833784832\n",
+            "text_embeddings_76   31.192859649658203     0.696733133490641     0.017436305473091503\n",
+            "text_embeddings_111  30.680063247680664     0.6852791581897119    0.01714966055464522\n",
+            "text_embeddings_88   30.59779167175293      0.683441515423478     0.017103672070558146\n",
+            "---                  ---                    ---                   ---\n",
+            "text_embeddings_115  4.673641204833984      0.10439186140763589   0.002612490058121545\n",
+            "text_embeddings_21   4.560031414031982      0.10185423880971668   0.0025489840173350785\n",
+            "text_embeddings_45   4.397317886352539      0.09821982031539768   0.0024580297795675837\n",
+            "text_embeddings_75   4.332575798034668      0.09677372147838603   0.002421839996342991\n",
+            "text_embeddings_79   4.291599273681641      0.09585845745537899   0.0023989347800893483\n",
+            "text_embeddings_66   3.9384031295776367     0.0879693617141653    0.002201503831799138\n",
+            "text_embeddings_103  3.5999584197998047     0.08040975846504725   0.0020123186973896113\n",
+            "text_embeddings_44   3.2862496376037598     0.07340266436473515   0.0018369605475631109\n",
+            "text_embeddings_13   2.807455539703369      0.06270817479649131   0.0015693223687148318\n",
+            "text_embeddings_20   0.9517765045166016     0.021259167444812564  0.000532027715998285\n",
+            "[113 rows x 4 columns]\n",
             "\n"
           ]
         }
@@ -1869,9 +2067,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "dvqjA5tck7Z1",
-        "outputId": "0cc159a9-44e1-4a31-93cf-c5092f365089"
+        "outputId": "39f2d29b-72a0-4a55-b5fe-b267e9c448f9"
       },
-      "execution_count": 31,
+      "execution_count": 24,
       "outputs": [
         {
           "output_type": "stream",
@@ -1879,72 +2077,171 @@
           "text": [
             "Model Details\n",
             "=============\n",
-            "H2ORandomForestEstimator : Distributed Random Forest\n",
-            "Model Key: XRT_1_AutoML_2_20250409_32324\n",
+            "H2OXGBoostEstimator : XGBoost\n",
+            "Model Key: XGBoost_2_AutoML_2_20250409_152135\n",
             "\n",
             "\n",
             "Model Summary: \n",
-            "    number_of_trees    number_of_internal_trees    model_size_in_bytes    min_depth    max_depth    mean_depth    min_leaves    max_leaves    mean_leaves\n",
-            "--  -----------------  --------------------------  ---------------------  -----------  -----------  ------------  ------------  ------------  -------------\n",
-            "    45                 45                          224181                 20           20           20            121           521           384.333\n",
+            "    number_of_trees\n",
+            "--  -----------------\n",
+            "    30\n",
             "\n",
-            "ModelMetricsRegression: drf\n",
+            "ModelMetricsBinomial: xgboost\n",
             "** Reported on train data. **\n",
             "\n",
-            "MSE: 0.0014280151758824398\n",
-            "RMSE: 0.03778908805306685\n",
-            "MAE: 0.02824063408464411\n",
-            "RMSLE: 0.037517547053815714\n",
-            "Mean Residual Deviance: 0.0014280151758824398\n",
+            "MSE: 0.04856159627892655\n",
+            "RMSE: 0.22036695822860228\n",
+            "LogLoss: 0.21811686278860418\n",
+            "Mean Per-Class Error: 0.0168651023003121\n",
+            "AUC: 0.9983354525488383\n",
+            "AUCPR: 0.9983430345133928\n",
+            "Gini: 0.9966709050976765\n",
+            "\n",
+            "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.4844781955083211\n",
+            "       0    1    Error    Rate\n",
+            "-----  ---  ---  -------  ------------\n",
+            "0      402  8    0.0195   (8.0/410.0)\n",
+            "1      6    416  0.0142   (6.0/422.0)\n",
+            "Total  408  424  0.0168   (14.0/832.0)\n",
             "\n",
-            "ModelMetricsRegression: drf\n",
+            "Maximum Metrics: Maximum metrics at their respective thresholds\n",
+            "metric                       threshold    value     idx\n",
+            "---------------------------  -----------  --------  -----\n",
+            "max f1                       0.484478     0.983452  201\n",
+            "max f2                       0.445234     0.989657  212\n",
+            "max f0point5                 0.551076     0.986395  192\n",
+            "max accuracy                 0.484478     0.983173  201\n",
+            "max precision                0.982302     1         0\n",
+            "max recall                   0.336748     1         234\n",
+            "max specificity              0.982302     1         0\n",
+            "max absolute_mcc             0.484478     0.966348  201\n",
+            "max min_per_class_accuracy   0.488712     0.981043  200\n",
+            "max mean_per_class_accuracy  0.484478     0.983135  201\n",
+            "max tns                      0.982302     410       0\n",
+            "max fns                      0.982302     421       0\n",
+            "max fps                      0.00968025   410       399\n",
+            "max tps                      0.336748     422       234\n",
+            "max tnr                      0.982302     1         0\n",
+            "max fnr                      0.982302     0.99763   0\n",
+            "max fpr                      0.00968025   1         399\n",
+            "max tpr                      0.336748     1         234\n",
+            "\n",
+            "Gains/Lift Table: Avg response rate: 50.72 %, avg score: 51.05 %\n",
+            "group    cumulative_data_fraction    lower_threshold    lift      cumulative_lift    response_rate    score      cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain      cumulative_gain    kolmogorov_smirnov\n",
+            "-------  --------------------------  -----------------  --------  -----------------  ---------------  ---------  --------------------------  ------------------  --------------  -------------------------  --------  -----------------  --------------------\n",
+            "1        0.0108173                   0.972137           1.97156   1.97156            1                0.976887   1                           0.976887            0.021327        0.021327                   97.1564   97.1564            0.021327\n",
+            "2        0.0204327                   0.963878           1.97156   1.97156            1                0.968609   1                           0.972991            0.0189573       0.0402844                  97.1564   97.1564            0.0402844\n",
+            "3        0.0300481                   0.958543           1.97156   1.97156            1                0.961398   1                           0.969281            0.0189573       0.0592417                  97.1564   97.1564            0.0592417\n",
+            "4        0.0408654                   0.951325           1.97156   1.97156            1                0.954896   1                           0.965473            0.021327        0.0805687                  97.1564   97.1564            0.0805687\n",
+            "5        0.0504808                   0.945304           1.97156   1.97156            1                0.949574   1                           0.962445            0.0189573       0.0995261                  97.1564   97.1564            0.0995261\n",
+            "6        0.100962                    0.919195           1.97156   1.97156            1                0.932157   1                           0.947301            0.0995261       0.199052                   97.1564   97.1564            0.199052\n",
+            "7        0.15024                     0.900065           1.97156   1.97156            1                0.909711   1                           0.934972            0.0971564       0.296209                   97.1564   97.1564            0.296209\n",
+            "8        0.200721                    0.876327           1.97156   1.97156            1                0.889046   1                           0.923421            0.0995261       0.395735                   97.1564   97.1564            0.395735\n",
+            "9        0.300481                    0.820207           1.97156   1.97156            1                0.850731   1                           0.899288            0.196682        0.592417                   97.1564   97.1564            0.592417\n",
+            "10       0.40024                     0.745953           1.97156   1.97156            1                0.782398   1                           0.870153            0.196682        0.7891                     97.1564   97.1564            0.7891\n",
+            "11       0.501202                    0.510782           1.83074   1.9432             0.928571         0.652121   0.985612                    0.826233            0.184834        0.973934                   83.0738   94.3196            0.9593\n",
+            "12       0.59976                     0.284746           0.264478  1.66733            0.134146         0.377337   0.845691                    0.752467            0.0260664       1                          -73.5522  66.7335            0.812195\n",
+            "13       0.699519                    0.202861           0         1.42955            0                0.24154    0.725086                    0.679603            0               1                          -100      42.9553            0.609756\n",
+            "14       0.799279                    0.138646           0         1.25113            0                0.170537   0.634586                    0.616065            0               1                          -100      25.1128            0.407317\n",
+            "15       0.899038                    0.0952722          0         1.1123             0                0.117953   0.564171                    0.560793            0               1                          -100      11.2299            0.204878\n",
+            "16       1                           0.00968025         0         1                  0                0.0629315  0.507212                    0.510528            0               1                          -100      0                  0\n",
+            "\n",
+            "ModelMetricsBinomial: xgboost\n",
             "** Reported on validation data. **\n",
             "\n",
-            "MSE: 0.0013854433734110827\n",
-            "RMSE: 0.037221544479119656\n",
-            "MAE: 0.02930384278875654\n",
-            "RMSLE: 0.037699716489869806\n",
-            "Mean Residual Deviance: 0.0013854433734110827\n",
+            "MSE: 0.30568781210268564\n",
+            "RMSE: 0.5528904159982208\n",
+            "LogLoss: 0.8681948644613136\n",
+            "Mean Per-Class Error: 0.49019607843137253\n",
+            "AUC: 0.5216817496229261\n",
+            "AUCPR: 0.5194412079480231\n",
+            "Gini: 0.043363499245852255\n",
+            "\n",
+            "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.06307325512170792\n",
+            "       0    1    Error    Rate\n",
+            "-----  ---  ---  -------  ------------\n",
+            "0      1    50   0.9804   (50.0/51.0)\n",
+            "1      0    52   0        (0.0/52.0)\n",
+            "Total  1    102  0.4854   (50.0/103.0)\n",
+            "\n",
+            "Maximum Metrics: Maximum metrics at their respective thresholds\n",
+            "metric                       threshold    value     idx\n",
+            "---------------------------  -----------  --------  -----\n",
+            "max f1                       0.0630733    0.675325  100\n",
+            "max f2                       0.0630733    0.83871   100\n",
+            "max f0point5                 0.58902      0.57377   46\n",
+            "max accuracy                 0.655599     0.572816  38\n",
+            "max precision                0.918544     0.666667  7\n",
+            "max recall                   0.0630733    1         100\n",
+            "max specificity              0.960617     0.980392  0\n",
+            "max absolute_mcc             0.655599     0.151635  38\n",
+            "max min_per_class_accuracy   0.572145     0.54902   50\n",
+            "max mean_per_class_accuracy  0.655599     0.573906  38\n",
+            "max tns                      0.960617     50        0\n",
+            "max fns                      0.960617     52        0\n",
+            "max fps                      0.0345234    51        101\n",
+            "max tps                      0.0630733    52        100\n",
+            "max tnr                      0.960617     0.980392  0\n",
+            "max fnr                      0.960617     1         0\n",
+            "max fpr                      0.0345234    1         101\n",
+            "max tpr                      0.0630733    1         100\n",
+            "\n",
+            "Gains/Lift Table: Avg response rate: 50.49 %, avg score: 54.79 %\n",
+            "group    cumulative_data_fraction    lower_threshold    lift      cumulative_lift    response_rate    score     cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain       cumulative_gain    kolmogorov_smirnov\n",
+            "-------  --------------------------  -----------------  --------  -----------------  ---------------  --------  --------------------------  ------------------  --------------  -------------------------  ---------  -----------------  --------------------\n",
+            "1        0.0194175                   0.953524           0         0                  0                0.957244  0                           0.957244            0               0                          -100       -100               -0.0392157\n",
+            "2        0.0291262                   0.93633            1.98077   0.660256           1                0.936525  0.333333                    0.950338            0.0192308       0.0192308                  98.0769    -33.9744           -0.0199849\n",
+            "3        0.0485437                   0.931649           0.990385  0.792308           0.5              0.931649  0.4                         0.942862            0.0192308       0.0384615                  -0.961538  -20.7692           -0.020362\n",
+            "4        0.0485437                   0.9311             0         0.792308           0                0         0.4                         0.942862            0               0.0384615                  -100       -20.7692           -0.020362\n",
+            "5        0.0582524                   0.924741           1.98077   0.990385           1                0.924782  0.5                         0.939849            0.0192308       0.0576923                  98.0769    -0.961538          -0.00113122\n",
+            "6        0.106796                    0.848649           1.18846   1.08042            0.6              0.89536   0.545455                    0.919626            0.0576923       0.115385                   18.8462    8.04196            0.0173454\n",
+            "7        0.15534                     0.816536           0.792308  0.990385           0.4              0.832529  0.5                         0.892409            0.0384615       0.153846                   -20.7692   -0.961538          -0.00301659\n",
+            "8        0.203883                    0.801701           1.18846   1.03755            0.6              0.808636  0.52381                     0.872463            0.0576923       0.211538                   18.8462    3.75458            0.01546\n",
+            "9        0.300971                    0.720564           0.990385  1.02233            0.5              0.777296  0.516129                    0.841764            0.0961538       0.307692                   -0.961538  2.23325            0.0135747\n",
+            "10       0.398058                    0.641569           1.58462   1.15947            0.8              0.679651  0.585366                    0.802224            0.153846        0.461538                   58.4615    15.9475            0.128205\n",
+            "11       0.504854                    0.572145           0.90035   1.10466            0.454545         0.601161  0.557692                    0.759692            0.0961538       0.557692                   -9.96503   10.466             0.106712\n",
+            "12       0.601942                    0.482146           0.792308  1.05428            0.4              0.51586   0.532258                    0.720364            0.0769231       0.634615                   -20.7692   5.42804            0.0659879\n",
+            "13       0.699029                    0.397178           0.792308  1.0179             0.4              0.438624  0.513889                    0.681233            0.0769231       0.711538                   -20.7692   1.78953            0.025264\n",
+            "14       0.796117                    0.291779           0.396154  0.942073           0.2              0.346037  0.47561                     0.640356            0.0384615       0.75                       -60.3846   -5.79268           -0.0931373\n",
+            "15       0.893204                    0.217459           1.18846   0.968855           0.6              0.252101  0.48913                     0.598154            0.115385        0.865385                   18.8462    -3.11455           -0.056184\n",
+            "16       1                           0.0345234          1.26049   1                  0.636364         0.127751  0.504854                    0.547917            0.134615        1                          26.049     0                  0\n",
             "\n",
             "Scoring History: \n",
-            "    timestamp            duration    number_of_trees    training_rmse    training_mae    training_deviance    validation_rmse    validation_mae    validation_deviance\n",
-            "--  -------------------  ----------  -----------------  ---------------  --------------  -------------------  -----------------  ----------------  ---------------------\n",
-            "    2025-04-09 03:23:38  0.003 sec   0                  nan              nan             nan                  nan                nan               nan\n",
-            "    2025-04-09 03:23:39  0.513 sec   5                  0.0472362        0.0355093       0.00223126           0.0439236          0.0356622         0.00192929\n",
-            "    2025-04-09 03:23:39  0.986 sec   10                 0.0439708        0.0331847       0.00193343           0.0404349          0.0324082         0.00163498\n",
-            "    2025-04-09 03:23:40  1.413 sec   15                 0.0410159        0.03078         0.00168231           0.0387639          0.0307172         0.00150264\n",
-            "    2025-04-09 03:23:40  1.841 sec   20                 0.0394821        0.0294457       0.00155884           0.0386431          0.0308515         0.00149329\n",
-            "    2025-04-09 03:23:40  2.238 sec   25                 0.0388611        0.0292513       0.00151019           0.0381057          0.0304304         0.00145204\n",
-            "    2025-04-09 03:23:41  2.611 sec   30                 0.0384766        0.0287862       0.00148045           0.037782           0.0299922         0.00142748\n",
-            "    2025-04-09 03:23:41  3.247 sec   35                 0.0382054        0.0286711       0.00145965           0.0377527          0.0297221         0.00142527\n",
-            "    2025-04-09 03:23:43  4.568 sec   40                 0.0379805        0.0284005       0.00144252           0.0375729          0.0296136         0.00141172\n",
-            "    2025-04-09 03:23:44  6.136 sec   45                 0.0377891        0.0282406       0.00142802           0.0372215          0.0293038         0.00138544\n",
+            "    timestamp            duration    number_of_trees    training_rmse    training_logloss    training_auc    training_pr_auc    training_lift    training_classification_error    validation_rmse    validation_logloss    validation_auc    validation_pr_auc    validation_lift    validation_classification_error\n",
+            "--  -------------------  ----------  -----------------  ---------------  ------------------  --------------  -----------------  ---------------  -------------------------------  -----------------  --------------------  ----------------  -------------------  -----------------  ---------------------------------\n",
+            "    2025-04-09 15:21:43  0.006 sec   0                  0.5              0.693147            0.5             0.507212           1                0.492788                         0.5                0.693147              0.5               0.504854             1                  0.495146\n",
+            "    2025-04-09 15:21:44  0.696 sec   5                  0.416868         0.530792            0.863614        0.862843           1.97156          0.210337                         0.56344            0.841232              0.344834          0.419535             0.990385           0.495146\n",
+            "    2025-04-09 15:21:45  1.189 sec   10                 0.356775         0.422749            0.946723        0.949857           1.97156          0.135817                         0.582213           0.902885              0.364819          0.427561             0.990385           0.485437\n",
+            "    2025-04-09 15:21:45  1.383 sec   15                 0.31032          0.34687             0.978676        0.978871           1.97156          0.0721154                        0.575048           0.893553              0.421757          0.455104             0.990385           0.466019\n",
+            "    2025-04-09 15:21:45  1.540 sec   20                 0.274289         0.291499            0.990984        0.991582           1.97156          0.0432692                        0.577017           0.910416              0.454563          0.496729             0.990385           0.485437\n",
+            "    2025-04-09 15:21:45  2.077 sec   25                 0.246853         0.252942            0.995284        0.995437           1.97156          0.0264423                        0.564181           0.894831              0.486991          0.498042             0                  0.485437\n",
+            "    2025-04-09 15:21:46  2.354 sec   30                 0.220367         0.218117            0.998335        0.998343           1.97156          0.0168269                        0.55289            0.868195              0.521682          0.519441             0                  0.485437\n",
             "\n",
             "Variable Importances: \n",
-            "variable                      relative_importance    scaled_importance     percentage\n",
-            "----------------------------  ---------------------  --------------------  ---------------------\n",
-            "RSI14                         2.7104482650756836     1.0                   0.07067964405500003\n",
-            "Financing Profit              2.217656135559082      0.818187959583563     0.057829233753452995\n",
-            "Revenue                       2.1343894004821777     0.7874673086308029    0.05565790907897401\n",
-            "Borrowing                     1.6832706928253174     0.6210303714386947    0.043894205600631404\n",
-            "Financing Margin %            1.6046725511550903     0.5920321637684102    0.04184462260426272\n",
-            "text_embeddings_118           0.6243953704833984     0.23036609055733573   0.016282193282934395\n",
-            "text_embeddings_92            0.6216288208961487     0.2293453923861524    0.016210050699507567\n",
-            "text_embeddings_40            0.44357430934906006    0.16365348679203592   0.01156697019482075\n",
-            "text_embeddings_14            0.43026837706565857    0.15874436070582745   0.01121999491042642\n",
-            "text_embeddings_117           0.4229094684123993     0.1560293453528029    0.011028098591670784\n",
-            "---                           ---                    ---                   ---\n",
-            "Interest                      0.05429164692759514    0.02003050477928202   0.0014157489480416303\n",
-            "Total Assets                  0.053345050662755966   0.019681265032840026  0.0013910648070732518\n",
-            "Other Income                  0.04873652383685112    0.01798098287461327   0.0012708894693367174\n",
-            "CWIP                          0.04434225335717201    0.01635974902326123   0.0011563012377932384\n",
-            "Cash from Operating Activity  0.03819295018911362    0.014091008738750879  0.0009959474820308073\n",
-            "Expenses                      0.036367498338222504   0.013417521672271069  0.000948345655896368\n",
-            "Fixed Assets                  0.03502538800239563    0.012922359911347586  0.0009133477988846492\n",
-            "Other Assets                  0.03407517820596695    0.012571786978939245  0.0008885694288067102\n",
-            "Total Liabilities             0.03146154060959816    0.01160750456482875   0.0008204142910088841\n",
-            "Depreciation                  0.021280160173773766   0.007851158956977753  0.0005549171204984129\n",
-            "[168 rows x 4 columns]\n",
+            "variable             relative_importance    scaled_importance     percentage\n",
+            "-------------------  ---------------------  --------------------  ---------------------\n",
+            "text_embeddings_40   44.77016830444336      1.0                   0.02502580203949166\n",
+            "text_embeddings_53   40.09682846069336      0.8956148698845481    0.022413480437355783\n",
+            "text_embeddings_123  38.98078155517578      0.8706865091527254    0.02178962821651215\n",
+            "text_embeddings_50   38.95652770996094      0.8701447679412582    0.021776070708197336\n",
+            "text_embeddings_10   37.253414154052734     0.8321035092100693    0.02082405769785752\n",
+            "text_embeddings_104  34.9406623840332       0.780445187215555     0.019531266757930488\n",
+            "text_embeddings_54   34.83060836791992      0.7779869874749398    0.01946974833784832\n",
+            "text_embeddings_76   31.192859649658203     0.696733133490641     0.017436305473091503\n",
+            "text_embeddings_111  30.680063247680664     0.6852791581897119    0.01714966055464522\n",
+            "text_embeddings_88   30.59779167175293      0.683441515423478     0.017103672070558146\n",
+            "---                  ---                    ---                   ---\n",
+            "text_embeddings_115  4.673641204833984      0.10439186140763589   0.002612490058121545\n",
+            "text_embeddings_21   4.560031414031982      0.10185423880971668   0.0025489840173350785\n",
+            "text_embeddings_45   4.397317886352539      0.09821982031539768   0.0024580297795675837\n",
+            "text_embeddings_75   4.332575798034668      0.09677372147838603   0.002421839996342991\n",
+            "text_embeddings_79   4.291599273681641      0.09585845745537899   0.0023989347800893483\n",
+            "text_embeddings_66   3.9384031295776367     0.0879693617141653    0.002201503831799138\n",
+            "text_embeddings_103  3.5999584197998047     0.08040975846504725   0.0020123186973896113\n",
+            "text_embeddings_44   3.2862496376037598     0.07340266436473515   0.0018369605475631109\n",
+            "text_embeddings_13   2.807455539703369      0.06270817479649131   0.0015693223687148318\n",
+            "text_embeddings_20   0.9517765045166016     0.021259167444812564  0.000532027715998285\n",
+            "[113 rows x 4 columns]\n",
             "\n"
           ]
         }
@@ -1954,7 +2251,7 @@
       "cell_type": "code",
       "source": [
         "# Get the best model using a non-default metric\n",
-        "m = aml.get_best_model(criterion=\"RMSE\")\n",
+        "m = aml.get_best_model(criterion=\"logloss\")\n",
         "print(m)"
       ],
       "metadata": {
@@ -1962,9 +2259,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "4RExMtKHk-k-",
-        "outputId": "d9d4b9b5-8328-41dd-a4ad-8a2a7669da5a"
+        "outputId": "c6afd883-7a20-4363-8e3d-88520a2020d4"
       },
-      "execution_count": 32,
+      "execution_count": 25,
       "outputs": [
         {
           "output_type": "stream",
@@ -1972,72 +2269,178 @@
           "text": [
             "Model Details\n",
             "=============\n",
-            "H2ORandomForestEstimator : Distributed Random Forest\n",
-            "Model Key: XRT_1_AutoML_2_20250409_32324\n",
+            "H2OGeneralizedLinearEstimator : Generalized Linear Modeling\n",
+            "Model Key: GLM_1_AutoML_2_20250409_152135\n",
             "\n",
             "\n",
-            "Model Summary: \n",
-            "    number_of_trees    number_of_internal_trees    model_size_in_bytes    min_depth    max_depth    mean_depth    min_leaves    max_leaves    mean_leaves\n",
-            "--  -----------------  --------------------------  ---------------------  -----------  -----------  ------------  ------------  ------------  -------------\n",
-            "    45                 45                          224181                 20           20           20            121           521           384.333\n",
+            "GLM Model: summary\n",
+            "    family    link    regularization             lambda_search                                                              number_of_predictors_total    number_of_active_predictors    number_of_iterations    training_frame\n",
+            "--  --------  ------  -------------------------  -------------------------------------------------------------------------  ----------------------------  -----------------------------  ----------------------  -----------------------------------------------\n",
+            "    binomial  logit   Ridge ( lambda = 8.3944 )  nlambda = 30, lambda.max = 8.3944, lambda.min = 8.3944, lambda.1se = -1.0  128                           128                            2                       AutoML_2_20250409_152135_training_py_3_sid_8ad7\n",
             "\n",
-            "ModelMetricsRegression: drf\n",
+            "ModelMetricsBinomialGLM: glm\n",
             "** Reported on train data. **\n",
             "\n",
-            "MSE: 0.0014280151758824398\n",
-            "RMSE: 0.03778908805306685\n",
-            "MAE: 0.02824063408464411\n",
-            "RMSLE: 0.037517547053815714\n",
-            "Mean Residual Deviance: 0.0014280151758824398\n",
+            "MSE: 0.24717480793730287\n",
+            "RMSE: 0.49716678080630333\n",
+            "LogLoss: 0.6874882191328064\n",
+            "AUC: 0.6474540515547336\n",
+            "AUCPR: 0.644085032591834\n",
+            "Gini: 0.29490810310946713\n",
+            "Null degrees of freedom: 831\n",
+            "Residual degrees of freedom: 703\n",
+            "Null deviance: 1153.223825527466\n",
+            "Residual deviance: 1143.98039663699\n",
+            "AIC: 1401.98039663699\n",
+            "\n",
+            "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.4969545190426191\n",
+            "       0    1    Error    Rate\n",
+            "-----  ---  ---  -------  -------------\n",
+            "0      92   318  0.7756   (318.0/410.0)\n",
+            "1      33   389  0.0782   (33.0/422.0)\n",
+            "Total  125  707  0.4219   (351.0/832.0)\n",
+            "\n",
+            "Maximum Metrics: Maximum metrics at their respective thresholds\n",
+            "metric                       threshold    value     idx\n",
+            "---------------------------  -----------  --------  -----\n",
+            "max f1                       0.496955     0.689105  321\n",
+            "max f2                       0.477531     0.837302  399\n",
+            "max f0point5                 0.501609     0.617147  263\n",
+            "max accuracy                 0.501609     0.609375  263\n",
+            "max precision                0.581164     1         0\n",
+            "max recall                   0.477531     1         399\n",
+            "max specificity              0.581164     1         0\n",
+            "max absolute_mcc             0.501609     0.229957  263\n",
+            "max min_per_class_accuracy   0.506473     0.590047  205\n",
+            "max mean_per_class_accuracy  0.501609     0.60671   263\n",
+            "max tns                      0.581164     410       0\n",
+            "max fns                      0.581164     421       0\n",
+            "max fps                      0.477737     410       398\n",
+            "max tps                      0.477531     422       399\n",
+            "max tnr                      0.581164     1         0\n",
+            "max fnr                      0.581164     0.99763   0\n",
+            "max fpr                      0.477737     1         398\n",
+            "max tpr                      0.477531     1         399\n",
             "\n",
-            "ModelMetricsRegression: drf\n",
+            "Gains/Lift Table: Avg response rate: 50.72 %, avg score: 50.72 %\n",
+            "group    cumulative_data_fraction    lower_threshold    lift      cumulative_lift    response_rate    score     cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain      cumulative_gain    kolmogorov_smirnov\n",
+            "-------  --------------------------  -----------------  --------  -----------------  ---------------  --------  --------------------------  ------------------  --------------  -------------------------  --------  -----------------  --------------------\n",
+            "1        0.0108173                   0.55484            1.53344   1.53344            0.777778         0.563771  0.777778                    0.563771            0.0165877       0.0165877                  53.3439   53.3439            0.0117096\n",
+            "2        0.0204327                   0.542193           1.47867   1.50767            0.75             0.549144  0.764706                    0.556888            0.014218        0.0308057                  47.8673   50.7667            0.0210496\n",
+            "3        0.0300481                   0.531147           1.97156   1.65611            1                0.535422  0.84                        0.550019            0.0189573       0.049763                   97.1564   65.6114            0.0400069\n",
+            "4        0.0408654                   0.527754           1.09531   1.50767            0.555556         0.529574  0.764706                    0.544607            0.0118483       0.0616114                  9.53133   50.7667            0.0420992\n",
+            "5        0.0504808                   0.525382           1.23223   1.4552             0.625            0.526306  0.738095                    0.541121            0.0118483       0.0734597                  23.2227   45.5202            0.0466304\n",
+            "6        0.100962                    0.520465           1.36132   1.40826            0.690476         0.521961  0.714286                    0.531541            0.0687204       0.14218                    36.1318   40.826             0.0836435\n",
+            "7        0.15024                     0.516847           1.53878   1.45107            0.780488         0.518698  0.736                       0.527328            0.0758294       0.218009                   53.8782   45.1071            0.137522\n",
+            "8        0.200721                    0.514332           1.36132   1.4285             0.690476         0.515369  0.724551                    0.524321            0.0687204       0.28673                    36.1318   42.8498            0.174535\n",
+            "9        0.300481                    0.510604           0.950151  1.26969            0.481928         0.512496  0.644                       0.520395            0.0947867       0.381517                   -4.98487  26.9687            0.164443\n",
+            "10       0.40024                     0.508288           1.11643   1.23149            0.566265         0.509393  0.624625                    0.517653            0.111374        0.492891                   11.6428   23.1487            0.188013\n",
+            "11       0.5                         0.50637            0.973905  1.18009            0.493976         0.507386  0.598558                    0.515604            0.0971564       0.590047                   -2.60949  18.0095            0.18273\n",
+            "12       0.59976                     0.504004           1.14018   1.17346            0.578313         0.505165  0.59519                     0.513868            0.113744        0.703791                   14.0182   17.3456            0.211109\n",
+            "13       0.699519                    0.501222           0.973905  1.145              0.493976         0.502581  0.580756                    0.512258            0.0971564       0.800948                   -2.60949  14.4998            0.205826\n",
+            "14       0.799279                    0.498592           0.760121  1.09696            0.385542         0.499945  0.556391                    0.510722            0.0758294       0.876777                   -23.9879  9.69604            0.157265\n",
+            "15       0.899038                    0.494264           0.68886   1.05168            0.349398         0.496661  0.533422                    0.509161            0.0687204       0.945498                   -31.114   5.16765            0.0942781\n",
+            "16       1                           0.477531           0.539833  1                  0.27381          0.489849  0.507212                    0.507212            0.0545024       1                          -46.0167  0                  0\n",
+            "\n",
+            "ModelMetricsBinomialGLM: glm\n",
             "** Reported on validation data. **\n",
             "\n",
-            "MSE: 0.0013854433734110827\n",
-            "RMSE: 0.037221544479119656\n",
-            "MAE: 0.02930384278875654\n",
-            "RMSLE: 0.037699716489869806\n",
-            "Mean Residual Deviance: 0.0013854433734110827\n",
+            "MSE: 0.2520009197817637\n",
+            "RMSE: 0.5019969320441746\n",
+            "LogLoss: 0.6971647390484171\n",
+            "AUC: 0.4342006033182504\n",
+            "AUCPR: 0.4602129339086532\n",
+            "Gini: -0.13159879336349922\n",
+            "Null degrees of freedom: 102\n",
+            "Residual degrees of freedom: -26\n",
+            "Null deviance: 142.7808998601915\n",
+            "Residual deviance: 143.61593624397392\n",
+            "AIC: 401.6159362439739\n",
+            "\n",
+            "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.4835198410872216\n",
+            "       0    1    Error    Rate\n",
+            "-----  ---  ---  -------  ------------\n",
+            "0      0    51   1        (51.0/51.0)\n",
+            "1      0    52   0        (0.0/52.0)\n",
+            "Total  0    103  0.4951   (51.0/103.0)\n",
+            "\n",
+            "Maximum Metrics: Maximum metrics at their respective thresholds\n",
+            "metric                       threshold    value     idx\n",
+            "---------------------------  -----------  --------  -----\n",
+            "max f1                       0.48352      0.670968  101\n",
+            "max f2                       0.48352      0.836013  101\n",
+            "max f0point5                 0.48352      0.560345  101\n",
+            "max accuracy                 0.520241     0.504854  16\n",
+            "max precision                0.520241     0.529412  16\n",
+            "max recall                   0.48352      1         101\n",
+            "max specificity              0.570215     0.980392  0\n",
+            "max absolute_mcc             0.505347     0.191478  61\n",
+            "max min_per_class_accuracy   0.508235     0.411765  51\n",
+            "max mean_per_class_accuracy  0.520241     0.508107  16\n",
+            "max tns                      0.570215     50        0\n",
+            "max fns                      0.570215     52        0\n",
+            "max fps                      0.492519     51        99\n",
+            "max tps                      0.48352      52        101\n",
+            "max tnr                      0.570215     0.980392  0\n",
+            "max fnr                      0.570215     1         0\n",
+            "max fpr                      0.492519     1         99\n",
+            "max tpr                      0.48352      1         101\n",
+            "\n",
+            "Gains/Lift Table: Avg response rate: 50.49 %, avg score: 51.06 %\n",
+            "group    cumulative_data_fraction    lower_threshold    lift      cumulative_lift    response_rate    score     cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain       cumulative_gain    kolmogorov_smirnov\n",
+            "-------  --------------------------  -----------------  --------  -----------------  ---------------  --------  --------------------------  ------------------  --------------  -------------------------  ---------  -----------------  --------------------\n",
+            "1        0.0194175                   0.564495           0         0                  0                0.567362  0                           0.567362            0               0                          -100       -100               -0.0392157\n",
+            "2        0.0291262                   0.563668           1.98077   0.660256           1                0.563813  0.333333                    0.566179            0.0192308       0.0192308                  98.0769    -33.9744           -0.0199849\n",
+            "3        0.038835                    0.559577           0         0.495192           0                0.560191  0.25                        0.564682            0               0.0192308                  -100       -50.4808           -0.0395928\n",
+            "4        0.0485437                   0.548342           1.98077   0.792308           1                0.549947  0.4                         0.561735            0.0192308       0.0384615                  98.0769    -20.7692           -0.020362\n",
+            "5        0.0582524                   0.529667           0         0.660256           0                0.529885  0.333333                    0.556427            0               0.0384615                  -100       -33.9744           -0.0399698\n",
+            "6        0.106796                    0.522543           1.18846   0.90035            0.6              0.526039  0.454545                    0.542614            0.0576923       0.0961538                  18.8462    -9.96503           -0.0214932\n",
+            "7        0.15534                     0.52082            1.18846   0.990385           0.6              0.521744  0.5                         0.536092            0.0576923       0.153846                   18.8462    -0.961538          -0.00301659\n",
+            "8        0.203883                    0.518536           0.792308  0.943223           0.4              0.519411  0.47619                     0.532121            0.0384615       0.192308                   -20.7692   -5.67766           -0.0233786\n",
+            "9        0.300971                    0.514355           0.990385  0.958437           0.5              0.516476  0.483871                    0.527074            0.0961538       0.288462                   -0.961538  -4.15633           -0.025264\n",
+            "10       0.398058                    0.510991           0.594231  0.869606           0.3              0.512703  0.439024                    0.523569            0.0576923       0.346154                   -40.5769   -13.0394           -0.104827\n",
+            "11       0.504854                    0.508235           0.72028   0.838018           0.363636         0.50967   0.423077                    0.520629            0.0769231       0.423077                   -27.972    -16.1982           -0.165158\n",
+            "12       0.601942                    0.505945           0.990385  0.862593           0.5              0.506583  0.435484                    0.518363            0.0961538       0.519231                   -0.961538  -13.7407           -0.167044\n",
+            "13       0.699029                    0.502526           1.58462   0.962874           0.8              0.50416   0.486111                    0.516391            0.153846        0.673077                   58.4615    -3.71261           -0.0524133\n",
+            "14       0.796117                    0.499338           1.18846   0.990385           0.6              0.5011    0.5                         0.514526            0.115385        0.788462                   18.8462    -0.961538          -0.01546\n",
+            "15       0.893204                    0.496516           0.792308  0.968855           0.4              0.498223  0.48913                     0.512754            0.0769231       0.865385                   -20.7692   -3.11455           -0.056184\n",
+            "16       1                           0.48352            1.26049   1                  0.636364         0.492397  0.504854                    0.51058             0.134615        1                          26.049     0                  0\n",
             "\n",
             "Scoring History: \n",
-            "    timestamp            duration    number_of_trees    training_rmse    training_mae    training_deviance    validation_rmse    validation_mae    validation_deviance\n",
-            "--  -------------------  ----------  -----------------  ---------------  --------------  -------------------  -----------------  ----------------  ---------------------\n",
-            "    2025-04-09 03:23:38  0.003 sec   0                  nan              nan             nan                  nan                nan               nan\n",
-            "    2025-04-09 03:23:39  0.513 sec   5                  0.0472362        0.0355093       0.00223126           0.0439236          0.0356622         0.00192929\n",
-            "    2025-04-09 03:23:39  0.986 sec   10                 0.0439708        0.0331847       0.00193343           0.0404349          0.0324082         0.00163498\n",
-            "    2025-04-09 03:23:40  1.413 sec   15                 0.0410159        0.03078         0.00168231           0.0387639          0.0307172         0.00150264\n",
-            "    2025-04-09 03:23:40  1.841 sec   20                 0.0394821        0.0294457       0.00155884           0.0386431          0.0308515         0.00149329\n",
-            "    2025-04-09 03:23:40  2.238 sec   25                 0.0388611        0.0292513       0.00151019           0.0381057          0.0304304         0.00145204\n",
-            "    2025-04-09 03:23:41  2.611 sec   30                 0.0384766        0.0287862       0.00148045           0.037782           0.0299922         0.00142748\n",
-            "    2025-04-09 03:23:41  3.247 sec   35                 0.0382054        0.0286711       0.00145965           0.0377527          0.0297221         0.00142527\n",
-            "    2025-04-09 03:23:43  4.568 sec   40                 0.0379805        0.0284005       0.00144252           0.0375729          0.0296136         0.00141172\n",
-            "    2025-04-09 03:23:44  6.136 sec   45                 0.0377891        0.0282406       0.00142802           0.0372215          0.0293038         0.00138544\n",
+            "    timestamp            duration    iteration    lambda    predictors    deviance_train      deviance_test       alpha    iterations    training_rmse        training_logloss    training_r2           training_auc        training_pr_auc    training_lift       training_classification_error    validation_rmse     validation_logloss    validation_r2         validation_auc      validation_pr_auc    validation_lift    validation_classification_error\n",
+            "--  -------------------  ----------  -----------  --------  ------------  ------------------  ------------------  -------  ------------  -------------------  ------------------  --------------------  ------------------  -----------------  ------------------  -------------------------------  ------------------  --------------------  --------------------  ------------------  -------------------  -----------------  ---------------------------------\n",
+            "    2025-04-09 15:21:39  0.000 sec   2            8.4       129           1.3749764382656129  1.3943294780968343  0.0\n",
+            "    2025-04-09 15:21:39  0.091 sec   4            6.1       129           1.3715574858622348  1.3972670743889783  0.0\n",
+            "    2025-04-09 15:21:38  1.244 sec   5                                                                                     5             0.49716678080630333  0.6874882191328064  0.011095051152516855  0.6474540515547336  0.644085032591834  1.5334386519220644  0.421875                         0.5019969320441746  0.6971647390484171    -0.00809870209831498  0.4342006033182504  0.4602129339086532   0.0                0.49514563106796117\n",
+            "    2025-04-09 15:21:39  0.207 sec   6            4.4       129           1.3673117597744593  1.401149508013086   0.0\n",
+            "    2025-04-09 15:21:39  0.310 sec   8            3.2       129           1.362134972955739   1.4061323998052682  0.0\n",
+            "    2025-04-09 15:21:39  0.466 sec   10           2.4       129           1.3559502468240774  1.4124134258298293  0.0\n",
             "\n",
             "Variable Importances: \n",
-            "variable                      relative_importance    scaled_importance     percentage\n",
-            "----------------------------  ---------------------  --------------------  ---------------------\n",
-            "RSI14                         2.7104482650756836     1.0                   0.07067964405500003\n",
-            "Financing Profit              2.217656135559082      0.818187959583563     0.057829233753452995\n",
-            "Revenue                       2.1343894004821777     0.7874673086308029    0.05565790907897401\n",
-            "Borrowing                     1.6832706928253174     0.6210303714386947    0.043894205600631404\n",
-            "Financing Margin %            1.6046725511550903     0.5920321637684102    0.04184462260426272\n",
-            "text_embeddings_118           0.6243953704833984     0.23036609055733573   0.016282193282934395\n",
-            "text_embeddings_92            0.6216288208961487     0.2293453923861524    0.016210050699507567\n",
-            "text_embeddings_40            0.44357430934906006    0.16365348679203592   0.01156697019482075\n",
-            "text_embeddings_14            0.43026837706565857    0.15874436070582745   0.01121999491042642\n",
-            "text_embeddings_117           0.4229094684123993     0.1560293453528029    0.011028098591670784\n",
-            "---                           ---                    ---                   ---\n",
-            "Interest                      0.05429164692759514    0.02003050477928202   0.0014157489480416303\n",
-            "Total Assets                  0.053345050662755966   0.019681265032840026  0.0013910648070732518\n",
-            "Other Income                  0.04873652383685112    0.01798098287461327   0.0012708894693367174\n",
-            "CWIP                          0.04434225335717201    0.01635974902326123   0.0011563012377932384\n",
-            "Cash from Operating Activity  0.03819295018911362    0.014091008738750879  0.0009959474820308073\n",
-            "Expenses                      0.036367498338222504   0.013417521672271069  0.000948345655896368\n",
-            "Fixed Assets                  0.03502538800239563    0.012922359911347586  0.0009133477988846492\n",
-            "Other Assets                  0.03407517820596695    0.012571786978939245  0.0008885694288067102\n",
-            "Total Liabilities             0.03146154060959816    0.01160750456482875   0.0008204142910088841\n",
-            "Depreciation                  0.021280160173773766   0.007851158956977753  0.0005549171204984129\n",
-            "[168 rows x 4 columns]\n",
+            "variable             relative_importance     scaled_importance      percentage\n",
+            "-------------------  ----------------------  ---------------------  ----------------------\n",
+            "text_embeddings_40   0.009123619645833969    1.0                    0.041831677065465825\n",
+            "text_embeddings_101  0.005517772864550352    0.6047789231404315     0.0252989166088107\n",
+            "text_embeddings_50   0.005264927167445421    0.5770656134102976     0.02413962238576451\n",
+            "text_embeddings_0    0.005262942053377628    0.5768480337494993     0.024130520663657985\n",
+            "text_embeddings_62   0.005164535716176033    0.5660621460183586     0.023679328891224535\n",
+            "text_embeddings_17   0.00513411732390523     0.5627281192338582     0.023539860959447705\n",
+            "text_embeddings_104  0.004551626276224852    0.49888382603753295    0.020869147103986106\n",
+            "text_embeddings_96   0.004230691585689783    0.463707579877204      0.019397665734231895\n",
+            "text_embeddings_30   0.003789584618061781    0.41535977662025647    0.01737519604156259\n",
+            "text_embeddings_14   0.003713886020705104    0.4070627848236681     0.017028118960112887\n",
+            "---                  ---                     ---                    ---\n",
+            "text_embeddings_86   0.000215283696888946    0.023596303358311185   0.0009870729420236402\n",
+            "text_embeddings_111  0.00018158181046601385  0.019902387157155088   0.0008325502323899861\n",
+            "text_embeddings_67   0.00017417987692169845  0.019091093632035892   0.0007986124636418964\n",
+            "text_embeddings_117  0.000165262128575705    0.018113658283767566   0.0007577247038007647\n",
+            "text_embeddings_54   0.0001237387623405084   0.013562463928119805   0.0005673406112531368\n",
+            "text_embeddings_37   8.588682976551354e-05   0.009413679339946095   0.00039378999414647247\n",
+            "text_embeddings_31   8.033354970393702e-05   0.008805008628414159   0.00036832827750246124\n",
+            "text_embeddings_59   5.6291384680662304e-05  0.006169852193078446   0.0002580952644825136\n",
+            "text_embeddings_8    4.806226570508443e-05   0.005267894494816061   0.00022036486132209068\n",
+            "text_embeddings_66   1.6554382455069572e-05  0.0018144533746130727  7.590162761715874e-05\n",
+            "[128 rows x 4 columns]\n",
             "\n"
           ]
         }
@@ -2055,15 +2458,15 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "mNLljtXYlB9O",
-        "outputId": "2d2b0812-997b-4efb-a313-da248982bbcd"
+        "outputId": "14c4ccdb-62ab-4d0e-a17b-d5092d1b9426"
       },
-      "execution_count": 33,
+      "execution_count": 26,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "{'creation_epoch': '1744169004', 'start_epoch': '1744169004', 'start_XGBoost_def_2': '1744169005', 'start_GLM_def_1': '1744169009', 'start_GBM_def_5': '1744169009', 'start_XGBoost_def_1': '1744169010', 'start_DRF_def_1': '1744169012', 'start_GBM_def_2': '1744169016', 'start_GBM_def_3': '1744169016', 'start_GBM_def_4': '1744169017', 'start_XGBoost_def_3': '1744169018', 'start_DRF_XRT': '1744169019', 'start_GBM_def_1': '1744169025', 'start_DeepLearning_def_1': '1744169026', 'start_XGBoost_grid_1': '1744169026', 'start_GBM_grid_1': '1744169030', 'start_DeepLearning_grid_1': '1744169032', 'start_DeepLearning_grid_2': '1744169060', 'start_DeepLearning_grid_3': '1744169091', 'stop_epoch': '1744169126', 'duration_secs': '122'}\n"
+            "{'creation_epoch': '1744212096', 'start_epoch': '1744212096', 'start_XGBoost_def_2': '1744212096', 'start_GLM_def_1': '1744212099', 'start_GBM_def_5': '1744212100', 'start_XGBoost_def_1': '1744212104', 'start_DRF_def_1': '1744212106', 'start_GBM_def_2': '1744212110', 'start_GBM_def_3': '1744212112', 'start_GBM_def_4': '1744212113', 'start_XGBoost_def_3': '1744212118', 'start_DRF_XRT': '1744212121', 'start_GBM_def_1': '1744212123', 'start_DeepLearning_def_1': '1744212125', 'start_XGBoost_grid_1': '1744212126', 'start_GBM_grid_1': '1744212139', 'start_DeepLearning_grid_1': '1744212141', 'start_DeepLearning_grid_2': '1744212171', 'start_DeepLearning_grid_3': '1744212204', 'stop_epoch': '1744212242', 'duration_secs': '146'}\n"
           ]
         }
       ]
@@ -2076,25 +2479,25 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 36
+          "height": 35
         },
         "id": "7XJb9DFnlFC_",
-        "outputId": "29b3e8b4-d757-4d49-e39d-62bcc9af5d3d"
+        "outputId": "4b41e87f-ec60-4720-f5ad-5c86bcde4a6e"
       },
-      "execution_count": 34,
+      "execution_count": 27,
       "outputs": [
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "'XRT_1_AutoML_2_20250409_32324'"
+              "'XGBoost_2_AutoML_2_20250409_152135'"
             ],
             "application/vnd.google.colaboratory.intrinsic+json": {
               "type": "string"
             }
           },
           "metadata": {},
-          "execution_count": 34
+          "execution_count": 27
         }
       ]
     },
@@ -2107,60 +2510,60 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 407
+          "height": 406
         },
         "id": "wJRQpJpqlHHp",
-        "outputId": "bba36516-49ae-4924-8306-4adadcf3a06b"
+        "outputId": "49e8229e-b1d8-4d51-9aab-0473cf628c68"
       },
-      "execution_count": 35,
+      "execution_count": 28,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "drf prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n"
+            "xgboost prediction progress: |███████████████████████████████████████████████████| (done) 100%\n"
           ]
         },
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "     predict\n",
-              "------------\n",
-              "-0.00841926\n",
-              " 0.00853072\n",
-              "-0.00422156\n",
-              "-0.0126223\n",
-              " 0.000967221\n",
-              "-0.00610708\n",
-              "-0.000593916\n",
-              "-0.00749558\n",
-              "-0.0024496\n",
-              "-0.0191394\n",
-              "[107 rows x 1 column]\n"
+              "  predict        p0         p1\n",
+              "---------  --------  ---------\n",
+              "        1  0.470249  0.529751\n",
+              "        1  0.26728   0.73272\n",
+              "        1  0.308075  0.691925\n",
+              "        1  0.820202  0.179798\n",
+              "        1  0.815303  0.184697\n",
+              "        1  0.855976  0.144024\n",
+              "        1  0.777378  0.222622\n",
+              "        1  0.932411  0.0675895\n",
+              "        1  0.383299  0.616701\n",
+              "        1  0.541873  0.458127\n",
+              "[107 rows x 3 columns]\n"
             ],
             "text/html": [
               "<table class='dataframe'>\n",
               "<thead>\n",
-              "<tr><th style=\"text-align: right;\">     predict</th></tr>\n",
+              "<tr><th style=\"text-align: right;\">  predict</th><th style=\"text-align: right;\">      p0</th><th style=\"text-align: right;\">       p1</th></tr>\n",
               "</thead>\n",
               "<tbody>\n",
-              "<tr><td style=\"text-align: right;\">-0.00841926 </td></tr>\n",
-              "<tr><td style=\"text-align: right;\"> 0.00853072 </td></tr>\n",
-              "<tr><td style=\"text-align: right;\">-0.00422156 </td></tr>\n",
-              "<tr><td style=\"text-align: right;\">-0.0126223  </td></tr>\n",
-              "<tr><td style=\"text-align: right;\"> 0.000967221</td></tr>\n",
-              "<tr><td style=\"text-align: right;\">-0.00610708 </td></tr>\n",
-              "<tr><td style=\"text-align: right;\">-0.000593916</td></tr>\n",
-              "<tr><td style=\"text-align: right;\">-0.00749558 </td></tr>\n",
-              "<tr><td style=\"text-align: right;\">-0.0024496  </td></tr>\n",
-              "<tr><td style=\"text-align: right;\">-0.0191394  </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.470249</td><td style=\"text-align: right;\">0.529751 </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.26728 </td><td style=\"text-align: right;\">0.73272  </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.308075</td><td style=\"text-align: right;\">0.691925 </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.820202</td><td style=\"text-align: right;\">0.179798 </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.815303</td><td style=\"text-align: right;\">0.184697 </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.855976</td><td style=\"text-align: right;\">0.144024 </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.777378</td><td style=\"text-align: right;\">0.222622 </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.932411</td><td style=\"text-align: right;\">0.0675895</td></tr>\n",
+              "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.383299</td><td style=\"text-align: right;\">0.616701 </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.541873</td><td style=\"text-align: right;\">0.458127 </td></tr>\n",
               "</tbody>\n",
-              "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[107 rows x 1 column]</pre>"
+              "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[107 rows x 3 columns]</pre>"
             ]
           },
           "metadata": {},
-          "execution_count": 35
+          "execution_count": 28
         }
       ]
     },
@@ -2168,25 +2571,31 @@
       "cell_type": "code",
       "source": [
         "print(\"\\n TRAIN SET \\n\")\n",
-        "train_predictions = aml.leader.predict(h2o_train).as_data_frame()['predict'].to_list()\n",
-        "print(calculate_regression_metrics(train[y_col], train_predictions))\n",
+        "train_predictions = aml.leader.predict(h2o_train).as_data_frame()['p1'].to_list()\n",
+        "\n",
         "\n",
         "print(\"\\n VALID SET \\n\")\n",
-        "valid_predictions = aml.leader.predict(h2o_valid).as_data_frame()['predict'].to_list()\n",
-        "print(calculate_regression_metrics(valid[y_col], valid_predictions))\n",
+        "valid_predictions = aml.leader.predict(h2o_valid).as_data_frame()['p1'].to_list()\n",
+        "\n",
         "\n",
         "print(\"\\n TEST SET \\n\")\n",
-        "test_predictions = aml.leader.predict(h2o_test).as_data_frame()['predict'].to_list()\n",
-        "print(calculate_regression_metrics(test[y_col],test_predictions))"
+        "test_predictions = aml.leader.predict(h2o_test).as_data_frame()['p1'].to_list()\n",
+        "\n",
+        "\n",
+        "\n",
+        "text_only_preds = {}\n",
+        "text_only_preds['train_predictions'] = train_predictions\n",
+        "text_only_preds['valid_predictions'] = valid_predictions\n",
+        "text_only_preds['test_predictions'] = test_predictions"
       ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "8C9FuDVSlLJl",
-        "outputId": "9bbd7018-9779-4125-9f3d-9c602b0e38cd"
+        "outputId": "a536cd02-540c-4405-c332-e4bb7a6b30b3"
       },
-      "execution_count": 36,
+      "execution_count": 30,
       "outputs": [
         {
           "output_type": "stream",
@@ -2195,74 +2604,23 @@
             "\n",
             " TRAIN SET \n",
             "\n",
-            "drf prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n",
-            "{'MAE': 0.01216811768416541, 'RMSE': np.float64(0.015817055887501787)}\n",
+            "xgboost prediction progress: |███████████████████████████████████████████████████| (done) 100%\n",
             "\n",
             " VALID SET \n",
             "\n",
-            "drf prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n",
-            "{'MAE': 0.02930384289162232, 'RMSE': np.float64(0.037221544707503766)}\n",
+            "xgboost prediction progress: |███████████████████████████████████████████████████| (done) 100%\n",
             "\n",
             " TEST SET \n",
             "\n",
-            "drf prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n",
-            "{'MAE': 0.03400567480288833, 'RMSE': np.float64(0.046998574922031564)}\n"
+            "xgboost prediction progress: |███████████████████████████████████████████████████| (done) 100%\n"
           ]
         }
       ]
     },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# Using Numeric, Text, and Image Data"
-      ],
-      "metadata": {
-        "id": "IbbSroQQmaX5"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "for col in final_with_funda.columns:\n",
-        "  if 'text_embeddings' in col:\n",
-        "    print(col)\n",
-        "    del final_with_funda[col]\n",
-        "\n",
-        "all_embeddings_128 = pickle.load(open(path + 'getting_all_texts_together_embeddings_dim128_CPU.pkl', 'rb'))\n",
-        "final_with_funda['text_embeddings'] = all_embeddings_128\n",
-        "\n",
-        "for i in range(128):\n",
-        "  final_with_funda['text_embeddings_' + str(i)] = final_with_funda['text_embeddings'].apply(lambda x : x[i])\n",
-        "\n",
-        "text_features = ['text_embeddings_' + str(i) for i in range(128)]"
-      ],
-      "metadata": {
-        "id": "YSTz6UtNp45q"
-      },
-      "execution_count": 11,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "img_emd_df = pd.read_csv(path + \"image_embedding_mean_pooled_128_dim_CPU_df.csv\")\n",
-        "final_with_funda = pd.concat([final_with_funda, img_emd_df] , axis = 1)"
-      ],
-      "metadata": {
-        "id": "oIDoIMmlmeHm"
-      },
-      "execution_count": 13,
-      "outputs": []
-    },
     {
       "cell_type": "code",
       "source": [
-        "image_features = [\"image_embedding_\" + str(i) for i in range(128)]\n",
-        "\n",
-        "train = final_with_funda[final_with_funda['split'] == 'train']\n",
-        "valid = final_with_funda[final_with_funda['split'] == 'validation']\n",
-        "test = final_with_funda[final_with_funda['split'] == 'test']\n",
-        "\n",
+        "y_col = 'RESULT DATE+1 open price' #'TARGET-2 REGRESSION NORMALIZED'\n",
         "x_cols_to_use = ['RESULT DATE open price', 'SMA20', 'SMA50', 'RSI14',\n",
         "                 'gdp_growth', 'inflation', 'NIFTY50_open', 'NIFTY50_close', 'NIFTY50_volume',\n",
         "                 'Sales', 'Expenses', 'Operating Profit', 'OPM %', 'Other Income', 'Interest',\n",
@@ -2273,18 +2631,13 @@
         "                 'Total Assets', 'Cash from Operating Activity',\n",
         "                 'Cash from Investing Activity', 'Cash from Financing Activity',\n",
         "                 'Net Cash Flow', 'Revenue', 'Financing Profit', 'Financing Margin %',\n",
-        "                 'Deposits', 'Borrowing'] + text_features + image_features"
-      ],
-      "metadata": {
-        "id": "LquPqDzGo4ae"
-      },
-      "execution_count": 14,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "y_col =  'TARGET-2 REGRESSION NORMALIZED'\n",
+        "                 'Deposits', 'Borrowing'] + ['text_probab']\n",
+        "\n",
+        "train['text_probab'] = text_only_preds['train_predictions']\n",
+        "valid['text_probab'] = text_only_preds['valid_predictions']\n",
+        "test['text_probab'] = text_only_preds['test_predictions']\n",
+        "\n",
+        "\n",
         "\n",
         "h2o_train = h2o.H2OFrame(train[x_cols_to_use])  # Convert train DataFrame to H2OFrame\n",
         "h2o_valid = h2o.H2OFrame(valid[x_cols_to_use])  # Convert valid DataFrame to H2OFrame\n",
@@ -2304,12 +2657,12 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 829
+          "height": 844
         },
-        "id": "0NrGS04FqMfQ",
-        "outputId": "2002bae8-6be3-488c-ba56-acc23cf5b48a"
+        "id": "sEXXSHnCKY5x",
+        "outputId": "36139365-344a-4b90-dceb-06f70e0bfaea"
       },
-      "execution_count": 15,
+      "execution_count": 33,
       "outputs": [
         {
           "output_type": "stream",
@@ -2328,62 +2681,62 @@
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "model_id                                                  rmse         mse        mae      rmsle    mean_residual_deviance\n",
-              "---------------------------------------------------  ---------  ----------  ---------  ---------  ------------------------\n",
-              "DRF_1_AutoML_1_20250409_35034                        0.0380428  0.00144725  0.0297124  0.0384799                0.00144725\n",
-              "XRT_1_AutoML_1_20250409_35034                        0.038101   0.00145169  0.0292937  0.038581                 0.00145169\n",
-              "GLM_1_AutoML_1_20250409_35034                        0.0385166  0.00148353  0.0305581  0.0388894                0.00148353\n",
-              "GBM_1_AutoML_1_20250409_35034                        0.0385579  0.00148671  0.0301153  0.0389712                0.00148671\n",
-              "GBM_grid_1_AutoML_1_20250409_35034_model_2           0.0386739  0.00149567  0.0302787  0.0391215                0.00149567\n",
-              "GBM_4_AutoML_1_20250409_35034                        0.0394625  0.00155729  0.0303366  0.0399091                0.00155729\n",
-              "XGBoost_grid_1_AutoML_1_20250409_35034_model_2       0.0398563  0.00158852  0.0304308  0.0403216                0.00158852\n",
-              "GBM_5_AutoML_1_20250409_35034                        0.0399377  0.00159502  0.0310639  0.0404214                0.00159502\n",
-              "DeepLearning_grid_3_AutoML_1_20250409_35034_model_1  0.0402288  0.00161836  0.0317706  0.0406846                0.00161836\n",
-              "GBM_grid_1_AutoML_1_20250409_35034_model_1           0.0410106  0.00168187  0.0316574  0.0414953                0.00168187\n",
-              "DeepLearning_grid_2_AutoML_1_20250409_35034_model_1  0.0411344  0.00169204  0.0328997  0.0415181                0.00169204\n",
-              "GBM_2_AutoML_1_20250409_35034                        0.0411842  0.00169614  0.0317147  0.0416665                0.00169614\n",
-              "XGBoost_grid_1_AutoML_1_20250409_35034_model_1       0.0412725  0.00170342  0.0312093  0.0416857                0.00170342\n",
-              "XGBoost_grid_1_AutoML_1_20250409_35034_model_3       0.0414495  0.00171806  0.0314454  0.0418697                0.00171806\n",
-              "DeepLearning_1_AutoML_1_20250409_35034               0.0414925  0.00172163  0.0323868  0.0417972                0.00172163\n",
-              "XGBoost_2_AutoML_1_20250409_35034                    0.0416682  0.00173624  0.0320133  0.0420432                0.00173624\n",
-              "GBM_3_AutoML_1_20250409_35034                        0.0418523  0.00175161  0.0327014  0.0423335                0.00175161\n",
-              "XGBoost_3_AutoML_1_20250409_35034                    0.0426772  0.00182134  0.0330675  0.0430788                0.00182134\n",
-              "XGBoost_1_AutoML_1_20250409_35034                    0.0440645  0.00194168  0.0339823  0.044233                 0.00194168\n",
-              "DeepLearning_grid_1_AutoML_1_20250409_35034_model_1  0.0443104  0.00196341  0.0348976  0.0446149                0.00196341\n",
+              "model_id                                                  rmse               mse        mae      rmsle    mean_residual_deviance\n",
+              "----------------------------------------------------  --------  ----------------  ---------  ---------  ------------------------\n",
+              "DeepLearning_grid_1_AutoML_3_20250409_153059_model_1   115.115   13251.4            78.0504  0.198571            13251.4\n",
+              "DeepLearning_grid_2_AutoML_3_20250409_153059_model_1   219.796   48310.4           135.763   0.274313            48310.4\n",
+              "DeepLearning_grid_3_AutoML_3_20250409_153059_model_1   283.643   80453.3           150.574   0.268955            80453.3\n",
+              "DeepLearning_1_AutoML_3_20250409_153059                327.661  107362             163.088   0.31191            107362\n",
+              "GBM_5_AutoML_3_20250409_153059                         353.027  124628             121.656   0.058028           124628\n",
+              "XGBoost_grid_1_AutoML_3_20250409_153059_model_2        359.132  128976             129.895   0.0806582          128976\n",
+              "GBM_grid_1_AutoML_3_20250409_153059_model_2            364.131  132591             113.84    0.0775484          132591\n",
+              "XGBoost_3_AutoML_3_20250409_153059                     365.803  133812             133.642   0.0807689          133812\n",
+              "XGBoost_2_AutoML_3_20250409_153059                     369.732  136702             132.975   0.0898605          136702\n",
+              "XGBoost_grid_1_AutoML_3_20250409_153059_model_3        372.733  138930             120.947   0.0713517          138930\n",
+              "GBM_2_AutoML_3_20250409_153059                         393.985  155224             123.257   0.0878179          155224\n",
+              "GBM_3_AutoML_3_20250409_153059                         397.278  157830             125.504   0.0984018          157830\n",
+              "GBM_4_AutoML_3_20250409_153059                         397.613  158096             119.102   0.0840173          158096\n",
+              "XGBoost_grid_1_AutoML_3_20250409_153059_model_1        411.003  168924             165.744   0.106208           168924\n",
+              "XGBoost_1_AutoML_3_20250409_153059                     421.675  177810             156.022   0.0850261          177810\n",
+              "GBM_grid_1_AutoML_3_20250409_153059_model_1            431.237  185966             184.649   0.240062           185966\n",
+              "XRT_1_AutoML_3_20250409_153059                         451.33   203698             170.92    0.138523           203698\n",
+              "DRF_1_AutoML_3_20250409_153059                         454.358  206441             163.565   0.0777229          206441\n",
+              "GBM_1_AutoML_3_20250409_153059                        1159.54        1.34453e+06   471.462   0.304123                1.34453e+06\n",
+              "GLM_1_AutoML_3_20250409_153059                        2474.66        6.12395e+06  1514.8     1.18928                 6.12395e+06\n",
               "[20 rows x 6 columns]\n"
             ],
             "text/html": [
               "<table class='dataframe'>\n",
               "<thead>\n",
-              "<tr><th>model_id                                           </th><th style=\"text-align: right;\">     rmse</th><th style=\"text-align: right;\">       mse</th><th style=\"text-align: right;\">      mae</th><th style=\"text-align: right;\">    rmsle</th><th style=\"text-align: right;\">  mean_residual_deviance</th></tr>\n",
+              "<tr><th>model_id                                            </th><th style=\"text-align: right;\">    rmse</th><th style=\"text-align: right;\">             mse</th><th style=\"text-align: right;\">      mae</th><th style=\"text-align: right;\">    rmsle</th><th style=\"text-align: right;\">  mean_residual_deviance</th></tr>\n",
               "</thead>\n",
               "<tbody>\n",
-              "<tr><td>DRF_1_AutoML_1_20250409_35034                      </td><td style=\"text-align: right;\">0.0380428</td><td style=\"text-align: right;\">0.00144725</td><td style=\"text-align: right;\">0.0297124</td><td style=\"text-align: right;\">0.0384799</td><td style=\"text-align: right;\">              0.00144725</td></tr>\n",
-              "<tr><td>XRT_1_AutoML_1_20250409_35034                      </td><td style=\"text-align: right;\">0.038101 </td><td style=\"text-align: right;\">0.00145169</td><td style=\"text-align: right;\">0.0292937</td><td style=\"text-align: right;\">0.038581 </td><td style=\"text-align: right;\">              0.00145169</td></tr>\n",
-              "<tr><td>GLM_1_AutoML_1_20250409_35034                      </td><td style=\"text-align: right;\">0.0385166</td><td style=\"text-align: right;\">0.00148353</td><td style=\"text-align: right;\">0.0305581</td><td style=\"text-align: right;\">0.0388894</td><td style=\"text-align: right;\">              0.00148353</td></tr>\n",
-              "<tr><td>GBM_1_AutoML_1_20250409_35034                      </td><td style=\"text-align: right;\">0.0385579</td><td style=\"text-align: right;\">0.00148671</td><td style=\"text-align: right;\">0.0301153</td><td style=\"text-align: right;\">0.0389712</td><td style=\"text-align: right;\">              0.00148671</td></tr>\n",
-              "<tr><td>GBM_grid_1_AutoML_1_20250409_35034_model_2         </td><td style=\"text-align: right;\">0.0386739</td><td style=\"text-align: right;\">0.00149567</td><td style=\"text-align: right;\">0.0302787</td><td style=\"text-align: right;\">0.0391215</td><td style=\"text-align: right;\">              0.00149567</td></tr>\n",
-              "<tr><td>GBM_4_AutoML_1_20250409_35034                      </td><td style=\"text-align: right;\">0.0394625</td><td style=\"text-align: right;\">0.00155729</td><td style=\"text-align: right;\">0.0303366</td><td style=\"text-align: right;\">0.0399091</td><td style=\"text-align: right;\">              0.00155729</td></tr>\n",
-              "<tr><td>XGBoost_grid_1_AutoML_1_20250409_35034_model_2     </td><td style=\"text-align: right;\">0.0398563</td><td style=\"text-align: right;\">0.00158852</td><td style=\"text-align: right;\">0.0304308</td><td style=\"text-align: right;\">0.0403216</td><td style=\"text-align: right;\">              0.00158852</td></tr>\n",
-              "<tr><td>GBM_5_AutoML_1_20250409_35034                      </td><td style=\"text-align: right;\">0.0399377</td><td style=\"text-align: right;\">0.00159502</td><td style=\"text-align: right;\">0.0310639</td><td style=\"text-align: right;\">0.0404214</td><td style=\"text-align: right;\">              0.00159502</td></tr>\n",
-              "<tr><td>DeepLearning_grid_3_AutoML_1_20250409_35034_model_1</td><td style=\"text-align: right;\">0.0402288</td><td style=\"text-align: right;\">0.00161836</td><td style=\"text-align: right;\">0.0317706</td><td style=\"text-align: right;\">0.0406846</td><td style=\"text-align: right;\">              0.00161836</td></tr>\n",
-              "<tr><td>GBM_grid_1_AutoML_1_20250409_35034_model_1         </td><td style=\"text-align: right;\">0.0410106</td><td style=\"text-align: right;\">0.00168187</td><td style=\"text-align: right;\">0.0316574</td><td style=\"text-align: right;\">0.0414953</td><td style=\"text-align: right;\">              0.00168187</td></tr>\n",
-              "<tr><td>DeepLearning_grid_2_AutoML_1_20250409_35034_model_1</td><td style=\"text-align: right;\">0.0411344</td><td style=\"text-align: right;\">0.00169204</td><td style=\"text-align: right;\">0.0328997</td><td style=\"text-align: right;\">0.0415181</td><td style=\"text-align: right;\">              0.00169204</td></tr>\n",
-              "<tr><td>GBM_2_AutoML_1_20250409_35034                      </td><td style=\"text-align: right;\">0.0411842</td><td style=\"text-align: right;\">0.00169614</td><td style=\"text-align: right;\">0.0317147</td><td style=\"text-align: right;\">0.0416665</td><td style=\"text-align: right;\">              0.00169614</td></tr>\n",
-              "<tr><td>XGBoost_grid_1_AutoML_1_20250409_35034_model_1     </td><td style=\"text-align: right;\">0.0412725</td><td style=\"text-align: right;\">0.00170342</td><td style=\"text-align: right;\">0.0312093</td><td style=\"text-align: right;\">0.0416857</td><td style=\"text-align: right;\">              0.00170342</td></tr>\n",
-              "<tr><td>XGBoost_grid_1_AutoML_1_20250409_35034_model_3     </td><td style=\"text-align: right;\">0.0414495</td><td style=\"text-align: right;\">0.00171806</td><td style=\"text-align: right;\">0.0314454</td><td style=\"text-align: right;\">0.0418697</td><td style=\"text-align: right;\">              0.00171806</td></tr>\n",
-              "<tr><td>DeepLearning_1_AutoML_1_20250409_35034             </td><td style=\"text-align: right;\">0.0414925</td><td style=\"text-align: right;\">0.00172163</td><td style=\"text-align: right;\">0.0323868</td><td style=\"text-align: right;\">0.0417972</td><td style=\"text-align: right;\">              0.00172163</td></tr>\n",
-              "<tr><td>XGBoost_2_AutoML_1_20250409_35034                  </td><td style=\"text-align: right;\">0.0416682</td><td style=\"text-align: right;\">0.00173624</td><td style=\"text-align: right;\">0.0320133</td><td style=\"text-align: right;\">0.0420432</td><td style=\"text-align: right;\">              0.00173624</td></tr>\n",
-              "<tr><td>GBM_3_AutoML_1_20250409_35034                      </td><td style=\"text-align: right;\">0.0418523</td><td style=\"text-align: right;\">0.00175161</td><td style=\"text-align: right;\">0.0327014</td><td style=\"text-align: right;\">0.0423335</td><td style=\"text-align: right;\">              0.00175161</td></tr>\n",
-              "<tr><td>XGBoost_3_AutoML_1_20250409_35034                  </td><td style=\"text-align: right;\">0.0426772</td><td style=\"text-align: right;\">0.00182134</td><td style=\"text-align: right;\">0.0330675</td><td style=\"text-align: right;\">0.0430788</td><td style=\"text-align: right;\">              0.00182134</td></tr>\n",
-              "<tr><td>XGBoost_1_AutoML_1_20250409_35034                  </td><td style=\"text-align: right;\">0.0440645</td><td style=\"text-align: right;\">0.00194168</td><td style=\"text-align: right;\">0.0339823</td><td style=\"text-align: right;\">0.044233 </td><td style=\"text-align: right;\">              0.00194168</td></tr>\n",
-              "<tr><td>DeepLearning_grid_1_AutoML_1_20250409_35034_model_1</td><td style=\"text-align: right;\">0.0443104</td><td style=\"text-align: right;\">0.00196341</td><td style=\"text-align: right;\">0.0348976</td><td style=\"text-align: right;\">0.0446149</td><td style=\"text-align: right;\">              0.00196341</td></tr>\n",
+              "<tr><td>DeepLearning_grid_1_AutoML_3_20250409_153059_model_1</td><td style=\"text-align: right;\"> 115.115</td><td style=\"text-align: right;\"> 13251.4        </td><td style=\"text-align: right;\">  78.0504</td><td style=\"text-align: right;\">0.198571 </td><td style=\"text-align: right;\">         13251.4        </td></tr>\n",
+              "<tr><td>DeepLearning_grid_2_AutoML_3_20250409_153059_model_1</td><td style=\"text-align: right;\"> 219.796</td><td style=\"text-align: right;\"> 48310.4        </td><td style=\"text-align: right;\"> 135.763 </td><td style=\"text-align: right;\">0.274313 </td><td style=\"text-align: right;\">         48310.4        </td></tr>\n",
+              "<tr><td>DeepLearning_grid_3_AutoML_3_20250409_153059_model_1</td><td style=\"text-align: right;\"> 283.643</td><td style=\"text-align: right;\"> 80453.3        </td><td style=\"text-align: right;\"> 150.574 </td><td style=\"text-align: right;\">0.268955 </td><td style=\"text-align: right;\">         80453.3        </td></tr>\n",
+              "<tr><td>DeepLearning_1_AutoML_3_20250409_153059             </td><td style=\"text-align: right;\"> 327.661</td><td style=\"text-align: right;\">107362          </td><td style=\"text-align: right;\"> 163.088 </td><td style=\"text-align: right;\">0.31191  </td><td style=\"text-align: right;\">        107362          </td></tr>\n",
+              "<tr><td>GBM_5_AutoML_3_20250409_153059                      </td><td style=\"text-align: right;\"> 353.027</td><td style=\"text-align: right;\">124628          </td><td style=\"text-align: right;\"> 121.656 </td><td style=\"text-align: right;\">0.058028 </td><td style=\"text-align: right;\">        124628          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_3_20250409_153059_model_2     </td><td style=\"text-align: right;\"> 359.132</td><td style=\"text-align: right;\">128976          </td><td style=\"text-align: right;\"> 129.895 </td><td style=\"text-align: right;\">0.0806582</td><td style=\"text-align: right;\">        128976          </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_3_20250409_153059_model_2         </td><td style=\"text-align: right;\"> 364.131</td><td style=\"text-align: right;\">132591          </td><td style=\"text-align: right;\"> 113.84  </td><td style=\"text-align: right;\">0.0775484</td><td style=\"text-align: right;\">        132591          </td></tr>\n",
+              "<tr><td>XGBoost_3_AutoML_3_20250409_153059                  </td><td style=\"text-align: right;\"> 365.803</td><td style=\"text-align: right;\">133812          </td><td style=\"text-align: right;\"> 133.642 </td><td style=\"text-align: right;\">0.0807689</td><td style=\"text-align: right;\">        133812          </td></tr>\n",
+              "<tr><td>XGBoost_2_AutoML_3_20250409_153059                  </td><td style=\"text-align: right;\"> 369.732</td><td style=\"text-align: right;\">136702          </td><td style=\"text-align: right;\"> 132.975 </td><td style=\"text-align: right;\">0.0898605</td><td style=\"text-align: right;\">        136702          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_3_20250409_153059_model_3     </td><td style=\"text-align: right;\"> 372.733</td><td style=\"text-align: right;\">138930          </td><td style=\"text-align: right;\"> 120.947 </td><td style=\"text-align: right;\">0.0713517</td><td style=\"text-align: right;\">        138930          </td></tr>\n",
+              "<tr><td>GBM_2_AutoML_3_20250409_153059                      </td><td style=\"text-align: right;\"> 393.985</td><td style=\"text-align: right;\">155224          </td><td style=\"text-align: right;\"> 123.257 </td><td style=\"text-align: right;\">0.0878179</td><td style=\"text-align: right;\">        155224          </td></tr>\n",
+              "<tr><td>GBM_3_AutoML_3_20250409_153059                      </td><td style=\"text-align: right;\"> 397.278</td><td style=\"text-align: right;\">157830          </td><td style=\"text-align: right;\"> 125.504 </td><td style=\"text-align: right;\">0.0984018</td><td style=\"text-align: right;\">        157830          </td></tr>\n",
+              "<tr><td>GBM_4_AutoML_3_20250409_153059                      </td><td style=\"text-align: right;\"> 397.613</td><td style=\"text-align: right;\">158096          </td><td style=\"text-align: right;\"> 119.102 </td><td style=\"text-align: right;\">0.0840173</td><td style=\"text-align: right;\">        158096          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_3_20250409_153059_model_1     </td><td style=\"text-align: right;\"> 411.003</td><td style=\"text-align: right;\">168924          </td><td style=\"text-align: right;\"> 165.744 </td><td style=\"text-align: right;\">0.106208 </td><td style=\"text-align: right;\">        168924          </td></tr>\n",
+              "<tr><td>XGBoost_1_AutoML_3_20250409_153059                  </td><td style=\"text-align: right;\"> 421.675</td><td style=\"text-align: right;\">177810          </td><td style=\"text-align: right;\"> 156.022 </td><td style=\"text-align: right;\">0.0850261</td><td style=\"text-align: right;\">        177810          </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_3_20250409_153059_model_1         </td><td style=\"text-align: right;\"> 431.237</td><td style=\"text-align: right;\">185966          </td><td style=\"text-align: right;\"> 184.649 </td><td style=\"text-align: right;\">0.240062 </td><td style=\"text-align: right;\">        185966          </td></tr>\n",
+              "<tr><td>XRT_1_AutoML_3_20250409_153059                      </td><td style=\"text-align: right;\"> 451.33 </td><td style=\"text-align: right;\">203698          </td><td style=\"text-align: right;\"> 170.92  </td><td style=\"text-align: right;\">0.138523 </td><td style=\"text-align: right;\">        203698          </td></tr>\n",
+              "<tr><td>DRF_1_AutoML_3_20250409_153059                      </td><td style=\"text-align: right;\"> 454.358</td><td style=\"text-align: right;\">206441          </td><td style=\"text-align: right;\"> 163.565 </td><td style=\"text-align: right;\">0.0777229</td><td style=\"text-align: right;\">        206441          </td></tr>\n",
+              "<tr><td>GBM_1_AutoML_3_20250409_153059                      </td><td style=\"text-align: right;\">1159.54 </td><td style=\"text-align: right;\">     1.34453e+06</td><td style=\"text-align: right;\"> 471.462 </td><td style=\"text-align: right;\">0.304123 </td><td style=\"text-align: right;\">             1.34453e+06</td></tr>\n",
+              "<tr><td>GLM_1_AutoML_3_20250409_153059                      </td><td style=\"text-align: right;\">2474.66 </td><td style=\"text-align: right;\">     6.12395e+06</td><td style=\"text-align: right;\">1514.8   </td><td style=\"text-align: right;\">1.18928  </td><td style=\"text-align: right;\">             6.12395e+06</td></tr>\n",
               "</tbody>\n",
               "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[20 rows x 6 columns]</pre>"
             ]
           },
           "metadata": {},
-          "execution_count": 15
+          "execution_count": 33
         }
       ]
     },
@@ -2398,51 +2751,51 @@
           "base_uri": "https://localhost:8080/",
           "height": 409
         },
-        "id": "INdYsJVVq9lX",
-        "outputId": "88b9732c-9062-4ca6-cdad-67ad154bf579"
+        "id": "_DWB7qlfLUne",
+        "outputId": "83d3ea0b-20fd-46ba-906d-ddcfbb57ebcf"
       },
-      "execution_count": 16,
+      "execution_count": 34,
       "outputs": [
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "model_id                                                  rmse         mse        mae      rmsle    mean_residual_deviance    training_time_ms    predict_time_per_row_ms  algo\n",
-              "---------------------------------------------------  ---------  ----------  ---------  ---------  ------------------------  ------------------  -------------------------  ------------\n",
-              "DRF_1_AutoML_1_20250409_35034                        0.0380428  0.00144725  0.0297124  0.0384799                0.00144725               24440                   0.266393  DRF\n",
-              "XRT_1_AutoML_1_20250409_35034                        0.038101   0.00145169  0.0292937  0.038581                 0.00145169               25852                   0.263379  DRF\n",
-              "GLM_1_AutoML_1_20250409_35034                        0.0385166  0.00148353  0.0305581  0.0388894                0.00148353                1524                   0.13218   GLM\n",
-              "GBM_1_AutoML_1_20250409_35034                        0.0385579  0.00148671  0.0301153  0.0389712                0.00148671                4126                   0.146618  GBM\n",
-              "GBM_grid_1_AutoML_1_20250409_35034_model_2           0.0386739  0.00149567  0.0302787  0.0391215                0.00149567                3576                   0.113431  GBM\n",
-              "GBM_4_AutoML_1_20250409_35034                        0.0394625  0.00155729  0.0303366  0.0399091                0.00155729                2809                   0.118063  GBM\n",
-              "XGBoost_grid_1_AutoML_1_20250409_35034_model_2       0.0398563  0.00158852  0.0304308  0.0403216                0.00158852                7630                   0.400345  XGBoost\n",
-              "GBM_5_AutoML_1_20250409_35034                        0.0399377  0.00159502  0.0310639  0.0404214                0.00159502                5179                   0.134716  GBM\n",
-              "DeepLearning_grid_3_AutoML_1_20250409_35034_model_1  0.0402288  0.00161836  0.0317706  0.0406846                0.00161836               30123                   0.415069  DeepLearning\n",
-              "GBM_grid_1_AutoML_1_20250409_35034_model_1           0.0410106  0.00168187  0.0316574  0.0414953                0.00168187                2123                   0.125124  GBM\n",
+              "model_id                                                 rmse       mse       mae      rmsle    mean_residual_deviance    training_time_ms    predict_time_per_row_ms  algo\n",
+              "----------------------------------------------------  -------  --------  --------  ---------  ------------------------  ------------------  -------------------------  ------------\n",
+              "DeepLearning_grid_1_AutoML_3_20250409_153059_model_1  115.115   13251.4   78.0504  0.198571                    13251.4               81106                   0.062635  DeepLearning\n",
+              "DeepLearning_grid_2_AutoML_3_20250409_153059_model_1  219.796   48310.4  135.763   0.274313                    48310.4               58250                   0.09149   DeepLearning\n",
+              "DeepLearning_grid_3_AutoML_3_20250409_153059_model_1  283.643   80453.3  150.574   0.268955                    80453.3               40590                   0.173145  DeepLearning\n",
+              "DeepLearning_1_AutoML_3_20250409_153059               327.661  107362    163.088   0.31191                    107362                   483                   0.025744  DeepLearning\n",
+              "GBM_5_AutoML_3_20250409_153059                        353.027  124628    121.656   0.058028                   124628                  1445                   0.047336  GBM\n",
+              "XGBoost_grid_1_AutoML_3_20250409_153059_model_2       359.132  128976    129.895   0.0806582                  128976                  4941                   0.084215  XGBoost\n",
+              "GBM_grid_1_AutoML_3_20250409_153059_model_2           364.131  132591    113.84    0.0775484                  132591                  1040                   0.045228  GBM\n",
+              "XGBoost_3_AutoML_3_20250409_153059                    365.803  133812    133.642   0.0807689                  133812                   610                   0.032342  XGBoost\n",
+              "XGBoost_2_AutoML_3_20250409_153059                    369.732  136702    132.975   0.0898605                  136702                  2056                   0.040969  XGBoost\n",
+              "XGBoost_grid_1_AutoML_3_20250409_153059_model_3       372.733  138930    120.947   0.0713517                  138930                  1874                   0.032302  XGBoost\n",
               "[20 rows x 9 columns]\n"
             ],
             "text/html": [
               "<table class='dataframe'>\n",
               "<thead>\n",
-              "<tr><th>model_id                                           </th><th style=\"text-align: right;\">     rmse</th><th style=\"text-align: right;\">       mse</th><th style=\"text-align: right;\">      mae</th><th style=\"text-align: right;\">    rmsle</th><th style=\"text-align: right;\">  mean_residual_deviance</th><th style=\"text-align: right;\">  training_time_ms</th><th style=\"text-align: right;\">  predict_time_per_row_ms</th><th>algo        </th></tr>\n",
+              "<tr><th>model_id                                            </th><th style=\"text-align: right;\">   rmse</th><th style=\"text-align: right;\">     mse</th><th style=\"text-align: right;\">     mae</th><th style=\"text-align: right;\">    rmsle</th><th style=\"text-align: right;\">  mean_residual_deviance</th><th style=\"text-align: right;\">  training_time_ms</th><th style=\"text-align: right;\">  predict_time_per_row_ms</th><th>algo        </th></tr>\n",
               "</thead>\n",
               "<tbody>\n",
-              "<tr><td>DRF_1_AutoML_1_20250409_35034                      </td><td style=\"text-align: right;\">0.0380428</td><td style=\"text-align: right;\">0.00144725</td><td style=\"text-align: right;\">0.0297124</td><td style=\"text-align: right;\">0.0384799</td><td style=\"text-align: right;\">              0.00144725</td><td style=\"text-align: right;\">             24440</td><td style=\"text-align: right;\">                 0.266393</td><td>DRF         </td></tr>\n",
-              "<tr><td>XRT_1_AutoML_1_20250409_35034                      </td><td style=\"text-align: right;\">0.038101 </td><td style=\"text-align: right;\">0.00145169</td><td style=\"text-align: right;\">0.0292937</td><td style=\"text-align: right;\">0.038581 </td><td style=\"text-align: right;\">              0.00145169</td><td style=\"text-align: right;\">             25852</td><td style=\"text-align: right;\">                 0.263379</td><td>DRF         </td></tr>\n",
-              "<tr><td>GLM_1_AutoML_1_20250409_35034                      </td><td style=\"text-align: right;\">0.0385166</td><td style=\"text-align: right;\">0.00148353</td><td style=\"text-align: right;\">0.0305581</td><td style=\"text-align: right;\">0.0388894</td><td style=\"text-align: right;\">              0.00148353</td><td style=\"text-align: right;\">              1524</td><td style=\"text-align: right;\">                 0.13218 </td><td>GLM         </td></tr>\n",
-              "<tr><td>GBM_1_AutoML_1_20250409_35034                      </td><td style=\"text-align: right;\">0.0385579</td><td style=\"text-align: right;\">0.00148671</td><td style=\"text-align: right;\">0.0301153</td><td style=\"text-align: right;\">0.0389712</td><td style=\"text-align: right;\">              0.00148671</td><td style=\"text-align: right;\">              4126</td><td style=\"text-align: right;\">                 0.146618</td><td>GBM         </td></tr>\n",
-              "<tr><td>GBM_grid_1_AutoML_1_20250409_35034_model_2         </td><td style=\"text-align: right;\">0.0386739</td><td style=\"text-align: right;\">0.00149567</td><td style=\"text-align: right;\">0.0302787</td><td style=\"text-align: right;\">0.0391215</td><td style=\"text-align: right;\">              0.00149567</td><td style=\"text-align: right;\">              3576</td><td style=\"text-align: right;\">                 0.113431</td><td>GBM         </td></tr>\n",
-              "<tr><td>GBM_4_AutoML_1_20250409_35034                      </td><td style=\"text-align: right;\">0.0394625</td><td style=\"text-align: right;\">0.00155729</td><td style=\"text-align: right;\">0.0303366</td><td style=\"text-align: right;\">0.0399091</td><td style=\"text-align: right;\">              0.00155729</td><td style=\"text-align: right;\">              2809</td><td style=\"text-align: right;\">                 0.118063</td><td>GBM         </td></tr>\n",
-              "<tr><td>XGBoost_grid_1_AutoML_1_20250409_35034_model_2     </td><td style=\"text-align: right;\">0.0398563</td><td style=\"text-align: right;\">0.00158852</td><td style=\"text-align: right;\">0.0304308</td><td style=\"text-align: right;\">0.0403216</td><td style=\"text-align: right;\">              0.00158852</td><td style=\"text-align: right;\">              7630</td><td style=\"text-align: right;\">                 0.400345</td><td>XGBoost     </td></tr>\n",
-              "<tr><td>GBM_5_AutoML_1_20250409_35034                      </td><td style=\"text-align: right;\">0.0399377</td><td style=\"text-align: right;\">0.00159502</td><td style=\"text-align: right;\">0.0310639</td><td style=\"text-align: right;\">0.0404214</td><td style=\"text-align: right;\">              0.00159502</td><td style=\"text-align: right;\">              5179</td><td style=\"text-align: right;\">                 0.134716</td><td>GBM         </td></tr>\n",
-              "<tr><td>DeepLearning_grid_3_AutoML_1_20250409_35034_model_1</td><td style=\"text-align: right;\">0.0402288</td><td style=\"text-align: right;\">0.00161836</td><td style=\"text-align: right;\">0.0317706</td><td style=\"text-align: right;\">0.0406846</td><td style=\"text-align: right;\">              0.00161836</td><td style=\"text-align: right;\">             30123</td><td style=\"text-align: right;\">                 0.415069</td><td>DeepLearning</td></tr>\n",
-              "<tr><td>GBM_grid_1_AutoML_1_20250409_35034_model_1         </td><td style=\"text-align: right;\">0.0410106</td><td style=\"text-align: right;\">0.00168187</td><td style=\"text-align: right;\">0.0316574</td><td style=\"text-align: right;\">0.0414953</td><td style=\"text-align: right;\">              0.00168187</td><td style=\"text-align: right;\">              2123</td><td style=\"text-align: right;\">                 0.125124</td><td>GBM         </td></tr>\n",
+              "<tr><td>DeepLearning_grid_1_AutoML_3_20250409_153059_model_1</td><td style=\"text-align: right;\">115.115</td><td style=\"text-align: right;\"> 13251.4</td><td style=\"text-align: right;\"> 78.0504</td><td style=\"text-align: right;\">0.198571 </td><td style=\"text-align: right;\">                 13251.4</td><td style=\"text-align: right;\">             81106</td><td style=\"text-align: right;\">                 0.062635</td><td>DeepLearning</td></tr>\n",
+              "<tr><td>DeepLearning_grid_2_AutoML_3_20250409_153059_model_1</td><td style=\"text-align: right;\">219.796</td><td style=\"text-align: right;\"> 48310.4</td><td style=\"text-align: right;\">135.763 </td><td style=\"text-align: right;\">0.274313 </td><td style=\"text-align: right;\">                 48310.4</td><td style=\"text-align: right;\">             58250</td><td style=\"text-align: right;\">                 0.09149 </td><td>DeepLearning</td></tr>\n",
+              "<tr><td>DeepLearning_grid_3_AutoML_3_20250409_153059_model_1</td><td style=\"text-align: right;\">283.643</td><td style=\"text-align: right;\"> 80453.3</td><td style=\"text-align: right;\">150.574 </td><td style=\"text-align: right;\">0.268955 </td><td style=\"text-align: right;\">                 80453.3</td><td style=\"text-align: right;\">             40590</td><td style=\"text-align: right;\">                 0.173145</td><td>DeepLearning</td></tr>\n",
+              "<tr><td>DeepLearning_1_AutoML_3_20250409_153059             </td><td style=\"text-align: right;\">327.661</td><td style=\"text-align: right;\">107362  </td><td style=\"text-align: right;\">163.088 </td><td style=\"text-align: right;\">0.31191  </td><td style=\"text-align: right;\">                107362  </td><td style=\"text-align: right;\">               483</td><td style=\"text-align: right;\">                 0.025744</td><td>DeepLearning</td></tr>\n",
+              "<tr><td>GBM_5_AutoML_3_20250409_153059                      </td><td style=\"text-align: right;\">353.027</td><td style=\"text-align: right;\">124628  </td><td style=\"text-align: right;\">121.656 </td><td style=\"text-align: right;\">0.058028 </td><td style=\"text-align: right;\">                124628  </td><td style=\"text-align: right;\">              1445</td><td style=\"text-align: right;\">                 0.047336</td><td>GBM         </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_3_20250409_153059_model_2     </td><td style=\"text-align: right;\">359.132</td><td style=\"text-align: right;\">128976  </td><td style=\"text-align: right;\">129.895 </td><td style=\"text-align: right;\">0.0806582</td><td style=\"text-align: right;\">                128976  </td><td style=\"text-align: right;\">              4941</td><td style=\"text-align: right;\">                 0.084215</td><td>XGBoost     </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_3_20250409_153059_model_2         </td><td style=\"text-align: right;\">364.131</td><td style=\"text-align: right;\">132591  </td><td style=\"text-align: right;\">113.84  </td><td style=\"text-align: right;\">0.0775484</td><td style=\"text-align: right;\">                132591  </td><td style=\"text-align: right;\">              1040</td><td style=\"text-align: right;\">                 0.045228</td><td>GBM         </td></tr>\n",
+              "<tr><td>XGBoost_3_AutoML_3_20250409_153059                  </td><td style=\"text-align: right;\">365.803</td><td style=\"text-align: right;\">133812  </td><td style=\"text-align: right;\">133.642 </td><td style=\"text-align: right;\">0.0807689</td><td style=\"text-align: right;\">                133812  </td><td style=\"text-align: right;\">               610</td><td style=\"text-align: right;\">                 0.032342</td><td>XGBoost     </td></tr>\n",
+              "<tr><td>XGBoost_2_AutoML_3_20250409_153059                  </td><td style=\"text-align: right;\">369.732</td><td style=\"text-align: right;\">136702  </td><td style=\"text-align: right;\">132.975 </td><td style=\"text-align: right;\">0.0898605</td><td style=\"text-align: right;\">                136702  </td><td style=\"text-align: right;\">              2056</td><td style=\"text-align: right;\">                 0.040969</td><td>XGBoost     </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_3_20250409_153059_model_3     </td><td style=\"text-align: right;\">372.733</td><td style=\"text-align: right;\">138930  </td><td style=\"text-align: right;\">120.947 </td><td style=\"text-align: right;\">0.0713517</td><td style=\"text-align: right;\">                138930  </td><td style=\"text-align: right;\">              1874</td><td style=\"text-align: right;\">                 0.032302</td><td>XGBoost     </td></tr>\n",
               "</tbody>\n",
               "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[20 rows x 9 columns]</pre>"
             ]
           },
           "metadata": {},
-          "execution_count": 16
+          "execution_count": 34
         }
       ]
     },
@@ -2451,25 +2804,16 @@
       "source": [
         "# Get the best model using the metric\n",
         "m = aml.leader\n",
-        "print(m)\n",
-        "# this is equivalent to\n",
-        "m = aml.get_best_model()\n",
-        "print(m)\n",
-        "# Get the best model using a non-default metric\n",
-        "m = aml.get_best_model(criterion=\"RMSE\")\n",
-        "print(m)\n",
-        "# Get training timing info\n",
-        "info = aml.training_info\n",
-        "print(info)\n"
+        "print(m)"
       ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "id": "HZ4-ceidq-_w",
-        "outputId": "3fa8fffc-00b9-4450-94f4-823cec6d3d94"
+        "id": "zJX3XjFHLV1s",
+        "outputId": "fd5f6a1a-77cb-42f5-e1e1-e38593af241d"
       },
-      "execution_count": 17,
+      "execution_count": 35,
       "outputs": [
         {
           "output_type": "stream",
@@ -2477,212 +2821,314 @@
           "text": [
             "Model Details\n",
             "=============\n",
-            "H2ORandomForestEstimator : Distributed Random Forest\n",
-            "Model Key: DRF_1_AutoML_1_20250409_35034\n",
+            "H2ODeepLearningEstimator : Deep Learning\n",
+            "Model Key: DeepLearning_grid_1_AutoML_3_20250409_153059_model_1\n",
             "\n",
             "\n",
-            "Model Summary: \n",
-            "    number_of_trees    number_of_internal_trees    model_size_in_bytes    min_depth    max_depth    mean_depth    min_leaves    max_leaves    mean_leaves\n",
-            "--  -----------------  --------------------------  ---------------------  -----------  -----------  ------------  ------------  ------------  -------------\n",
-            "    45                 45                          252849                 19           20           19.9778       303           521           441.089\n",
+            "Status of Neuron Layers: predicting RESULT DATE+1 open price, regression, gaussian distribution, Quadratic loss, 28,201 weights/biases, 346.8 KB, 858,931 training samples, mini-batch size 1\n",
+            "    layer    units    type              dropout    l1    l2    mean_rate              rate_rms               momentum    mean_weight             weight_rms           mean_bias             bias_rms\n",
+            "--  -------  -------  ----------------  ---------  ----  ----  ---------------------  ---------------------  ----------  ----------------------  -------------------  --------------------  -----------------------\n",
+            "    1        280      Input             15.0\n",
+            "    2        100      RectifierDropout  10.0       0.0   0.0   0.04534329561915365    0.1347193717956543     0.0         -0.0048023015300245305  0.07585853338241577  0.06739497309629429   0.1536393165588379\n",
+            "    3        1        Linear                       0.0   0.0   0.0021420995789230802  0.0016298778355121613  0.0         0.018227952951565384    0.10617497563362122  -0.23974569455258748  1.0971281125650402e-154\n",
             "\n",
-            "ModelMetricsRegression: drf\n",
+            "ModelMetricsRegression: deeplearning\n",
             "** Reported on train data. **\n",
             "\n",
-            "MSE: 0.0014267131828796813\n",
-            "RMSE: 0.03777185702185797\n",
-            "MAE: 0.028688241892682823\n",
-            "RMSLE: 0.03758799502993611\n",
-            "Mean Residual Deviance: 0.0014267131828796813\n",
+            "MSE: 9173.741318110073\n",
+            "RMSE: 95.77964981200377\n",
+            "MAE: 72.66573385585798\n",
+            "RMSLE: NaN\n",
+            "Mean Residual Deviance: 9173.741318110073\n",
             "\n",
-            "ModelMetricsRegression: drf\n",
+            "ModelMetricsRegression: deeplearning\n",
             "** Reported on validation data. **\n",
             "\n",
-            "MSE: 0.001447253170302201\n",
-            "RMSE: 0.03804278079087018\n",
-            "MAE: 0.029712366126973765\n",
-            "RMSLE: 0.03847986738748068\n",
-            "Mean Residual Deviance: 0.001447253170302201\n",
+            "MSE: 13251.392204832708\n",
+            "RMSE: 115.1146915247255\n",
+            "MAE: 78.05043438612668\n",
+            "RMSLE: 0.19857098335218032\n",
+            "Mean Residual Deviance: 13251.392204832708\n",
             "\n",
             "Scoring History: \n",
-            "    timestamp            duration    number_of_trees    training_rmse    training_mae    training_deviance    validation_rmse    validation_mae    validation_deviance\n",
-            "--  -------------------  ----------  -----------------  ---------------  --------------  -------------------  -----------------  ----------------  ---------------------\n",
-            "    2025-04-09 03:51:02  0.016 sec   0                  nan              nan             nan                  nan                nan               nan\n",
-            "    2025-04-09 03:51:07  4.841 sec   5                  0.0475896        0.0363132       0.00226477           0.0400197          0.0319293         0.00160158\n",
-            "    2025-04-09 03:51:09  7.150 sec   10                 0.0439589        0.0329243       0.00193238           0.0385291          0.0303373         0.00148449\n",
-            "    2025-04-09 03:51:12  9.813 sec   15                 0.0406966        0.0309486       0.00165621           0.0396747          0.0313307         0.00157408\n",
-            "    2025-04-09 03:51:15  12.842 sec  20                 0.0396125        0.0300239       0.00156915           0.0387996          0.0303913         0.00150541\n",
-            "    2025-04-09 03:51:17  15.506 sec  25                 0.0390685        0.0295927       0.00152635           0.0383261          0.0297908         0.00146889\n",
-            "    2025-04-09 03:51:21  18.979 sec  30                 0.038768         0.0291746       0.00150296           0.0382451          0.0298145         0.00146269\n",
-            "    2025-04-09 03:51:23  20.999 sec  35                 0.0382724        0.0288737       0.00146477           0.0383061          0.0298538         0.00146736\n",
-            "    2025-04-09 03:51:24  22.270 sec  40                 0.0381419        0.0289535       0.0014548            0.0381015          0.0297459         0.00145172\n",
-            "    2025-04-09 03:51:26  24.409 sec  45                 0.0377719        0.0286882       0.00142671           0.0380428          0.0297124         0.00144725\n",
+            "    timestamp            duration          training_speed    epochs    iterations    samples    training_rmse    training_deviance    training_mae    training_r2    validation_rmse    validation_deviance    validation_mae    validation_r2\n",
+            "--  -------------------  ----------------  ----------------  --------  ------------  ---------  ---------------  -------------------  --------------  -------------  -----------------  ---------------------  ----------------  ---------------\n",
+            "    2025-04-09 15:31:45  0.000 sec                           0         0             0          nan              nan                  nan             nan            nan                nan                    nan               nan\n",
+            "    2025-04-09 15:31:46  1.225 sec         7777 obs/sec      9.62861   1             8011       268.819          72263.4              208.437         0.972513       362.259            131232                 258.831           0.97669\n",
+            "    2025-04-09 15:31:51  6.267 sec         9403 obs/sec      67.5517   7             56203      243.589          59335.6              158.98          0.97743        220.515            48627                  146.444           0.991362\n",
+            "    2025-04-09 15:31:56  11.598 sec        7836 obs/sec      106.173   11            88336      217.947          47501.1              141.468         0.981932       214.145            45857.9                141.017           0.991854\n",
+            "    2025-04-09 15:32:02  17.166 sec        9565 obs/sec      193.077   20            160640     236.658          56007                138.355         0.978696       245.581            60310.2                179.234           0.989287\n",
+            "    2025-04-09 15:32:07  22.589 sec        9805 obs/sec      260.573   27            216797     250.98           62990.8              136.343         0.97604        205.631            42284.3                149.644           0.992489\n",
+            "    2025-04-09 15:32:13  28.042 sec        9334 obs/sec      308.879   32            256987     269.767          72774.3              124.139         0.972319       200.255            40102                  151.834           0.992877\n",
+            "    2025-04-09 15:32:18  33.512 sec        9996 obs/sec      395.794   41            329301     206.496          42640.7              117.108         0.983781       192.205            36942.6                138.162           0.993438\n",
+            "    2025-04-09 15:32:23  38.556 sec        9740 obs/sec      444.06    46            369458     127.498          16255.7              91.034          0.993817       148.004            21905.1                112.624           0.996109\n",
+            "    2025-04-09 15:32:29  44.001 sec        9637 obs/sec      502.006   52            417669     151.472          22943.7              117.446         0.991273       168.935            28539.1                126.815           0.994931\n",
+            "    2025-04-09 15:32:34  49.136 sec        9615 obs/sec      559.846   58            465792     133.393          17793.8              101.435         0.993232       129.569            16788.1                96.4624           0.997018\n",
+            "    2025-04-09 15:32:39  54.236 sec        9607 obs/sec      617.743   64            513962     119.925          14381.9              92.0844         0.994529       119.316            14236.4                88.808            0.997471\n",
+            "    2025-04-09 15:32:45  59.627 sec        9687 obs/sec      685.221   71            570104     95.7796          9173.74              72.6657         0.996511       115.115            13251.4                78.0504           0.997646\n",
+            "    2025-04-09 15:32:50  1 min  5.233 sec  10098 obs/sec     781.655   81            650337     104.341          10887                74.5243         0.995859       115.824            13415.1                90.5743           0.997617\n",
+            "    2025-04-09 15:32:56  1 min 10.724 sec  10113 obs/sec     849.118   88            706466     89.6967          8045.5               63.0588         0.99694        134.554            18104.8                98.6566           0.996784\n",
+            "    2025-04-09 15:33:01  1 min 15.901 sec  10489 obs/sec     945.573   98            786717     84.7465          7181.96              58.7717         0.997268       156.665            24544                  122.798           0.99564\n",
+            "    2025-04-09 15:33:06  1 min 21.118 sec  10715 obs/sec     1032.37   107           858931     104.103          10837.4              63.9932         0.995878       126.884            16099.5                89.5696           0.99714\n",
+            "    2025-04-09 15:33:06  1 min 21.185 sec  10713 obs/sec     1032.37   107           858931     95.7796          9173.74              72.6657         0.996511       115.115            13251.4                78.0504           0.997646\n",
             "\n",
             "Variable Importances: \n",
-            "variable             relative_importance    scaled_importance      percentage\n",
-            "-------------------  ---------------------  ---------------------  ----------------------\n",
-            "RSI14                1.701903223991394      1.0                    0.0494276988048072\n",
-            "Financing Profit     1.6198419332504272     0.9517826339452414     0.04704442535829146\n",
-            "Revenue              1.4415013790130615     0.8469937412965091     0.04186495153436064\n",
-            "Borrowing            0.8915032148361206     0.5238272084268809     0.025891573483886834\n",
-            "image_embedding_37   0.3991141617298126     0.2345104916093812     0.01159131394583576\n",
-            "image_embedding_93   0.3840157985687256     0.22563903349810413    0.011152818186352094\n",
-            "image_embedding_50   0.3491175174713135     0.2051335895895094     0.010139281280979203\n",
-            "text_embeddings_118  0.31623440980911255    0.18581221620079125    0.009184270256626426\n",
-            "image_embedding_25   0.2951158285140991     0.1734034135160622     0.008570931694997355\n",
-            "Financing Margin %   0.2903645932674408     0.1706116947040398     0.008432943458408998\n",
-            "---                  ---                    ---                    ---\n",
-            "image_embedding_121  0.022208569571375847   0.013049255244543886   0.0006449946578543659\n",
-            "Sales                0.021111510694026947   0.012404648158850718   0.0006131332129752794\n",
-            "Other Income         0.021029533818364143   0.012356480393194485   0.0006107523911623227\n",
-            "CWIP                 0.017842406406998634   0.010483796114536802   0.000518189916680333\n",
-            "Other Liabilities    0.017541129142045975   0.010306772379752348   0.000509440040836105\n",
-            "Total Assets         0.01722622476518154    0.010121741660951603   0.0005002943981975848\n",
-            "Other Assets         0.01622455008327961    0.00953318018002747    0.0004712031585903555\n",
-            "Depreciation         0.015722332522273064   0.00923808845334943    0.00045661745360432276\n",
-            "Fixed Assets         0.004519629757851362   0.0026556326435833912  0.00013126181044325377\n",
-            "Equity Capital       0.0033692489378154278  0.0019796947854142295  9.785175757890195e-05\n",
-            "[296 rows x 4 columns]\n",
-            "\n",
+            "variable                        relative_importance    scaled_importance    percentage\n",
+            "------------------------------  ---------------------  -------------------  ---------------------\n",
+            "RESULT DATE open price          1.0                    1.0                  0.015160175860818066\n",
+            "SMA20                           0.7217313051223755     0.7217313051223755   0.010941573509912955\n",
+            "SMA50                           0.6360406279563904     0.6360406279563904   0.009642487774444033\n",
+            "Tax %                           0.4826675355434418     0.4826675355434418   0.007317324721146231\n",
+            "Equity Capital                  0.33975687623023987    0.33975687623023987  0.005150773993572634\n",
+            "CWIP                            0.31883957982063293    0.31883957982063293  0.004833664101470134\n",
+            "Borrowing.46043.0               0.2975037693977356     0.2975037693977356   0.004510209463325935\n",
+            "Revenue.33741.0                 0.2882390022277832     0.2882390022277832   0.004369753963719924\n",
+            "Financing Profit.2579.0         0.28453347086906433    0.28453347086906433  0.0043135774566639695\n",
+            "Borrowing.206214.0              0.2839178442955017     0.2839178442955017   0.004304244449544167\n",
+            "---                             ---                    ---                  ---\n",
+            "NIFTY50_open                    0.08175812661647797    0.08175812661647797  0.0012394675775568363\n",
+            "inflation                       0.07582783699035645    0.07582783699035645  0.001149563343919249\n",
+            "NIFTY50_close                   0.07514502108097076    0.07514502108097076  0.0011392117346523975\n",
+            "NIFTY50_volume                  0.06987543404102325    0.06987543404102325  0.0010593238684129056\n",
+            "gdp_growth                      0.06714197248220444    0.06714197248220444  0.0010178841104724265\n",
+            "Revenue.missing(NA)             0.0                    0.0                  0.0\n",
+            "Financing Profit.missing(NA)    0.0                    0.0                  0.0\n",
+            "Borrowing.missing(NA)           0.0                    0.0                  0.0\n",
+            "Financing Margin %.missing(NA)  0.0                    0.0                  0.0\n",
+            "Deposits.missing(NA)            0.0                    0.0                  0.0\n",
+            "[280 rows x 4 columns]\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# this is equivalent to\n",
+        "m = aml.get_best_model()\n",
+        "print(m)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OMpmg38CLYIa",
+        "outputId": "5bebe6a3-a33f-4951-9718-dbfeed29f4f8"
+      },
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
             "Model Details\n",
             "=============\n",
-            "H2ORandomForestEstimator : Distributed Random Forest\n",
-            "Model Key: DRF_1_AutoML_1_20250409_35034\n",
+            "H2ODeepLearningEstimator : Deep Learning\n",
+            "Model Key: DeepLearning_grid_1_AutoML_3_20250409_153059_model_1\n",
             "\n",
             "\n",
-            "Model Summary: \n",
-            "    number_of_trees    number_of_internal_trees    model_size_in_bytes    min_depth    max_depth    mean_depth    min_leaves    max_leaves    mean_leaves\n",
-            "--  -----------------  --------------------------  ---------------------  -----------  -----------  ------------  ------------  ------------  -------------\n",
-            "    45                 45                          252849                 19           20           19.9778       303           521           441.089\n",
+            "Status of Neuron Layers: predicting RESULT DATE+1 open price, regression, gaussian distribution, Quadratic loss, 28,201 weights/biases, 346.8 KB, 858,931 training samples, mini-batch size 1\n",
+            "    layer    units    type              dropout    l1    l2    mean_rate              rate_rms               momentum    mean_weight             weight_rms           mean_bias             bias_rms\n",
+            "--  -------  -------  ----------------  ---------  ----  ----  ---------------------  ---------------------  ----------  ----------------------  -------------------  --------------------  -----------------------\n",
+            "    1        280      Input             15.0\n",
+            "    2        100      RectifierDropout  10.0       0.0   0.0   0.04534329561915365    0.1347193717956543     0.0         -0.0048023015300245305  0.07585853338241577  0.06739497309629429   0.1536393165588379\n",
+            "    3        1        Linear                       0.0   0.0   0.0021420995789230802  0.0016298778355121613  0.0         0.018227952951565384    0.10617497563362122  -0.23974569455258748  1.0971281125650402e-154\n",
             "\n",
-            "ModelMetricsRegression: drf\n",
+            "ModelMetricsRegression: deeplearning\n",
             "** Reported on train data. **\n",
             "\n",
-            "MSE: 0.0014267131828796813\n",
-            "RMSE: 0.03777185702185797\n",
-            "MAE: 0.028688241892682823\n",
-            "RMSLE: 0.03758799502993611\n",
-            "Mean Residual Deviance: 0.0014267131828796813\n",
+            "MSE: 9173.741318110073\n",
+            "RMSE: 95.77964981200377\n",
+            "MAE: 72.66573385585798\n",
+            "RMSLE: NaN\n",
+            "Mean Residual Deviance: 9173.741318110073\n",
             "\n",
-            "ModelMetricsRegression: drf\n",
+            "ModelMetricsRegression: deeplearning\n",
             "** Reported on validation data. **\n",
             "\n",
-            "MSE: 0.001447253170302201\n",
-            "RMSE: 0.03804278079087018\n",
-            "MAE: 0.029712366126973765\n",
-            "RMSLE: 0.03847986738748068\n",
-            "Mean Residual Deviance: 0.001447253170302201\n",
+            "MSE: 13251.392204832708\n",
+            "RMSE: 115.1146915247255\n",
+            "MAE: 78.05043438612668\n",
+            "RMSLE: 0.19857098335218032\n",
+            "Mean Residual Deviance: 13251.392204832708\n",
             "\n",
             "Scoring History: \n",
-            "    timestamp            duration    number_of_trees    training_rmse    training_mae    training_deviance    validation_rmse    validation_mae    validation_deviance\n",
-            "--  -------------------  ----------  -----------------  ---------------  --------------  -------------------  -----------------  ----------------  ---------------------\n",
-            "    2025-04-09 03:51:02  0.016 sec   0                  nan              nan             nan                  nan                nan               nan\n",
-            "    2025-04-09 03:51:07  4.841 sec   5                  0.0475896        0.0363132       0.00226477           0.0400197          0.0319293         0.00160158\n",
-            "    2025-04-09 03:51:09  7.150 sec   10                 0.0439589        0.0329243       0.00193238           0.0385291          0.0303373         0.00148449\n",
-            "    2025-04-09 03:51:12  9.813 sec   15                 0.0406966        0.0309486       0.00165621           0.0396747          0.0313307         0.00157408\n",
-            "    2025-04-09 03:51:15  12.842 sec  20                 0.0396125        0.0300239       0.00156915           0.0387996          0.0303913         0.00150541\n",
-            "    2025-04-09 03:51:17  15.506 sec  25                 0.0390685        0.0295927       0.00152635           0.0383261          0.0297908         0.00146889\n",
-            "    2025-04-09 03:51:21  18.979 sec  30                 0.038768         0.0291746       0.00150296           0.0382451          0.0298145         0.00146269\n",
-            "    2025-04-09 03:51:23  20.999 sec  35                 0.0382724        0.0288737       0.00146477           0.0383061          0.0298538         0.00146736\n",
-            "    2025-04-09 03:51:24  22.270 sec  40                 0.0381419        0.0289535       0.0014548            0.0381015          0.0297459         0.00145172\n",
-            "    2025-04-09 03:51:26  24.409 sec  45                 0.0377719        0.0286882       0.00142671           0.0380428          0.0297124         0.00144725\n",
+            "    timestamp            duration          training_speed    epochs    iterations    samples    training_rmse    training_deviance    training_mae    training_r2    validation_rmse    validation_deviance    validation_mae    validation_r2\n",
+            "--  -------------------  ----------------  ----------------  --------  ------------  ---------  ---------------  -------------------  --------------  -------------  -----------------  ---------------------  ----------------  ---------------\n",
+            "    2025-04-09 15:31:45  0.000 sec                           0         0             0          nan              nan                  nan             nan            nan                nan                    nan               nan\n",
+            "    2025-04-09 15:31:46  1.225 sec         7777 obs/sec      9.62861   1             8011       268.819          72263.4              208.437         0.972513       362.259            131232                 258.831           0.97669\n",
+            "    2025-04-09 15:31:51  6.267 sec         9403 obs/sec      67.5517   7             56203      243.589          59335.6              158.98          0.97743        220.515            48627                  146.444           0.991362\n",
+            "    2025-04-09 15:31:56  11.598 sec        7836 obs/sec      106.173   11            88336      217.947          47501.1              141.468         0.981932       214.145            45857.9                141.017           0.991854\n",
+            "    2025-04-09 15:32:02  17.166 sec        9565 obs/sec      193.077   20            160640     236.658          56007                138.355         0.978696       245.581            60310.2                179.234           0.989287\n",
+            "    2025-04-09 15:32:07  22.589 sec        9805 obs/sec      260.573   27            216797     250.98           62990.8              136.343         0.97604        205.631            42284.3                149.644           0.992489\n",
+            "    2025-04-09 15:32:13  28.042 sec        9334 obs/sec      308.879   32            256987     269.767          72774.3              124.139         0.972319       200.255            40102                  151.834           0.992877\n",
+            "    2025-04-09 15:32:18  33.512 sec        9996 obs/sec      395.794   41            329301     206.496          42640.7              117.108         0.983781       192.205            36942.6                138.162           0.993438\n",
+            "    2025-04-09 15:32:23  38.556 sec        9740 obs/sec      444.06    46            369458     127.498          16255.7              91.034          0.993817       148.004            21905.1                112.624           0.996109\n",
+            "    2025-04-09 15:32:29  44.001 sec        9637 obs/sec      502.006   52            417669     151.472          22943.7              117.446         0.991273       168.935            28539.1                126.815           0.994931\n",
+            "    2025-04-09 15:32:34  49.136 sec        9615 obs/sec      559.846   58            465792     133.393          17793.8              101.435         0.993232       129.569            16788.1                96.4624           0.997018\n",
+            "    2025-04-09 15:32:39  54.236 sec        9607 obs/sec      617.743   64            513962     119.925          14381.9              92.0844         0.994529       119.316            14236.4                88.808            0.997471\n",
+            "    2025-04-09 15:32:45  59.627 sec        9687 obs/sec      685.221   71            570104     95.7796          9173.74              72.6657         0.996511       115.115            13251.4                78.0504           0.997646\n",
+            "    2025-04-09 15:32:50  1 min  5.233 sec  10098 obs/sec     781.655   81            650337     104.341          10887                74.5243         0.995859       115.824            13415.1                90.5743           0.997617\n",
+            "    2025-04-09 15:32:56  1 min 10.724 sec  10113 obs/sec     849.118   88            706466     89.6967          8045.5               63.0588         0.99694        134.554            18104.8                98.6566           0.996784\n",
+            "    2025-04-09 15:33:01  1 min 15.901 sec  10489 obs/sec     945.573   98            786717     84.7465          7181.96              58.7717         0.997268       156.665            24544                  122.798           0.99564\n",
+            "    2025-04-09 15:33:06  1 min 21.118 sec  10715 obs/sec     1032.37   107           858931     104.103          10837.4              63.9932         0.995878       126.884            16099.5                89.5696           0.99714\n",
+            "    2025-04-09 15:33:06  1 min 21.185 sec  10713 obs/sec     1032.37   107           858931     95.7796          9173.74              72.6657         0.996511       115.115            13251.4                78.0504           0.997646\n",
             "\n",
             "Variable Importances: \n",
-            "variable             relative_importance    scaled_importance      percentage\n",
-            "-------------------  ---------------------  ---------------------  ----------------------\n",
-            "RSI14                1.701903223991394      1.0                    0.0494276988048072\n",
-            "Financing Profit     1.6198419332504272     0.9517826339452414     0.04704442535829146\n",
-            "Revenue              1.4415013790130615     0.8469937412965091     0.04186495153436064\n",
-            "Borrowing            0.8915032148361206     0.5238272084268809     0.025891573483886834\n",
-            "image_embedding_37   0.3991141617298126     0.2345104916093812     0.01159131394583576\n",
-            "image_embedding_93   0.3840157985687256     0.22563903349810413    0.011152818186352094\n",
-            "image_embedding_50   0.3491175174713135     0.2051335895895094     0.010139281280979203\n",
-            "text_embeddings_118  0.31623440980911255    0.18581221620079125    0.009184270256626426\n",
-            "image_embedding_25   0.2951158285140991     0.1734034135160622     0.008570931694997355\n",
-            "Financing Margin %   0.2903645932674408     0.1706116947040398     0.008432943458408998\n",
-            "---                  ---                    ---                    ---\n",
-            "image_embedding_121  0.022208569571375847   0.013049255244543886   0.0006449946578543659\n",
-            "Sales                0.021111510694026947   0.012404648158850718   0.0006131332129752794\n",
-            "Other Income         0.021029533818364143   0.012356480393194485   0.0006107523911623227\n",
-            "CWIP                 0.017842406406998634   0.010483796114536802   0.000518189916680333\n",
-            "Other Liabilities    0.017541129142045975   0.010306772379752348   0.000509440040836105\n",
-            "Total Assets         0.01722622476518154    0.010121741660951603   0.0005002943981975848\n",
-            "Other Assets         0.01622455008327961    0.00953318018002747    0.0004712031585903555\n",
-            "Depreciation         0.015722332522273064   0.00923808845334943    0.00045661745360432276\n",
-            "Fixed Assets         0.004519629757851362   0.0026556326435833912  0.00013126181044325377\n",
-            "Equity Capital       0.0033692489378154278  0.0019796947854142295  9.785175757890195e-05\n",
-            "[296 rows x 4 columns]\n",
-            "\n",
+            "variable                        relative_importance    scaled_importance    percentage\n",
+            "------------------------------  ---------------------  -------------------  ---------------------\n",
+            "RESULT DATE open price          1.0                    1.0                  0.015160175860818066\n",
+            "SMA20                           0.7217313051223755     0.7217313051223755   0.010941573509912955\n",
+            "SMA50                           0.6360406279563904     0.6360406279563904   0.009642487774444033\n",
+            "Tax %                           0.4826675355434418     0.4826675355434418   0.007317324721146231\n",
+            "Equity Capital                  0.33975687623023987    0.33975687623023987  0.005150773993572634\n",
+            "CWIP                            0.31883957982063293    0.31883957982063293  0.004833664101470134\n",
+            "Borrowing.46043.0               0.2975037693977356     0.2975037693977356   0.004510209463325935\n",
+            "Revenue.33741.0                 0.2882390022277832     0.2882390022277832   0.004369753963719924\n",
+            "Financing Profit.2579.0         0.28453347086906433    0.28453347086906433  0.0043135774566639695\n",
+            "Borrowing.206214.0              0.2839178442955017     0.2839178442955017   0.004304244449544167\n",
+            "---                             ---                    ---                  ---\n",
+            "NIFTY50_open                    0.08175812661647797    0.08175812661647797  0.0012394675775568363\n",
+            "inflation                       0.07582783699035645    0.07582783699035645  0.001149563343919249\n",
+            "NIFTY50_close                   0.07514502108097076    0.07514502108097076  0.0011392117346523975\n",
+            "NIFTY50_volume                  0.06987543404102325    0.06987543404102325  0.0010593238684129056\n",
+            "gdp_growth                      0.06714197248220444    0.06714197248220444  0.0010178841104724265\n",
+            "Revenue.missing(NA)             0.0                    0.0                  0.0\n",
+            "Financing Profit.missing(NA)    0.0                    0.0                  0.0\n",
+            "Borrowing.missing(NA)           0.0                    0.0                  0.0\n",
+            "Financing Margin %.missing(NA)  0.0                    0.0                  0.0\n",
+            "Deposits.missing(NA)            0.0                    0.0                  0.0\n",
+            "[280 rows x 4 columns]\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Get the best model using a non-default metric\n",
+        "m = aml.get_best_model(criterion=\"RMSE\")\n",
+        "print(m)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "FiOVLrGkLa2B",
+        "outputId": "705eee7b-7337-419f-9c36-b20be64e4237"
+      },
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
             "Model Details\n",
             "=============\n",
-            "H2ORandomForestEstimator : Distributed Random Forest\n",
-            "Model Key: DRF_1_AutoML_1_20250409_35034\n",
+            "H2ODeepLearningEstimator : Deep Learning\n",
+            "Model Key: DeepLearning_grid_1_AutoML_3_20250409_153059_model_1\n",
             "\n",
             "\n",
-            "Model Summary: \n",
-            "    number_of_trees    number_of_internal_trees    model_size_in_bytes    min_depth    max_depth    mean_depth    min_leaves    max_leaves    mean_leaves\n",
-            "--  -----------------  --------------------------  ---------------------  -----------  -----------  ------------  ------------  ------------  -------------\n",
-            "    45                 45                          252849                 19           20           19.9778       303           521           441.089\n",
+            "Status of Neuron Layers: predicting RESULT DATE+1 open price, regression, gaussian distribution, Quadratic loss, 28,201 weights/biases, 346.8 KB, 858,931 training samples, mini-batch size 1\n",
+            "    layer    units    type              dropout    l1    l2    mean_rate              rate_rms               momentum    mean_weight             weight_rms           mean_bias             bias_rms\n",
+            "--  -------  -------  ----------------  ---------  ----  ----  ---------------------  ---------------------  ----------  ----------------------  -------------------  --------------------  -----------------------\n",
+            "    1        280      Input             15.0\n",
+            "    2        100      RectifierDropout  10.0       0.0   0.0   0.04534329561915365    0.1347193717956543     0.0         -0.0048023015300245305  0.07585853338241577  0.06739497309629429   0.1536393165588379\n",
+            "    3        1        Linear                       0.0   0.0   0.0021420995789230802  0.0016298778355121613  0.0         0.018227952951565384    0.10617497563362122  -0.23974569455258748  1.0971281125650402e-154\n",
             "\n",
-            "ModelMetricsRegression: drf\n",
+            "ModelMetricsRegression: deeplearning\n",
             "** Reported on train data. **\n",
             "\n",
-            "MSE: 0.0014267131828796813\n",
-            "RMSE: 0.03777185702185797\n",
-            "MAE: 0.028688241892682823\n",
-            "RMSLE: 0.03758799502993611\n",
-            "Mean Residual Deviance: 0.0014267131828796813\n",
+            "MSE: 9173.741318110073\n",
+            "RMSE: 95.77964981200377\n",
+            "MAE: 72.66573385585798\n",
+            "RMSLE: NaN\n",
+            "Mean Residual Deviance: 9173.741318110073\n",
             "\n",
-            "ModelMetricsRegression: drf\n",
+            "ModelMetricsRegression: deeplearning\n",
             "** Reported on validation data. **\n",
             "\n",
-            "MSE: 0.001447253170302201\n",
-            "RMSE: 0.03804278079087018\n",
-            "MAE: 0.029712366126973765\n",
-            "RMSLE: 0.03847986738748068\n",
-            "Mean Residual Deviance: 0.001447253170302201\n",
+            "MSE: 13251.392204832708\n",
+            "RMSE: 115.1146915247255\n",
+            "MAE: 78.05043438612668\n",
+            "RMSLE: 0.19857098335218032\n",
+            "Mean Residual Deviance: 13251.392204832708\n",
             "\n",
             "Scoring History: \n",
-            "    timestamp            duration    number_of_trees    training_rmse    training_mae    training_deviance    validation_rmse    validation_mae    validation_deviance\n",
-            "--  -------------------  ----------  -----------------  ---------------  --------------  -------------------  -----------------  ----------------  ---------------------\n",
-            "    2025-04-09 03:51:02  0.016 sec   0                  nan              nan             nan                  nan                nan               nan\n",
-            "    2025-04-09 03:51:07  4.841 sec   5                  0.0475896        0.0363132       0.00226477           0.0400197          0.0319293         0.00160158\n",
-            "    2025-04-09 03:51:09  7.150 sec   10                 0.0439589        0.0329243       0.00193238           0.0385291          0.0303373         0.00148449\n",
-            "    2025-04-09 03:51:12  9.813 sec   15                 0.0406966        0.0309486       0.00165621           0.0396747          0.0313307         0.00157408\n",
-            "    2025-04-09 03:51:15  12.842 sec  20                 0.0396125        0.0300239       0.00156915           0.0387996          0.0303913         0.00150541\n",
-            "    2025-04-09 03:51:17  15.506 sec  25                 0.0390685        0.0295927       0.00152635           0.0383261          0.0297908         0.00146889\n",
-            "    2025-04-09 03:51:21  18.979 sec  30                 0.038768         0.0291746       0.00150296           0.0382451          0.0298145         0.00146269\n",
-            "    2025-04-09 03:51:23  20.999 sec  35                 0.0382724        0.0288737       0.00146477           0.0383061          0.0298538         0.00146736\n",
-            "    2025-04-09 03:51:24  22.270 sec  40                 0.0381419        0.0289535       0.0014548            0.0381015          0.0297459         0.00145172\n",
-            "    2025-04-09 03:51:26  24.409 sec  45                 0.0377719        0.0286882       0.00142671           0.0380428          0.0297124         0.00144725\n",
+            "    timestamp            duration          training_speed    epochs    iterations    samples    training_rmse    training_deviance    training_mae    training_r2    validation_rmse    validation_deviance    validation_mae    validation_r2\n",
+            "--  -------------------  ----------------  ----------------  --------  ------------  ---------  ---------------  -------------------  --------------  -------------  -----------------  ---------------------  ----------------  ---------------\n",
+            "    2025-04-09 15:31:45  0.000 sec                           0         0             0          nan              nan                  nan             nan            nan                nan                    nan               nan\n",
+            "    2025-04-09 15:31:46  1.225 sec         7777 obs/sec      9.62861   1             8011       268.819          72263.4              208.437         0.972513       362.259            131232                 258.831           0.97669\n",
+            "    2025-04-09 15:31:51  6.267 sec         9403 obs/sec      67.5517   7             56203      243.589          59335.6              158.98          0.97743        220.515            48627                  146.444           0.991362\n",
+            "    2025-04-09 15:31:56  11.598 sec        7836 obs/sec      106.173   11            88336      217.947          47501.1              141.468         0.981932       214.145            45857.9                141.017           0.991854\n",
+            "    2025-04-09 15:32:02  17.166 sec        9565 obs/sec      193.077   20            160640     236.658          56007                138.355         0.978696       245.581            60310.2                179.234           0.989287\n",
+            "    2025-04-09 15:32:07  22.589 sec        9805 obs/sec      260.573   27            216797     250.98           62990.8              136.343         0.97604        205.631            42284.3                149.644           0.992489\n",
+            "    2025-04-09 15:32:13  28.042 sec        9334 obs/sec      308.879   32            256987     269.767          72774.3              124.139         0.972319       200.255            40102                  151.834           0.992877\n",
+            "    2025-04-09 15:32:18  33.512 sec        9996 obs/sec      395.794   41            329301     206.496          42640.7              117.108         0.983781       192.205            36942.6                138.162           0.993438\n",
+            "    2025-04-09 15:32:23  38.556 sec        9740 obs/sec      444.06    46            369458     127.498          16255.7              91.034          0.993817       148.004            21905.1                112.624           0.996109\n",
+            "    2025-04-09 15:32:29  44.001 sec        9637 obs/sec      502.006   52            417669     151.472          22943.7              117.446         0.991273       168.935            28539.1                126.815           0.994931\n",
+            "    2025-04-09 15:32:34  49.136 sec        9615 obs/sec      559.846   58            465792     133.393          17793.8              101.435         0.993232       129.569            16788.1                96.4624           0.997018\n",
+            "    2025-04-09 15:32:39  54.236 sec        9607 obs/sec      617.743   64            513962     119.925          14381.9              92.0844         0.994529       119.316            14236.4                88.808            0.997471\n",
+            "    2025-04-09 15:32:45  59.627 sec        9687 obs/sec      685.221   71            570104     95.7796          9173.74              72.6657         0.996511       115.115            13251.4                78.0504           0.997646\n",
+            "    2025-04-09 15:32:50  1 min  5.233 sec  10098 obs/sec     781.655   81            650337     104.341          10887                74.5243         0.995859       115.824            13415.1                90.5743           0.997617\n",
+            "    2025-04-09 15:32:56  1 min 10.724 sec  10113 obs/sec     849.118   88            706466     89.6967          8045.5               63.0588         0.99694        134.554            18104.8                98.6566           0.996784\n",
+            "    2025-04-09 15:33:01  1 min 15.901 sec  10489 obs/sec     945.573   98            786717     84.7465          7181.96              58.7717         0.997268       156.665            24544                  122.798           0.99564\n",
+            "    2025-04-09 15:33:06  1 min 21.118 sec  10715 obs/sec     1032.37   107           858931     104.103          10837.4              63.9932         0.995878       126.884            16099.5                89.5696           0.99714\n",
+            "    2025-04-09 15:33:06  1 min 21.185 sec  10713 obs/sec     1032.37   107           858931     95.7796          9173.74              72.6657         0.996511       115.115            13251.4                78.0504           0.997646\n",
             "\n",
             "Variable Importances: \n",
-            "variable             relative_importance    scaled_importance      percentage\n",
-            "-------------------  ---------------------  ---------------------  ----------------------\n",
-            "RSI14                1.701903223991394      1.0                    0.0494276988048072\n",
-            "Financing Profit     1.6198419332504272     0.9517826339452414     0.04704442535829146\n",
-            "Revenue              1.4415013790130615     0.8469937412965091     0.04186495153436064\n",
-            "Borrowing            0.8915032148361206     0.5238272084268809     0.025891573483886834\n",
-            "image_embedding_37   0.3991141617298126     0.2345104916093812     0.01159131394583576\n",
-            "image_embedding_93   0.3840157985687256     0.22563903349810413    0.011152818186352094\n",
-            "image_embedding_50   0.3491175174713135     0.2051335895895094     0.010139281280979203\n",
-            "text_embeddings_118  0.31623440980911255    0.18581221620079125    0.009184270256626426\n",
-            "image_embedding_25   0.2951158285140991     0.1734034135160622     0.008570931694997355\n",
-            "Financing Margin %   0.2903645932674408     0.1706116947040398     0.008432943458408998\n",
-            "---                  ---                    ---                    ---\n",
-            "image_embedding_121  0.022208569571375847   0.013049255244543886   0.0006449946578543659\n",
-            "Sales                0.021111510694026947   0.012404648158850718   0.0006131332129752794\n",
-            "Other Income         0.021029533818364143   0.012356480393194485   0.0006107523911623227\n",
-            "CWIP                 0.017842406406998634   0.010483796114536802   0.000518189916680333\n",
-            "Other Liabilities    0.017541129142045975   0.010306772379752348   0.000509440040836105\n",
-            "Total Assets         0.01722622476518154    0.010121741660951603   0.0005002943981975848\n",
-            "Other Assets         0.01622455008327961    0.00953318018002747    0.0004712031585903555\n",
-            "Depreciation         0.015722332522273064   0.00923808845334943    0.00045661745360432276\n",
-            "Fixed Assets         0.004519629757851362   0.0026556326435833912  0.00013126181044325377\n",
-            "Equity Capital       0.0033692489378154278  0.0019796947854142295  9.785175757890195e-05\n",
-            "[296 rows x 4 columns]\n",
-            "\n",
-            "{'creation_epoch': '1744170635', 'start_epoch': '1744170635', 'start_XGBoost_def_2': '1744170635', 'start_GLM_def_1': '1744170649', 'start_GBM_def_5': '1744170651', 'start_XGBoost_def_1': '1744170655', 'start_DRF_def_1': '1744170662', 'start_GBM_def_2': '1744170687', 'start_GBM_def_3': '1744170694', 'start_GBM_def_4': '1744170697', 'start_XGBoost_def_3': '1744170700', 'start_DRF_XRT': '1744170706', 'start_GBM_def_1': '1744170732', 'start_DeepLearning_def_1': '1744170737', 'start_XGBoost_grid_1': '1744170741', 'start_GBM_grid_1': '1744170758', 'start_DeepLearning_grid_1': '1744170764', 'start_DeepLearning_grid_2': '1744170794', 'start_DeepLearning_grid_3': '1744170825', 'stop_epoch': '1744170855', 'duration_secs': '221'}\n"
+            "variable                        relative_importance    scaled_importance    percentage\n",
+            "------------------------------  ---------------------  -------------------  ---------------------\n",
+            "RESULT DATE open price          1.0                    1.0                  0.015160175860818066\n",
+            "SMA20                           0.7217313051223755     0.7217313051223755   0.010941573509912955\n",
+            "SMA50                           0.6360406279563904     0.6360406279563904   0.009642487774444033\n",
+            "Tax %                           0.4826675355434418     0.4826675355434418   0.007317324721146231\n",
+            "Equity Capital                  0.33975687623023987    0.33975687623023987  0.005150773993572634\n",
+            "CWIP                            0.31883957982063293    0.31883957982063293  0.004833664101470134\n",
+            "Borrowing.46043.0               0.2975037693977356     0.2975037693977356   0.004510209463325935\n",
+            "Revenue.33741.0                 0.2882390022277832     0.2882390022277832   0.004369753963719924\n",
+            "Financing Profit.2579.0         0.28453347086906433    0.28453347086906433  0.0043135774566639695\n",
+            "Borrowing.206214.0              0.2839178442955017     0.2839178442955017   0.004304244449544167\n",
+            "---                             ---                    ---                  ---\n",
+            "NIFTY50_open                    0.08175812661647797    0.08175812661647797  0.0012394675775568363\n",
+            "inflation                       0.07582783699035645    0.07582783699035645  0.001149563343919249\n",
+            "NIFTY50_close                   0.07514502108097076    0.07514502108097076  0.0011392117346523975\n",
+            "NIFTY50_volume                  0.06987543404102325    0.06987543404102325  0.0010593238684129056\n",
+            "gdp_growth                      0.06714197248220444    0.06714197248220444  0.0010178841104724265\n",
+            "Revenue.missing(NA)             0.0                    0.0                  0.0\n",
+            "Financing Profit.missing(NA)    0.0                    0.0                  0.0\n",
+            "Borrowing.missing(NA)           0.0                    0.0                  0.0\n",
+            "Financing Margin %.missing(NA)  0.0                    0.0                  0.0\n",
+            "Deposits.missing(NA)            0.0                    0.0                  0.0\n",
+            "[280 rows x 4 columns]\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Get training timing info\n",
+        "info = aml.training_info\n",
+        "print(info)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QjYm40bYLdNt",
+        "outputId": "62fbb703-2a75-4577-d9ce-d6e3debdc110"
+      },
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{'creation_epoch': '1744212660', 'start_epoch': '1744212660', 'start_XGBoost_def_2': '1744212660', 'start_GLM_def_1': '1744212669', 'start_GBM_def_5': '1744212670', 'start_XGBoost_def_1': '1744212672', 'start_DRF_def_1': '1744212674', 'start_GBM_def_2': '1744212678', 'start_GBM_def_3': '1744212680', 'start_GBM_def_4': '1744212684', 'start_XGBoost_def_3': '1744212688', 'start_DRF_XRT': '1744212688', 'start_GBM_def_1': '1744212692', 'start_DeepLearning_def_1': '1744212693', 'start_XGBoost_grid_1': '1744212694', 'start_GBM_grid_1': '1744212703', 'start_DeepLearning_grid_1': '1744212705', 'start_DeepLearning_grid_2': '1744212787', 'start_DeepLearning_grid_3': '1744212845', 'stop_epoch': '1744212886', 'duration_secs': '226'}\n"
           ]
         }
       ]
@@ -2695,25 +3141,25 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 36
+          "height": 35
         },
-        "id": "orRokDpZrDpf",
-        "outputId": "7453ee70-96c7-49f9-bfc0-c404f9f9b0e1"
+        "id": "1lU0X7MULhaw",
+        "outputId": "366925fd-82a5-4731-8fc8-ded57d309ad3"
       },
-      "execution_count": 18,
+      "execution_count": 39,
       "outputs": [
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "'DRF_1_AutoML_1_20250409_35034'"
+              "'DeepLearning_grid_1_AutoML_3_20250409_153059_model_1'"
             ],
             "application/vnd.google.colaboratory.intrinsic+json": {
               "type": "string"
             }
           },
           "metadata": {},
-          "execution_count": 18
+          "execution_count": 39
         }
       ]
     },
@@ -2721,65 +3167,65 @@
       "cell_type": "code",
       "source": [
         "preds_leader = aml.leader.predict(h2o_test)\n",
-        "preds_leader\n"
+        "preds_leader"
       ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 407
+          "height": 406
         },
-        "id": "oV3lUV26rE7O",
-        "outputId": "2485324b-2a37-4eee-db22-ab7f81749ddf"
+        "id": "Y87tqIQDLjrq",
+        "outputId": "b6632d43-dbf8-454a-b7eb-15f065be8bf3"
       },
-      "execution_count": 19,
+      "execution_count": 40,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "drf prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n"
+            "deeplearning prediction progress: |██████████████████████████████████████████████| (done) 100%\n"
           ]
         },
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "     predict\n",
-              "------------\n",
-              "-0.00437683\n",
-              " 0.0247389\n",
-              "-0.0024143\n",
-              "-0.00363317\n",
-              "-0.000408548\n",
-              "-0.00947364\n",
-              " 0.00130559\n",
-              "-0.00813302\n",
-              " 0.00175513\n",
-              "-0.0157648\n",
+              "  predict\n",
+              "---------\n",
+              " 111.347\n",
+              " 949.212\n",
+              "1912.91\n",
+              "  79.4909\n",
+              " 121.781\n",
+              "2905.96\n",
+              "4425.99\n",
+              " 637.457\n",
+              " 684.523\n",
+              " 991.548\n",
               "[107 rows x 1 column]\n"
             ],
             "text/html": [
               "<table class='dataframe'>\n",
               "<thead>\n",
-              "<tr><th style=\"text-align: right;\">     predict</th></tr>\n",
+              "<tr><th style=\"text-align: right;\">  predict</th></tr>\n",
               "</thead>\n",
               "<tbody>\n",
-              "<tr><td style=\"text-align: right;\">-0.00437683 </td></tr>\n",
-              "<tr><td style=\"text-align: right;\"> 0.0247389  </td></tr>\n",
-              "<tr><td style=\"text-align: right;\">-0.0024143  </td></tr>\n",
-              "<tr><td style=\"text-align: right;\">-0.00363317 </td></tr>\n",
-              "<tr><td style=\"text-align: right;\">-0.000408548</td></tr>\n",
-              "<tr><td style=\"text-align: right;\">-0.00947364 </td></tr>\n",
-              "<tr><td style=\"text-align: right;\"> 0.00130559 </td></tr>\n",
-              "<tr><td style=\"text-align: right;\">-0.00813302 </td></tr>\n",
-              "<tr><td style=\"text-align: right;\"> 0.00175513 </td></tr>\n",
-              "<tr><td style=\"text-align: right;\">-0.0157648  </td></tr>\n",
+              "<tr><td style=\"text-align: right;\"> 111.347 </td></tr>\n",
+              "<tr><td style=\"text-align: right;\"> 949.212 </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">1912.91  </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">  79.4909</td></tr>\n",
+              "<tr><td style=\"text-align: right;\"> 121.781 </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">2905.96  </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">4425.99  </td></tr>\n",
+              "<tr><td style=\"text-align: right;\"> 637.457 </td></tr>\n",
+              "<tr><td style=\"text-align: right;\"> 684.523 </td></tr>\n",
+              "<tr><td style=\"text-align: right;\"> 991.548 </td></tr>\n",
               "</tbody>\n",
               "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[107 rows x 1 column]</pre>"
             ]
           },
           "metadata": {},
-          "execution_count": 19
+          "execution_count": 40
         }
       ]
     },
@@ -2802,10 +3248,10 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "id": "x-_f6tugrOgC",
-        "outputId": "ed84dc1b-1863-41f5-87df-1f8dd29d983d"
+        "id": "hN7dhhXsLl-Z",
+        "outputId": "6a276831-6e04-4c9e-a9af-b65192b953bb"
       },
-      "execution_count": 20,
+      "execution_count": 41,
       "outputs": [
         {
           "output_type": "stream",
@@ -2814,21 +3260,1723 @@
             "\n",
             " TRAIN SET \n",
             "\n",
-            "drf prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n",
-            "{'MAE': 0.010986810057243166, 'RMSE': 0.014322139318291804}\n",
+            "deeplearning prediction progress: |██████████████████████████████████████████████| (done) 100%\n",
+            "{'MAE': 72.6657340887975, 'RMSE': np.float64(95.77964918899451), 'MAPE': 0.27260300292523304}\n",
             "\n",
             " VALID SET \n",
             "\n",
-            "drf prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n",
-            "{'MAE': 0.029712366229212053, 'RMSE': 0.03804278106459263}\n",
+            "deeplearning prediction progress: |██████████████████████████████████████████████| (done) 100%\n",
+            "{'MAE': 78.05043850896263, 'RMSE': np.float64(115.11468693949165), 'MAPE': 0.12153298746353784}\n",
             "\n",
             " TEST SET \n",
             "\n",
-            "drf prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n",
-            "{'MAE': 0.03456114962036238, 'RMSE': 0.04753291990282533}\n"
+            "deeplearning prediction progress: |██████████████████████████████████████████████| (done) 100%\n",
+            "{'MAE': 125.20351368229714, 'RMSE': np.float64(216.63874679442355), 'MAPE': 0.3489434709529733}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Using Numeric, Text Data Classifier Probabilities, and Image Data Classifier Probabilities"
+      ],
+      "metadata": {
+        "id": "IbbSroQQmaX5"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "for col in final_with_funda.columns:\n",
+        "  if 'text_embeddings' in col:\n",
+        "    print(col)\n",
+        "    del final_with_funda[col]\n",
+        "\n",
+        "# all_embeddings_128 = pickle.load(open(path + 'getting_all_texts_together_embeddings_dim128_CPU.pkl', 'rb'))\n",
+        "# final_with_funda['text_embeddings'] = all_embeddings_128\n",
+        "\n",
+        "# for i in range(128):\n",
+        "#   final_with_funda['text_embeddings_' + str(i)] = final_with_funda['text_embeddings'].apply(lambda x : x[i])\n",
+        "\n",
+        "# text_features = ['text_embeddings_' + str(i) for i in range(128)]"
+      ],
+      "metadata": {
+        "id": "YSTz6UtNp45q",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b53efc5a-7646-4e0f-e182-8671cb538bad"
+      },
+      "execution_count": 43,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "text_embeddings\n",
+            "text_embeddings_0\n",
+            "text_embeddings_1\n",
+            "text_embeddings_2\n",
+            "text_embeddings_3\n",
+            "text_embeddings_4\n",
+            "text_embeddings_5\n",
+            "text_embeddings_6\n",
+            "text_embeddings_7\n",
+            "text_embeddings_8\n",
+            "text_embeddings_9\n",
+            "text_embeddings_10\n",
+            "text_embeddings_11\n",
+            "text_embeddings_12\n",
+            "text_embeddings_13\n",
+            "text_embeddings_14\n",
+            "text_embeddings_15\n",
+            "text_embeddings_16\n",
+            "text_embeddings_17\n",
+            "text_embeddings_18\n",
+            "text_embeddings_19\n",
+            "text_embeddings_20\n",
+            "text_embeddings_21\n",
+            "text_embeddings_22\n",
+            "text_embeddings_23\n",
+            "text_embeddings_24\n",
+            "text_embeddings_25\n",
+            "text_embeddings_26\n",
+            "text_embeddings_27\n",
+            "text_embeddings_28\n",
+            "text_embeddings_29\n",
+            "text_embeddings_30\n",
+            "text_embeddings_31\n",
+            "text_embeddings_32\n",
+            "text_embeddings_33\n",
+            "text_embeddings_34\n",
+            "text_embeddings_35\n",
+            "text_embeddings_36\n",
+            "text_embeddings_37\n",
+            "text_embeddings_38\n",
+            "text_embeddings_39\n",
+            "text_embeddings_40\n",
+            "text_embeddings_41\n",
+            "text_embeddings_42\n",
+            "text_embeddings_43\n",
+            "text_embeddings_44\n",
+            "text_embeddings_45\n",
+            "text_embeddings_46\n",
+            "text_embeddings_47\n",
+            "text_embeddings_48\n",
+            "text_embeddings_49\n",
+            "text_embeddings_50\n",
+            "text_embeddings_51\n",
+            "text_embeddings_52\n",
+            "text_embeddings_53\n",
+            "text_embeddings_54\n",
+            "text_embeddings_55\n",
+            "text_embeddings_56\n",
+            "text_embeddings_57\n",
+            "text_embeddings_58\n",
+            "text_embeddings_59\n",
+            "text_embeddings_60\n",
+            "text_embeddings_61\n",
+            "text_embeddings_62\n",
+            "text_embeddings_63\n",
+            "text_embeddings_64\n",
+            "text_embeddings_65\n",
+            "text_embeddings_66\n",
+            "text_embeddings_67\n",
+            "text_embeddings_68\n",
+            "text_embeddings_69\n",
+            "text_embeddings_70\n",
+            "text_embeddings_71\n",
+            "text_embeddings_72\n",
+            "text_embeddings_73\n",
+            "text_embeddings_74\n",
+            "text_embeddings_75\n",
+            "text_embeddings_76\n",
+            "text_embeddings_77\n",
+            "text_embeddings_78\n",
+            "text_embeddings_79\n",
+            "text_embeddings_80\n",
+            "text_embeddings_81\n",
+            "text_embeddings_82\n",
+            "text_embeddings_83\n",
+            "text_embeddings_84\n",
+            "text_embeddings_85\n",
+            "text_embeddings_86\n",
+            "text_embeddings_87\n",
+            "text_embeddings_88\n",
+            "text_embeddings_89\n",
+            "text_embeddings_90\n",
+            "text_embeddings_91\n",
+            "text_embeddings_92\n",
+            "text_embeddings_93\n",
+            "text_embeddings_94\n",
+            "text_embeddings_95\n",
+            "text_embeddings_96\n",
+            "text_embeddings_97\n",
+            "text_embeddings_98\n",
+            "text_embeddings_99\n",
+            "text_embeddings_100\n",
+            "text_embeddings_101\n",
+            "text_embeddings_102\n",
+            "text_embeddings_103\n",
+            "text_embeddings_104\n",
+            "text_embeddings_105\n",
+            "text_embeddings_106\n",
+            "text_embeddings_107\n",
+            "text_embeddings_108\n",
+            "text_embeddings_109\n",
+            "text_embeddings_110\n",
+            "text_embeddings_111\n",
+            "text_embeddings_112\n",
+            "text_embeddings_113\n",
+            "text_embeddings_114\n",
+            "text_embeddings_115\n",
+            "text_embeddings_116\n",
+            "text_embeddings_117\n",
+            "text_embeddings_118\n",
+            "text_embeddings_119\n",
+            "text_embeddings_120\n",
+            "text_embeddings_121\n",
+            "text_embeddings_122\n",
+            "text_embeddings_123\n",
+            "text_embeddings_124\n",
+            "text_embeddings_125\n",
+            "text_embeddings_126\n",
+            "text_embeddings_127\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "img_emd_df = pd.read_csv(path + \"image_embedding_mean_pooled_128_dim_CPU_df.csv\")\n",
+        "final_with_funda = pd.concat([final_with_funda, img_emd_df] , axis = 1)"
+      ],
+      "metadata": {
+        "id": "oIDoIMmlmeHm"
+      },
+      "execution_count": 44,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "image_features = [\"image_embedding_\" + str(i) for i in range(128)]\n",
+        "\n",
+        "train = final_with_funda[final_with_funda['split'] == 'train']\n",
+        "valid = final_with_funda[final_with_funda['split'] == 'validation']\n",
+        "test = final_with_funda[final_with_funda['split'] == 'test']\n",
+        "\n",
+        "x_cols_to_use =  image_features"
+      ],
+      "metadata": {
+        "id": "LquPqDzGo4ae"
+      },
+      "execution_count": 48,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_col =  'classification' #'RESULT DATE+1 open price'#'TARGET-2 REGRESSION NORMALIZED'\n",
+        "\n",
+        "h2o_train = h2o.H2OFrame(train[x_cols_to_use])  # Convert train DataFrame to H2OFrame\n",
+        "h2o_valid = h2o.H2OFrame(valid[x_cols_to_use])  # Convert valid DataFrame to H2OFrame\n",
+        "h2o_test = h2o.H2OFrame(test[x_cols_to_use])  # Convert test DataFrame to H2OFrame\n",
+        "\n",
+        "h2o_train[y_col] = h2o.H2OFrame(train[y_col].to_list()).asfactor()  # Convert Pandas Series to H2OFrame before assigning\n",
+        "h2o_valid[y_col] = h2o.H2OFrame(valid[y_col].to_list()).asfactor()  # Convert Pandas Series to H2OFrame before assigning\n",
+        "h2o_test[y_col] = h2o.H2OFrame(test[y_col].to_list()).asfactor()  # Convert Pandas Series to H2OFrame before assigning\n",
+        "\n",
+        "aml = H2OAutoML(max_models=20, seed=1,nfolds = 0)\n",
+        "aml.train(x=x_cols_to_use, y=y_col, training_frame=h2o_train,validation_frame=h2o_valid)\n",
+        "\n",
+        "# View the AutoML Leaderboard\n",
+        "lb = aml.leaderboard\n",
+        "lb.head(rows=lb.nrows)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 844
+        },
+        "id": "0NrGS04FqMfQ",
+        "outputId": "4d498c24-ac2c-4626-e42c-126af926e038"
+      },
+      "execution_count": 49,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
+            "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
+            "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
+            "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
+            "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
+            "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
+            "AutoML progress: |███████████████████████████████████████████████████████████████| (done) 100%\n"
           ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "model_id                                                   auc    logloss     aucpr    mean_per_class_error      rmse       mse\n",
+              "----------------------------------------------------  --------  ---------  --------  ----------------------  --------  --------\n",
+              "XRT_1_AutoML_4_20250409_154354                        0.503017   0.733152  0.484082                0.480392  0.516668  0.266945\n",
+              "XGBoost_grid_1_AutoML_4_20250409_154354_model_1       0.488499   0.847333  0.503155                0.5       0.548085  0.300397\n",
+              "GBM_grid_1_AutoML_4_20250409_154354_model_2           0.487745   0.724781  0.476677                0.470588  0.514349  0.264555\n",
+              "GLM_1_AutoML_4_20250409_154354                        0.486991   0.692994  0.534541                0.4704    0.499927  0.249927\n",
+              "GBM_2_AutoML_4_20250409_154354                        0.481335   0.792884  0.465885                0.490196  0.537859  0.289292\n",
+              "GBM_4_AutoML_4_20250409_154354                        0.479072   0.766912  0.484253                0.480392  0.530249  0.281163\n",
+              "XGBoost_3_AutoML_4_20250409_154354                    0.477941   0.868045  0.477292                0.5       0.557476  0.310779\n",
+              "DeepLearning_grid_2_AutoML_4_20250409_154354_model_1  0.473039   1.05933   0.456692                0.450603  0.596275  0.355543\n",
+              "DeepLearning_1_AutoML_4_20250409_154354               0.472662   0.857147  0.486409                0.5       0.550378  0.302916\n",
+              "DeepLearning_grid_3_AutoML_4_20250409_154354_model_1  0.468514   0.844478  0.459279                0.5       0.549941  0.302435\n",
+              "DRF_1_AutoML_4_20250409_154354                        0.467195   0.760494  0.452942                0.5       0.527926  0.278706\n",
+              "DeepLearning_grid_1_AutoML_4_20250409_154354_model_1  0.462858   1.32825   0.463777                0.5       0.60405   0.364876\n",
+              "XGBoost_grid_1_AutoML_4_20250409_154354_model_3       0.458333   0.917929  0.45348                 0.5       0.568323  0.322991\n",
+              "GBM_1_AutoML_4_20250409_154354                        0.45494    0.719961  0.459342                0.470211  0.512716  0.262878\n",
+              "GBM_5_AutoML_4_20250409_154354                        0.453808   0.776823  0.455488                0.460219  0.53518   0.286418\n",
+              "XGBoost_grid_1_AutoML_4_20250409_154354_model_2       0.4523     0.860165  0.447011                0.5       0.559062  0.31255\n",
+              "GBM_grid_1_AutoML_4_20250409_154354_model_1           0.439103   0.796162  0.464251                0.490008  0.543544  0.29544\n",
+              "XGBoost_2_AutoML_4_20250409_154354                    0.435332   1.03246   0.449842                0.5       0.594755  0.353734\n",
+              "XGBoost_1_AutoML_4_20250409_154354                    0.429299   0.86822   0.437805                0.5       0.563479  0.317509\n",
+              "GBM_3_AutoML_4_20250409_154354                        0.40856    0.819399  0.431929                0.480392  0.552144  0.304863\n",
+              "[20 rows x 7 columns]\n"
+            ],
+            "text/html": [
+              "<table class='dataframe'>\n",
+              "<thead>\n",
+              "<tr><th>model_id                                            </th><th style=\"text-align: right;\">     auc</th><th style=\"text-align: right;\">  logloss</th><th style=\"text-align: right;\">   aucpr</th><th style=\"text-align: right;\">  mean_per_class_error</th><th style=\"text-align: right;\">    rmse</th><th style=\"text-align: right;\">     mse</th></tr>\n",
+              "</thead>\n",
+              "<tbody>\n",
+              "<tr><td>XRT_1_AutoML_4_20250409_154354                      </td><td style=\"text-align: right;\">0.503017</td><td style=\"text-align: right;\"> 0.733152</td><td style=\"text-align: right;\">0.484082</td><td style=\"text-align: right;\">              0.480392</td><td style=\"text-align: right;\">0.516668</td><td style=\"text-align: right;\">0.266945</td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_4_20250409_154354_model_1     </td><td style=\"text-align: right;\">0.488499</td><td style=\"text-align: right;\"> 0.847333</td><td style=\"text-align: right;\">0.503155</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.548085</td><td style=\"text-align: right;\">0.300397</td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_4_20250409_154354_model_2         </td><td style=\"text-align: right;\">0.487745</td><td style=\"text-align: right;\"> 0.724781</td><td style=\"text-align: right;\">0.476677</td><td style=\"text-align: right;\">              0.470588</td><td style=\"text-align: right;\">0.514349</td><td style=\"text-align: right;\">0.264555</td></tr>\n",
+              "<tr><td>GLM_1_AutoML_4_20250409_154354                      </td><td style=\"text-align: right;\">0.486991</td><td style=\"text-align: right;\"> 0.692994</td><td style=\"text-align: right;\">0.534541</td><td style=\"text-align: right;\">              0.4704  </td><td style=\"text-align: right;\">0.499927</td><td style=\"text-align: right;\">0.249927</td></tr>\n",
+              "<tr><td>GBM_2_AutoML_4_20250409_154354                      </td><td style=\"text-align: right;\">0.481335</td><td style=\"text-align: right;\"> 0.792884</td><td style=\"text-align: right;\">0.465885</td><td style=\"text-align: right;\">              0.490196</td><td style=\"text-align: right;\">0.537859</td><td style=\"text-align: right;\">0.289292</td></tr>\n",
+              "<tr><td>GBM_4_AutoML_4_20250409_154354                      </td><td style=\"text-align: right;\">0.479072</td><td style=\"text-align: right;\"> 0.766912</td><td style=\"text-align: right;\">0.484253</td><td style=\"text-align: right;\">              0.480392</td><td style=\"text-align: right;\">0.530249</td><td style=\"text-align: right;\">0.281163</td></tr>\n",
+              "<tr><td>XGBoost_3_AutoML_4_20250409_154354                  </td><td style=\"text-align: right;\">0.477941</td><td style=\"text-align: right;\"> 0.868045</td><td style=\"text-align: right;\">0.477292</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.557476</td><td style=\"text-align: right;\">0.310779</td></tr>\n",
+              "<tr><td>DeepLearning_grid_2_AutoML_4_20250409_154354_model_1</td><td style=\"text-align: right;\">0.473039</td><td style=\"text-align: right;\"> 1.05933 </td><td style=\"text-align: right;\">0.456692</td><td style=\"text-align: right;\">              0.450603</td><td style=\"text-align: right;\">0.596275</td><td style=\"text-align: right;\">0.355543</td></tr>\n",
+              "<tr><td>DeepLearning_1_AutoML_4_20250409_154354             </td><td style=\"text-align: right;\">0.472662</td><td style=\"text-align: right;\"> 0.857147</td><td style=\"text-align: right;\">0.486409</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.550378</td><td style=\"text-align: right;\">0.302916</td></tr>\n",
+              "<tr><td>DeepLearning_grid_3_AutoML_4_20250409_154354_model_1</td><td style=\"text-align: right;\">0.468514</td><td style=\"text-align: right;\"> 0.844478</td><td style=\"text-align: right;\">0.459279</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.549941</td><td style=\"text-align: right;\">0.302435</td></tr>\n",
+              "<tr><td>DRF_1_AutoML_4_20250409_154354                      </td><td style=\"text-align: right;\">0.467195</td><td style=\"text-align: right;\"> 0.760494</td><td style=\"text-align: right;\">0.452942</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.527926</td><td style=\"text-align: right;\">0.278706</td></tr>\n",
+              "<tr><td>DeepLearning_grid_1_AutoML_4_20250409_154354_model_1</td><td style=\"text-align: right;\">0.462858</td><td style=\"text-align: right;\"> 1.32825 </td><td style=\"text-align: right;\">0.463777</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.60405 </td><td style=\"text-align: right;\">0.364876</td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_4_20250409_154354_model_3     </td><td style=\"text-align: right;\">0.458333</td><td style=\"text-align: right;\"> 0.917929</td><td style=\"text-align: right;\">0.45348 </td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.568323</td><td style=\"text-align: right;\">0.322991</td></tr>\n",
+              "<tr><td>GBM_1_AutoML_4_20250409_154354                      </td><td style=\"text-align: right;\">0.45494 </td><td style=\"text-align: right;\"> 0.719961</td><td style=\"text-align: right;\">0.459342</td><td style=\"text-align: right;\">              0.470211</td><td style=\"text-align: right;\">0.512716</td><td style=\"text-align: right;\">0.262878</td></tr>\n",
+              "<tr><td>GBM_5_AutoML_4_20250409_154354                      </td><td style=\"text-align: right;\">0.453808</td><td style=\"text-align: right;\"> 0.776823</td><td style=\"text-align: right;\">0.455488</td><td style=\"text-align: right;\">              0.460219</td><td style=\"text-align: right;\">0.53518 </td><td style=\"text-align: right;\">0.286418</td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_4_20250409_154354_model_2     </td><td style=\"text-align: right;\">0.4523  </td><td style=\"text-align: right;\"> 0.860165</td><td style=\"text-align: right;\">0.447011</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.559062</td><td style=\"text-align: right;\">0.31255 </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_4_20250409_154354_model_1         </td><td style=\"text-align: right;\">0.439103</td><td style=\"text-align: right;\"> 0.796162</td><td style=\"text-align: right;\">0.464251</td><td style=\"text-align: right;\">              0.490008</td><td style=\"text-align: right;\">0.543544</td><td style=\"text-align: right;\">0.29544 </td></tr>\n",
+              "<tr><td>XGBoost_2_AutoML_4_20250409_154354                  </td><td style=\"text-align: right;\">0.435332</td><td style=\"text-align: right;\"> 1.03246 </td><td style=\"text-align: right;\">0.449842</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.594755</td><td style=\"text-align: right;\">0.353734</td></tr>\n",
+              "<tr><td>XGBoost_1_AutoML_4_20250409_154354                  </td><td style=\"text-align: right;\">0.429299</td><td style=\"text-align: right;\"> 0.86822 </td><td style=\"text-align: right;\">0.437805</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.563479</td><td style=\"text-align: right;\">0.317509</td></tr>\n",
+              "<tr><td>GBM_3_AutoML_4_20250409_154354                      </td><td style=\"text-align: right;\">0.40856 </td><td style=\"text-align: right;\"> 0.819399</td><td style=\"text-align: right;\">0.431929</td><td style=\"text-align: right;\">              0.480392</td><td style=\"text-align: right;\">0.552144</td><td style=\"text-align: right;\">0.304863</td></tr>\n",
+              "</tbody>\n",
+              "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[20 rows x 7 columns]</pre>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 49
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "lb = h2o.automl.get_leaderboard(aml, extra_columns = \"ALL\")\n",
+        "lb"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 409
+        },
+        "id": "INdYsJVVq9lX",
+        "outputId": "b67b1b31-8f3e-44f5-efb3-021bcdd4ec3c"
+      },
+      "execution_count": 50,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "model_id                                                   auc    logloss     aucpr    mean_per_class_error      rmse       mse    training_time_ms    predict_time_per_row_ms  algo\n",
+              "----------------------------------------------------  --------  ---------  --------  ----------------------  --------  --------  ------------------  -------------------------  ------------\n",
+              "XRT_1_AutoML_4_20250409_154354                        0.503017   0.733152  0.484082                0.480392  0.516668  0.266945                1101                   0.129188  DRF\n",
+              "XGBoost_grid_1_AutoML_4_20250409_154354_model_1       0.488499   0.847333  0.503155                0.5       0.548085  0.300397                1456                   0.102439  XGBoost\n",
+              "GBM_grid_1_AutoML_4_20250409_154354_model_2           0.487745   0.724781  0.476677                0.470588  0.514349  0.264555                1472                   0.096619  GBM\n",
+              "GLM_1_AutoML_4_20250409_154354                        0.486991   0.692994  0.534541                0.4704    0.499927  0.249927                 573                   0.145861  GLM\n",
+              "GBM_2_AutoML_4_20250409_154354                        0.481335   0.792884  0.465885                0.490196  0.537859  0.289292                1917                   0.13284   GBM\n",
+              "GBM_4_AutoML_4_20250409_154354                        0.479072   0.766912  0.484253                0.480392  0.530249  0.281163                1843                   0.101481  GBM\n",
+              "XGBoost_3_AutoML_4_20250409_154354                    0.477941   0.868045  0.477292                0.5       0.557476  0.310779                 999                   0.090673  XGBoost\n",
+              "DeepLearning_grid_2_AutoML_4_20250409_154354_model_1  0.473039   1.05933   0.456692                0.450603  0.596275  0.355543               32263                   0.134248  DeepLearning\n",
+              "DeepLearning_1_AutoML_4_20250409_154354               0.472662   0.857147  0.486409                0.5       0.550378  0.302916                 353                   0.0919    DeepLearning\n",
+              "DeepLearning_grid_3_AutoML_4_20250409_154354_model_1  0.468514   0.844478  0.459279                0.5       0.549941  0.302435               33586                   0.160156  DeepLearning\n",
+              "[20 rows x 10 columns]\n"
+            ],
+            "text/html": [
+              "<table class='dataframe'>\n",
+              "<thead>\n",
+              "<tr><th>model_id                                            </th><th style=\"text-align: right;\">     auc</th><th style=\"text-align: right;\">  logloss</th><th style=\"text-align: right;\">   aucpr</th><th style=\"text-align: right;\">  mean_per_class_error</th><th style=\"text-align: right;\">    rmse</th><th style=\"text-align: right;\">     mse</th><th style=\"text-align: right;\">  training_time_ms</th><th style=\"text-align: right;\">  predict_time_per_row_ms</th><th>algo        </th></tr>\n",
+              "</thead>\n",
+              "<tbody>\n",
+              "<tr><td>XRT_1_AutoML_4_20250409_154354                      </td><td style=\"text-align: right;\">0.503017</td><td style=\"text-align: right;\"> 0.733152</td><td style=\"text-align: right;\">0.484082</td><td style=\"text-align: right;\">              0.480392</td><td style=\"text-align: right;\">0.516668</td><td style=\"text-align: right;\">0.266945</td><td style=\"text-align: right;\">              1101</td><td style=\"text-align: right;\">                 0.129188</td><td>DRF         </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_4_20250409_154354_model_1     </td><td style=\"text-align: right;\">0.488499</td><td style=\"text-align: right;\"> 0.847333</td><td style=\"text-align: right;\">0.503155</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.548085</td><td style=\"text-align: right;\">0.300397</td><td style=\"text-align: right;\">              1456</td><td style=\"text-align: right;\">                 0.102439</td><td>XGBoost     </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_4_20250409_154354_model_2         </td><td style=\"text-align: right;\">0.487745</td><td style=\"text-align: right;\"> 0.724781</td><td style=\"text-align: right;\">0.476677</td><td style=\"text-align: right;\">              0.470588</td><td style=\"text-align: right;\">0.514349</td><td style=\"text-align: right;\">0.264555</td><td style=\"text-align: right;\">              1472</td><td style=\"text-align: right;\">                 0.096619</td><td>GBM         </td></tr>\n",
+              "<tr><td>GLM_1_AutoML_4_20250409_154354                      </td><td style=\"text-align: right;\">0.486991</td><td style=\"text-align: right;\"> 0.692994</td><td style=\"text-align: right;\">0.534541</td><td style=\"text-align: right;\">              0.4704  </td><td style=\"text-align: right;\">0.499927</td><td style=\"text-align: right;\">0.249927</td><td style=\"text-align: right;\">               573</td><td style=\"text-align: right;\">                 0.145861</td><td>GLM         </td></tr>\n",
+              "<tr><td>GBM_2_AutoML_4_20250409_154354                      </td><td style=\"text-align: right;\">0.481335</td><td style=\"text-align: right;\"> 0.792884</td><td style=\"text-align: right;\">0.465885</td><td style=\"text-align: right;\">              0.490196</td><td style=\"text-align: right;\">0.537859</td><td style=\"text-align: right;\">0.289292</td><td style=\"text-align: right;\">              1917</td><td style=\"text-align: right;\">                 0.13284 </td><td>GBM         </td></tr>\n",
+              "<tr><td>GBM_4_AutoML_4_20250409_154354                      </td><td style=\"text-align: right;\">0.479072</td><td style=\"text-align: right;\"> 0.766912</td><td style=\"text-align: right;\">0.484253</td><td style=\"text-align: right;\">              0.480392</td><td style=\"text-align: right;\">0.530249</td><td style=\"text-align: right;\">0.281163</td><td style=\"text-align: right;\">              1843</td><td style=\"text-align: right;\">                 0.101481</td><td>GBM         </td></tr>\n",
+              "<tr><td>XGBoost_3_AutoML_4_20250409_154354                  </td><td style=\"text-align: right;\">0.477941</td><td style=\"text-align: right;\"> 0.868045</td><td style=\"text-align: right;\">0.477292</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.557476</td><td style=\"text-align: right;\">0.310779</td><td style=\"text-align: right;\">               999</td><td style=\"text-align: right;\">                 0.090673</td><td>XGBoost     </td></tr>\n",
+              "<tr><td>DeepLearning_grid_2_AutoML_4_20250409_154354_model_1</td><td style=\"text-align: right;\">0.473039</td><td style=\"text-align: right;\"> 1.05933 </td><td style=\"text-align: right;\">0.456692</td><td style=\"text-align: right;\">              0.450603</td><td style=\"text-align: right;\">0.596275</td><td style=\"text-align: right;\">0.355543</td><td style=\"text-align: right;\">             32263</td><td style=\"text-align: right;\">                 0.134248</td><td>DeepLearning</td></tr>\n",
+              "<tr><td>DeepLearning_1_AutoML_4_20250409_154354             </td><td style=\"text-align: right;\">0.472662</td><td style=\"text-align: right;\"> 0.857147</td><td style=\"text-align: right;\">0.486409</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.550378</td><td style=\"text-align: right;\">0.302916</td><td style=\"text-align: right;\">               353</td><td style=\"text-align: right;\">                 0.0919  </td><td>DeepLearning</td></tr>\n",
+              "<tr><td>DeepLearning_grid_3_AutoML_4_20250409_154354_model_1</td><td style=\"text-align: right;\">0.468514</td><td style=\"text-align: right;\"> 0.844478</td><td style=\"text-align: right;\">0.459279</td><td style=\"text-align: right;\">              0.5     </td><td style=\"text-align: right;\">0.549941</td><td style=\"text-align: right;\">0.302435</td><td style=\"text-align: right;\">             33586</td><td style=\"text-align: right;\">                 0.160156</td><td>DeepLearning</td></tr>\n",
+              "</tbody>\n",
+              "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[20 rows x 10 columns]</pre>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 50
         }
       ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Get the best model using the metric\n",
+        "m = aml.leader\n",
+        "print(m)\n",
+        "# this is equivalent to\n",
+        "m = aml.get_best_model()\n",
+        "print(m)\n",
+        "# Get the best model using a non-default metric\n",
+        "m = aml.get_best_model(criterion=\"logloss\")\n",
+        "print(m)\n",
+        "# Get training timing info\n",
+        "info = aml.training_info\n",
+        "print(info)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HZ4-ceidq-_w",
+        "outputId": "be9a876e-6d63-42bf-d9fc-345df60648d8"
+      },
+      "execution_count": 51,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Could not find exact threshold 0.0; using closest threshold found 0.0.\n",
+            "Model Details\n",
+            "=============\n",
+            "H2ORandomForestEstimator : Distributed Random Forest\n",
+            "Model Key: XRT_1_AutoML_4_20250409_154354\n",
+            "\n",
+            "\n",
+            "Model Summary: \n",
+            "    number_of_trees    number_of_internal_trees    model_size_in_bytes    min_depth    max_depth    mean_depth    min_leaves    max_leaves    mean_leaves\n",
+            "--  -----------------  --------------------------  ---------------------  -----------  -----------  ------------  ------------  ------------  -------------\n",
+            "    40                 40                          54873                  13           20           16.5          94            115           104.475\n",
+            "\n",
+            "ModelMetricsBinomial: drf\n",
+            "** Reported on train data. **\n",
+            "\n",
+            "MSE: 0.27357332063544987\n",
+            "RMSE: 0.5230423698281526\n",
+            "LogLoss: 0.8737924656542455\n",
+            "Mean Per-Class Error: 0.5\n",
+            "AUC: 0.5218963125650214\n",
+            "AUCPR: 0.5305415130974961\n",
+            "Gini: 0.043792625130042895\n",
+            "\n",
+            "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.0\n",
+            "       0    1    Error    Rate\n",
+            "-----  ---  ---  -------  -------------\n",
+            "0      0    410  1        (410.0/410.0)\n",
+            "1      0    422  0        (0.0/422.0)\n",
+            "Total  0    832  0.4928   (410.0/832.0)\n",
+            "\n",
+            "Maximum Metrics: Maximum metrics at their respective thresholds\n",
+            "metric                       threshold    value      idx\n",
+            "---------------------------  -----------  ---------  -----\n",
+            "max f1                       0            0.673046   383\n",
+            "max f2                       0            0.837302   383\n",
+            "max f0point5                 0.0909091    0.563475   378\n",
+            "max accuracy                 0.505051     0.536058   281\n",
+            "max precision                1            0.75       0\n",
+            "max recall                   0            1          383\n",
+            "max specificity              1            0.997561   0\n",
+            "max absolute_mcc             0.505051     0.0706843  281\n",
+            "max min_per_class_accuracy   0.527509     0.492683   211\n",
+            "max mean_per_class_accuracy  0.505051     0.534782   281\n",
+            "max tns                      1            409        0\n",
+            "max fns                      1            419        0\n",
+            "max fps                      0.0625       410        382\n",
+            "max tps                      0            422        383\n",
+            "max tnr                      1            0.997561   0\n",
+            "max fnr                      1            0.992891   0\n",
+            "max fpr                      0.0625       1          382\n",
+            "max tpr                      0            1          383\n",
+            "\n",
+            "Gains/Lift Table: Avg response rate: 50.72 %, avg score: 51.72 %\n",
+            "group    cumulative_data_fraction    lower_threshold    lift      cumulative_lift    response_rate    score     cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain      cumulative_gain    kolmogorov_smirnov\n",
+            "-------  --------------------------  -----------------  --------  -----------------  ---------------  --------  --------------------------  ------------------  --------------  -------------------------  --------  -----------------  --------------------\n",
+            "1        0.0108173                   0.937269           1.31438   1.31438            0.666667         0.976276  0.666667                    0.976276            0.014218        0.014218                   31.4376   31.4376            0.00690094\n",
+            "2        0.0216346                   0.9                0.876251  1.09531            0.444444         0.910603  0.555556                    0.943439            0.00947867      0.0236967                  -12.3749  9.53133            0.00418449\n",
+            "3        0.0300481                   0.86725            0.844956  1.02521            0.428571         0.885818  0.52                        0.927305            0.007109        0.0308057                  -15.5044  2.52133            0.00153739\n",
+            "4        0.0408654                   0.848966           1.31438   1.10176            0.666667         0.859016  0.558824                    0.909229            0.014218        0.0450237                  31.4376   10.1756            0.00843833\n",
+            "5        0.0504808                   0.827941           1.23223   1.12661            0.625            0.841038  0.571429                    0.89624             0.0118483       0.056872                   23.2227   12.6608            0.0129696\n",
+            "6        0.104567                    0.75               1.00769   1.0651             0.511111         0.781686  0.54023                     0.836988            0.0545024       0.111374                   0.768826  6.50978            0.0138134\n",
+            "7        0.155048                    0.6875             1.22049   1.11569            0.619048         0.708304  0.565891                    0.795091            0.0616114       0.172986                   22.0492   11.5691            0.0364004\n",
+            "8        0.209135                    0.647059           0.920063  1.0651             0.466667         0.661059  0.54023                     0.760428            0.049763        0.222749                   -7.99368  6.50978            0.0276269\n",
+            "9        0.307692                    0.571429           1.08196   1.0705             0.54878          0.608311  0.542969                    0.711703            0.106635        0.329384                   8.19558   7.04976            0.044018\n",
+            "10       0.40024                     0.534287           0.793747  1.0065             0.402597         0.545712  0.510511                    0.67332             0.0734597       0.402844                   -20.6253  0.650413           0.00528263\n",
+            "11       0.5                         0.527439           0.902644  0.985782           0.457831         0.530691  0.5                         0.644863            0.0900474       0.492891                   -9.73562  -1.4218            -0.0144261\n",
+            "12       0.644231                    0.5                1.24866   1.04463            0.633333         0.513626  0.529851                    0.615482            0.180095        0.672986                   24.8657   4.46346            0.0583516\n",
+            "13       0.700721                    0.45               0.880912  1.03144            0.446809         0.466114  0.523156                    0.60344             0.049763        0.722749                   -11.9088  3.14357            0.0447\n",
+            "14       0.800481                    0.384615           0.950151  1.02131            0.481928         0.41356   0.518018                    0.579776            0.0947867       0.817536                   -4.98487  2.13057            0.0346087\n",
+            "15       0.902644                    0.285714           0.81182   0.997596           0.411765         0.335175  0.505992                    0.552092            0.0829384       0.900474                   -18.818   -0.240438          -0.00440412\n",
+            "16       1                           0                  1.02229   1                  0.518519         0.193554  0.507212                    0.517186            0.0995261       1                          2.22924   0                  0\n",
+            "\n",
+            "ModelMetricsBinomial: drf\n",
+            "** Reported on validation data. **\n",
+            "\n",
+            "MSE: 0.26694534134080883\n",
+            "RMSE: 0.5166675346301612\n",
+            "LogLoss: 0.733151810715092\n",
+            "Mean Per-Class Error: 0.4803921568627451\n",
+            "AUC: 0.5030165912518854\n",
+            "AUCPR: 0.48408172623538337\n",
+            "Gini: 0.006033182503770718\n",
+            "\n",
+            "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.26249999999999996\n",
+            "       0    1    Error    Rate\n",
+            "-----  ---  ---  -------  ------------\n",
+            "0      2    49   0.9608   (49.0/51.0)\n",
+            "1      0    52   0        (0.0/52.0)\n",
+            "Total  2    101  0.4757   (49.0/103.0)\n",
+            "\n",
+            "Maximum Metrics: Maximum metrics at their respective thresholds\n",
+            "metric                       threshold    value     idx\n",
+            "---------------------------  -----------  --------  -----\n",
+            "max f1                       0.2625       0.679739  44\n",
+            "max f2                       0.2625       0.841424  44\n",
+            "max f0point5                 0.4625       0.596591  30\n",
+            "max accuracy                 0.4625       0.582524  30\n",
+            "max precision                0.4625       0.56      30\n",
+            "max recall                   0.2625       1         44\n",
+            "max specificity              0.872449     0.980392  0\n",
+            "max absolute_mcc             0.4625       0.180515  30\n",
+            "max min_per_class_accuracy   0.529681     0.490196  20\n",
+            "max mean_per_class_accuracy  0.4625       0.580317  30\n",
+            "max tns                      0.872449     50        0\n",
+            "max fns                      0.872449     52        0\n",
+            "max fps                      0.175        51        46\n",
+            "max tps                      0.2625       52        44\n",
+            "max tnr                      0.872449     0.980392  0\n",
+            "max fnr                      0.872449     1         0\n",
+            "max fpr                      0.175        1         46\n",
+            "max tpr                      0.2625       1         44\n",
+            "\n",
+            "Gains/Lift Table: Avg response rate: 50.49 %, avg score: 51.42 %\n",
+            "group    cumulative_data_fraction    lower_threshold    lift      cumulative_lift    response_rate    score     cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain      cumulative_gain    kolmogorov_smirnov\n",
+            "-------  --------------------------  -----------------  --------  -----------------  ---------------  --------  --------------------------  ------------------  --------------  -------------------------  --------  -----------------  --------------------\n",
+            "1        0.0194175                   0.764477           0         0                  0                0.818611  0                           0.818611            0               0                          -100      -100               -0.0392157\n",
+            "2        0.0291262                   0.749898           1.98077   0.660256           1                0.75      0.333333                    0.795741            0.0192308       0.0192308                  98.0769   -33.9744           -0.0199849\n",
+            "3        0.038835                    0.747313           1.98077   0.990385           1                0.747449  0.5                         0.783668            0.0192308       0.0384615                  98.0769   -0.961538          -0.000754148\n",
+            "4        0.0485437                   0.744563           0         0.792308           0                0.745177  0.4                         0.775969            0               0.0384615                  -100      -20.7692           -0.020362\n",
+            "5        0.0582524                   0.73625            1.98077   0.990385           1                0.7375    0.5                         0.769558            0.0192308       0.0576923                  98.0769   -0.961538          -0.00113122\n",
+            "6        0.106796                    0.72               0.792308  0.90035            0.4              0.725     0.454545                    0.749304            0.0384615       0.0961538                  -20.7692  -9.96503           -0.0214932\n",
+            "7        0.15534                     0.6425             0.792308  0.866587           0.4              0.685     0.4375                      0.729209            0.0384615       0.134615                   -20.7692  -13.3413           -0.0418552\n",
+            "8        0.213592                    0.6                0.330128  0.72028            0.166667         0.612075  0.363636                    0.697263            0.0192308       0.153846                   -66.9872  -27.972            -0.120664\n",
+            "9        0.339806                    0.55               1.21893   0.905495           0.615385         0.56729   0.457143                    0.648988            0.153846        0.307692                   21.8935   -9.45055           -0.0648567\n",
+            "10       0.524272                    0.529681           1.25101   1.02707            0.631579         0.530498  0.518519                    0.607297            0.230769        0.538462                   25.1012   2.70655            0.0286576\n",
+            "11       0.650485                    0.5                1.06657   1.03473            0.538462         0.510986  0.522388                    0.58861             0.134615        0.673077                   6.6568    3.47302            0.0456259\n",
+            "12       0.708738                    0.475              1.65064   1.08535            0.833333         0.479398  0.547945                    0.579633            0.0961538       0.769231                   65.0641   8.5353             0.122172\n",
+            "13       0.796117                    0.400909           0.880342  1.06285            0.444444         0.440758  0.536585                    0.564391            0.0769231       0.846154                   -11.9658  6.28518            0.101056\n",
+            "14       0.902913                    0.325              0.36014   0.979735           0.181818         0.366477  0.494624                    0.540982            0.0384615       0.884615                   -63.986   -2.02647           -0.0369532\n",
+            "15       1                           0.175              1.18846   1                  0.6              0.265083  0.504854                    0.514196            0.115385        1                          18.8462   0                  0\n",
+            "\n",
+            "Scoring History: \n",
+            "    timestamp            duration    number_of_trees    training_rmse    training_logloss    training_auc    training_pr_auc    training_lift    training_classification_error    validation_rmse    validation_logloss    validation_auc    validation_pr_auc    validation_lift    validation_classification_error\n",
+            "--  -------------------  ----------  -----------------  ---------------  ------------------  --------------  -----------------  ---------------  -------------------------------  -----------------  --------------------  ----------------  -------------------  -----------------  ---------------------------------\n",
+            "    2025-04-09 15:44:08  0.003 sec   0                  nan              nan                 nan             nan                nan              nan                              nan                nan                   nan               nan                  nan                nan\n",
+            "    2025-04-09 15:44:08  0.134 sec   5                  0.612095         8.68454             0.4993          0.500615           0.975067         0.496032                         0.54931            2.057                 0.483786          0.486203             0.990385           0.485437\n",
+            "    2025-04-09 15:44:08  0.269 sec   10                 0.567014         4.71586             0.517176        0.519524           0.995739         0.493917                         0.533117           1.09021               0.494155          0.471029             0                  0.456311\n",
+            "    2025-04-09 15:44:08  0.405 sec   15                 0.546601         2.73683             0.518128        0.530648           1.0715           0.493381                         0.528227           0.764315              0.486048          0.461115             0                  0.446602\n",
+            "    2025-04-09 15:44:08  0.543 sec   20                 0.537079         1.78979             0.522478        0.533993           1.24136          0.493381                         0.524952           0.755205              0.502074          0.486332             0                  0.485437\n",
+            "    2025-04-09 15:44:08  0.673 sec   25                 0.529604         1.1952              0.515466        0.532999           1.54909          0.492788                         0.518512           0.73966               0.510181          0.490486             0                  0.466019\n",
+            "    2025-04-09 15:44:09  0.815 sec   30                 0.526731         1.03656             0.517221        0.533481           1.43386          0.492788                         0.513769           0.728093              0.518477          0.510371             1.32051            0.475728\n",
+            "    2025-04-09 15:44:09  0.954 sec   35                 0.523509         0.913184            0.525754        0.538357           1.43386          0.492788                         0.511981           0.722618              0.517345          0.500646             0.990385           0.466019\n",
+            "    2025-04-09 15:44:09  1.085 sec   40                 0.523042         0.873792            0.521896        0.530542           1.31438          0.492788                         0.516668           0.733152              0.503017          0.484082             0                  0.475728\n",
+            "\n",
+            "Variable Importances: \n",
+            "variable             relative_importance    scaled_importance    percentage\n",
+            "-------------------  ---------------------  -------------------  --------------------\n",
+            "image_embedding_102  57.265804290771484     1.0                  0.013768384106252987\n",
+            "image_embedding_2    57.028099060058594     0.9958490894582407   0.013711232775523349\n",
+            "image_embedding_58   55.984230041503906     0.9776206015939235   0.013460255952931259\n",
+            "image_embedding_119  52.04658889770508      0.9088598255502456   0.012513531176917864\n",
+            "image_embedding_121  49.1323356628418       0.8579698874631817   0.011812858962191733\n",
+            "image_embedding_74   48.09544372558594      0.8398632363806096   0.01156355963520898\n",
+            "image_embedding_112  47.8549690246582       0.8356639641638656   0.011505742442362132\n",
+            "image_embedding_26   46.8930549621582       0.8188666088413802   0.01127447000231294\n",
+            "image_embedding_34   46.083770751953125     0.804734541367118    0.011079894269111813\n",
+            "image_embedding_29   44.053897857666016     0.7692880315446019   0.010591853106649344\n",
+            "---                  ---                    ---                  ---\n",
+            "image_embedding_79   23.79640769958496      0.41554306264095914  0.005721356499129471\n",
+            "image_embedding_72   23.59850311279297      0.412087167988941    0.00567377441412974\n",
+            "image_embedding_118  22.900894165039062     0.3999052217752505   0.005506048699497935\n",
+            "image_embedding_98   22.726417541503906     0.3968584362512188   0.005464099386113695\n",
+            "image_embedding_59   22.507030487060547     0.3930274055486829   0.005411352283878332\n",
+            "image_embedding_77   20.217405319213867     0.3530449902800361   0.004860859032963889\n",
+            "image_embedding_33   19.780132293701172     0.3454091414357169   0.004755725733098014\n",
+            "image_embedding_3    19.47235870361328      0.34003466719407094  0.00468172790736987\n",
+            "image_embedding_71   19.449649810791016     0.3396381147819724   0.004676268021441837\n",
+            "image_embedding_81   19.439210891723633     0.33945582590649664  0.004673758198185989\n",
+            "[128 rows x 4 columns]\n",
+            "\n",
+            "Could not find exact threshold 0.0; using closest threshold found 0.0.\n",
+            "Model Details\n",
+            "=============\n",
+            "H2ORandomForestEstimator : Distributed Random Forest\n",
+            "Model Key: XRT_1_AutoML_4_20250409_154354\n",
+            "\n",
+            "\n",
+            "Model Summary: \n",
+            "    number_of_trees    number_of_internal_trees    model_size_in_bytes    min_depth    max_depth    mean_depth    min_leaves    max_leaves    mean_leaves\n",
+            "--  -----------------  --------------------------  ---------------------  -----------  -----------  ------------  ------------  ------------  -------------\n",
+            "    40                 40                          54873                  13           20           16.5          94            115           104.475\n",
+            "\n",
+            "ModelMetricsBinomial: drf\n",
+            "** Reported on train data. **\n",
+            "\n",
+            "MSE: 0.27357332063544987\n",
+            "RMSE: 0.5230423698281526\n",
+            "LogLoss: 0.8737924656542455\n",
+            "Mean Per-Class Error: 0.5\n",
+            "AUC: 0.5218963125650214\n",
+            "AUCPR: 0.5305415130974961\n",
+            "Gini: 0.043792625130042895\n",
+            "\n",
+            "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.0\n",
+            "       0    1    Error    Rate\n",
+            "-----  ---  ---  -------  -------------\n",
+            "0      0    410  1        (410.0/410.0)\n",
+            "1      0    422  0        (0.0/422.0)\n",
+            "Total  0    832  0.4928   (410.0/832.0)\n",
+            "\n",
+            "Maximum Metrics: Maximum metrics at their respective thresholds\n",
+            "metric                       threshold    value      idx\n",
+            "---------------------------  -----------  ---------  -----\n",
+            "max f1                       0            0.673046   383\n",
+            "max f2                       0            0.837302   383\n",
+            "max f0point5                 0.0909091    0.563475   378\n",
+            "max accuracy                 0.505051     0.536058   281\n",
+            "max precision                1            0.75       0\n",
+            "max recall                   0            1          383\n",
+            "max specificity              1            0.997561   0\n",
+            "max absolute_mcc             0.505051     0.0706843  281\n",
+            "max min_per_class_accuracy   0.527509     0.492683   211\n",
+            "max mean_per_class_accuracy  0.505051     0.534782   281\n",
+            "max tns                      1            409        0\n",
+            "max fns                      1            419        0\n",
+            "max fps                      0.0625       410        382\n",
+            "max tps                      0            422        383\n",
+            "max tnr                      1            0.997561   0\n",
+            "max fnr                      1            0.992891   0\n",
+            "max fpr                      0.0625       1          382\n",
+            "max tpr                      0            1          383\n",
+            "\n",
+            "Gains/Lift Table: Avg response rate: 50.72 %, avg score: 51.72 %\n",
+            "group    cumulative_data_fraction    lower_threshold    lift      cumulative_lift    response_rate    score     cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain      cumulative_gain    kolmogorov_smirnov\n",
+            "-------  --------------------------  -----------------  --------  -----------------  ---------------  --------  --------------------------  ------------------  --------------  -------------------------  --------  -----------------  --------------------\n",
+            "1        0.0108173                   0.937269           1.31438   1.31438            0.666667         0.976276  0.666667                    0.976276            0.014218        0.014218                   31.4376   31.4376            0.00690094\n",
+            "2        0.0216346                   0.9                0.876251  1.09531            0.444444         0.910603  0.555556                    0.943439            0.00947867      0.0236967                  -12.3749  9.53133            0.00418449\n",
+            "3        0.0300481                   0.86725            0.844956  1.02521            0.428571         0.885818  0.52                        0.927305            0.007109        0.0308057                  -15.5044  2.52133            0.00153739\n",
+            "4        0.0408654                   0.848966           1.31438   1.10176            0.666667         0.859016  0.558824                    0.909229            0.014218        0.0450237                  31.4376   10.1756            0.00843833\n",
+            "5        0.0504808                   0.827941           1.23223   1.12661            0.625            0.841038  0.571429                    0.89624             0.0118483       0.056872                   23.2227   12.6608            0.0129696\n",
+            "6        0.104567                    0.75               1.00769   1.0651             0.511111         0.781686  0.54023                     0.836988            0.0545024       0.111374                   0.768826  6.50978            0.0138134\n",
+            "7        0.155048                    0.6875             1.22049   1.11569            0.619048         0.708304  0.565891                    0.795091            0.0616114       0.172986                   22.0492   11.5691            0.0364004\n",
+            "8        0.209135                    0.647059           0.920063  1.0651             0.466667         0.661059  0.54023                     0.760428            0.049763        0.222749                   -7.99368  6.50978            0.0276269\n",
+            "9        0.307692                    0.571429           1.08196   1.0705             0.54878          0.608311  0.542969                    0.711703            0.106635        0.329384                   8.19558   7.04976            0.044018\n",
+            "10       0.40024                     0.534287           0.793747  1.0065             0.402597         0.545712  0.510511                    0.67332             0.0734597       0.402844                   -20.6253  0.650413           0.00528263\n",
+            "11       0.5                         0.527439           0.902644  0.985782           0.457831         0.530691  0.5                         0.644863            0.0900474       0.492891                   -9.73562  -1.4218            -0.0144261\n",
+            "12       0.644231                    0.5                1.24866   1.04463            0.633333         0.513626  0.529851                    0.615482            0.180095        0.672986                   24.8657   4.46346            0.0583516\n",
+            "13       0.700721                    0.45               0.880912  1.03144            0.446809         0.466114  0.523156                    0.60344             0.049763        0.722749                   -11.9088  3.14357            0.0447\n",
+            "14       0.800481                    0.384615           0.950151  1.02131            0.481928         0.41356   0.518018                    0.579776            0.0947867       0.817536                   -4.98487  2.13057            0.0346087\n",
+            "15       0.902644                    0.285714           0.81182   0.997596           0.411765         0.335175  0.505992                    0.552092            0.0829384       0.900474                   -18.818   -0.240438          -0.00440412\n",
+            "16       1                           0                  1.02229   1                  0.518519         0.193554  0.507212                    0.517186            0.0995261       1                          2.22924   0                  0\n",
+            "\n",
+            "ModelMetricsBinomial: drf\n",
+            "** Reported on validation data. **\n",
+            "\n",
+            "MSE: 0.26694534134080883\n",
+            "RMSE: 0.5166675346301612\n",
+            "LogLoss: 0.733151810715092\n",
+            "Mean Per-Class Error: 0.4803921568627451\n",
+            "AUC: 0.5030165912518854\n",
+            "AUCPR: 0.48408172623538337\n",
+            "Gini: 0.006033182503770718\n",
+            "\n",
+            "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.26249999999999996\n",
+            "       0    1    Error    Rate\n",
+            "-----  ---  ---  -------  ------------\n",
+            "0      2    49   0.9608   (49.0/51.0)\n",
+            "1      0    52   0        (0.0/52.0)\n",
+            "Total  2    101  0.4757   (49.0/103.0)\n",
+            "\n",
+            "Maximum Metrics: Maximum metrics at their respective thresholds\n",
+            "metric                       threshold    value     idx\n",
+            "---------------------------  -----------  --------  -----\n",
+            "max f1                       0.2625       0.679739  44\n",
+            "max f2                       0.2625       0.841424  44\n",
+            "max f0point5                 0.4625       0.596591  30\n",
+            "max accuracy                 0.4625       0.582524  30\n",
+            "max precision                0.4625       0.56      30\n",
+            "max recall                   0.2625       1         44\n",
+            "max specificity              0.872449     0.980392  0\n",
+            "max absolute_mcc             0.4625       0.180515  30\n",
+            "max min_per_class_accuracy   0.529681     0.490196  20\n",
+            "max mean_per_class_accuracy  0.4625       0.580317  30\n",
+            "max tns                      0.872449     50        0\n",
+            "max fns                      0.872449     52        0\n",
+            "max fps                      0.175        51        46\n",
+            "max tps                      0.2625       52        44\n",
+            "max tnr                      0.872449     0.980392  0\n",
+            "max fnr                      0.872449     1         0\n",
+            "max fpr                      0.175        1         46\n",
+            "max tpr                      0.2625       1         44\n",
+            "\n",
+            "Gains/Lift Table: Avg response rate: 50.49 %, avg score: 51.42 %\n",
+            "group    cumulative_data_fraction    lower_threshold    lift      cumulative_lift    response_rate    score     cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain      cumulative_gain    kolmogorov_smirnov\n",
+            "-------  --------------------------  -----------------  --------  -----------------  ---------------  --------  --------------------------  ------------------  --------------  -------------------------  --------  -----------------  --------------------\n",
+            "1        0.0194175                   0.764477           0         0                  0                0.818611  0                           0.818611            0               0                          -100      -100               -0.0392157\n",
+            "2        0.0291262                   0.749898           1.98077   0.660256           1                0.75      0.333333                    0.795741            0.0192308       0.0192308                  98.0769   -33.9744           -0.0199849\n",
+            "3        0.038835                    0.747313           1.98077   0.990385           1                0.747449  0.5                         0.783668            0.0192308       0.0384615                  98.0769   -0.961538          -0.000754148\n",
+            "4        0.0485437                   0.744563           0         0.792308           0                0.745177  0.4                         0.775969            0               0.0384615                  -100      -20.7692           -0.020362\n",
+            "5        0.0582524                   0.73625            1.98077   0.990385           1                0.7375    0.5                         0.769558            0.0192308       0.0576923                  98.0769   -0.961538          -0.00113122\n",
+            "6        0.106796                    0.72               0.792308  0.90035            0.4              0.725     0.454545                    0.749304            0.0384615       0.0961538                  -20.7692  -9.96503           -0.0214932\n",
+            "7        0.15534                     0.6425             0.792308  0.866587           0.4              0.685     0.4375                      0.729209            0.0384615       0.134615                   -20.7692  -13.3413           -0.0418552\n",
+            "8        0.213592                    0.6                0.330128  0.72028            0.166667         0.612075  0.363636                    0.697263            0.0192308       0.153846                   -66.9872  -27.972            -0.120664\n",
+            "9        0.339806                    0.55               1.21893   0.905495           0.615385         0.56729   0.457143                    0.648988            0.153846        0.307692                   21.8935   -9.45055           -0.0648567\n",
+            "10       0.524272                    0.529681           1.25101   1.02707            0.631579         0.530498  0.518519                    0.607297            0.230769        0.538462                   25.1012   2.70655            0.0286576\n",
+            "11       0.650485                    0.5                1.06657   1.03473            0.538462         0.510986  0.522388                    0.58861             0.134615        0.673077                   6.6568    3.47302            0.0456259\n",
+            "12       0.708738                    0.475              1.65064   1.08535            0.833333         0.479398  0.547945                    0.579633            0.0961538       0.769231                   65.0641   8.5353             0.122172\n",
+            "13       0.796117                    0.400909           0.880342  1.06285            0.444444         0.440758  0.536585                    0.564391            0.0769231       0.846154                   -11.9658  6.28518            0.101056\n",
+            "14       0.902913                    0.325              0.36014   0.979735           0.181818         0.366477  0.494624                    0.540982            0.0384615       0.884615                   -63.986   -2.02647           -0.0369532\n",
+            "15       1                           0.175              1.18846   1                  0.6              0.265083  0.504854                    0.514196            0.115385        1                          18.8462   0                  0\n",
+            "\n",
+            "Scoring History: \n",
+            "    timestamp            duration    number_of_trees    training_rmse    training_logloss    training_auc    training_pr_auc    training_lift    training_classification_error    validation_rmse    validation_logloss    validation_auc    validation_pr_auc    validation_lift    validation_classification_error\n",
+            "--  -------------------  ----------  -----------------  ---------------  ------------------  --------------  -----------------  ---------------  -------------------------------  -----------------  --------------------  ----------------  -------------------  -----------------  ---------------------------------\n",
+            "    2025-04-09 15:44:08  0.003 sec   0                  nan              nan                 nan             nan                nan              nan                              nan                nan                   nan               nan                  nan                nan\n",
+            "    2025-04-09 15:44:08  0.134 sec   5                  0.612095         8.68454             0.4993          0.500615           0.975067         0.496032                         0.54931            2.057                 0.483786          0.486203             0.990385           0.485437\n",
+            "    2025-04-09 15:44:08  0.269 sec   10                 0.567014         4.71586             0.517176        0.519524           0.995739         0.493917                         0.533117           1.09021               0.494155          0.471029             0                  0.456311\n",
+            "    2025-04-09 15:44:08  0.405 sec   15                 0.546601         2.73683             0.518128        0.530648           1.0715           0.493381                         0.528227           0.764315              0.486048          0.461115             0                  0.446602\n",
+            "    2025-04-09 15:44:08  0.543 sec   20                 0.537079         1.78979             0.522478        0.533993           1.24136          0.493381                         0.524952           0.755205              0.502074          0.486332             0                  0.485437\n",
+            "    2025-04-09 15:44:08  0.673 sec   25                 0.529604         1.1952              0.515466        0.532999           1.54909          0.492788                         0.518512           0.73966               0.510181          0.490486             0                  0.466019\n",
+            "    2025-04-09 15:44:09  0.815 sec   30                 0.526731         1.03656             0.517221        0.533481           1.43386          0.492788                         0.513769           0.728093              0.518477          0.510371             1.32051            0.475728\n",
+            "    2025-04-09 15:44:09  0.954 sec   35                 0.523509         0.913184            0.525754        0.538357           1.43386          0.492788                         0.511981           0.722618              0.517345          0.500646             0.990385           0.466019\n",
+            "    2025-04-09 15:44:09  1.085 sec   40                 0.523042         0.873792            0.521896        0.530542           1.31438          0.492788                         0.516668           0.733152              0.503017          0.484082             0                  0.475728\n",
+            "\n",
+            "Variable Importances: \n",
+            "variable             relative_importance    scaled_importance    percentage\n",
+            "-------------------  ---------------------  -------------------  --------------------\n",
+            "image_embedding_102  57.265804290771484     1.0                  0.013768384106252987\n",
+            "image_embedding_2    57.028099060058594     0.9958490894582407   0.013711232775523349\n",
+            "image_embedding_58   55.984230041503906     0.9776206015939235   0.013460255952931259\n",
+            "image_embedding_119  52.04658889770508      0.9088598255502456   0.012513531176917864\n",
+            "image_embedding_121  49.1323356628418       0.8579698874631817   0.011812858962191733\n",
+            "image_embedding_74   48.09544372558594      0.8398632363806096   0.01156355963520898\n",
+            "image_embedding_112  47.8549690246582       0.8356639641638656   0.011505742442362132\n",
+            "image_embedding_26   46.8930549621582       0.8188666088413802   0.01127447000231294\n",
+            "image_embedding_34   46.083770751953125     0.804734541367118    0.011079894269111813\n",
+            "image_embedding_29   44.053897857666016     0.7692880315446019   0.010591853106649344\n",
+            "---                  ---                    ---                  ---\n",
+            "image_embedding_79   23.79640769958496      0.41554306264095914  0.005721356499129471\n",
+            "image_embedding_72   23.59850311279297      0.412087167988941    0.00567377441412974\n",
+            "image_embedding_118  22.900894165039062     0.3999052217752505   0.005506048699497935\n",
+            "image_embedding_98   22.726417541503906     0.3968584362512188   0.005464099386113695\n",
+            "image_embedding_59   22.507030487060547     0.3930274055486829   0.005411352283878332\n",
+            "image_embedding_77   20.217405319213867     0.3530449902800361   0.004860859032963889\n",
+            "image_embedding_33   19.780132293701172     0.3454091414357169   0.004755725733098014\n",
+            "image_embedding_3    19.47235870361328      0.34003466719407094  0.00468172790736987\n",
+            "image_embedding_71   19.449649810791016     0.3396381147819724   0.004676268021441837\n",
+            "image_embedding_81   19.439210891723633     0.33945582590649664  0.004673758198185989\n",
+            "[128 rows x 4 columns]\n",
+            "\n",
+            "Model Details\n",
+            "=============\n",
+            "H2OGeneralizedLinearEstimator : Generalized Linear Modeling\n",
+            "Model Key: GLM_1_AutoML_4_20250409_154354\n",
+            "\n",
+            "\n",
+            "GLM Model: summary\n",
+            "    family    link    regularization             lambda_search                                                              number_of_predictors_total    number_of_active_predictors    number_of_iterations    training_frame\n",
+            "--  --------  ------  -------------------------  -------------------------------------------------------------------------  ----------------------------  -----------------------------  ----------------------  ------------------------------------------------\n",
+            "    binomial  logit   Ridge ( lambda = 4.4996 )  nlambda = 30, lambda.max = 4.4996, lambda.min = 4.4996, lambda.1se = -1.0  128                           128                            2                       AutoML_4_20250409_154354_training_py_33_sid_8ad7\n",
+            "\n",
+            "ModelMetricsBinomialGLM: glm\n",
+            "** Reported on train data. **\n",
+            "\n",
+            "MSE: 0.24671077965958113\n",
+            "RMSE: 0.4966998889264836\n",
+            "LogLoss: 0.6865573685617513\n",
+            "AUC: 0.6203357993295573\n",
+            "AUCPR: 0.6311231274689704\n",
+            "Gini: 0.24067159865911458\n",
+            "Null degrees of freedom: 831\n",
+            "Residual degrees of freedom: 703\n",
+            "Null deviance: 1153.223825527466\n",
+            "Residual deviance: 1142.4314612867543\n",
+            "AIC: 1400.4314612867543\n",
+            "\n",
+            "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.48191718356880825\n",
+            "       0    1    Error    Rate\n",
+            "-----  ---  ---  -------  -------------\n",
+            "0      39   371  0.9049   (371.0/410.0)\n",
+            "1      3    419  0.0071   (3.0/422.0)\n",
+            "Total  42   790  0.4495   (374.0/832.0)\n",
+            "\n",
+            "Maximum Metrics: Maximum metrics at their respective thresholds\n",
+            "metric                       threshold    value     idx\n",
+            "---------------------------  -----------  --------  -----\n",
+            "max f1                       0.481917     0.691419  363\n",
+            "max f2                       0.481917     0.84544   363\n",
+            "max f0point5                 0.496723     0.599146  286\n",
+            "max accuracy                 0.496723     0.581731  286\n",
+            "max precision                0.584894     1         0\n",
+            "max recall                   0.472586     1         396\n",
+            "max specificity              0.584894     1         0\n",
+            "max absolute_mcc             0.481917     0.200982  363\n",
+            "max min_per_class_accuracy   0.507302     0.481043  204\n",
+            "max mean_per_class_accuracy  0.514329     0.580511  139\n",
+            "max tns                      0.584894     410       0\n",
+            "max fns                      0.584894     421       0\n",
+            "max fps                      0.465854     410       399\n",
+            "max tps                      0.472586     422       396\n",
+            "max tnr                      0.584894     1         0\n",
+            "max fnr                      0.584894     0.99763   0\n",
+            "max fpr                      0.465854     1         399\n",
+            "max tpr                      0.472586     1         396\n",
+            "\n",
+            "Gains/Lift Table: Avg response rate: 50.72 %, avg score: 50.72 %\n",
+            "group    cumulative_data_fraction    lower_threshold    lift      cumulative_lift    response_rate    score     cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain       cumulative_gain    kolmogorov_smirnov\n",
+            "-------  --------------------------  -----------------  --------  -----------------  ---------------  --------  --------------------------  ------------------  --------------  -------------------------  ---------  -----------------  --------------------\n",
+            "1        0.0108173                   0.549577           1.7525    1.7525             0.888889         0.560555  0.888889                    0.560555            0.0189573       0.0189573                  75.2501    75.2501            0.0165183\n",
+            "2        0.0204327                   0.53997            1.47867   1.62364            0.75             0.545459  0.823529                    0.553451            0.014218        0.0331754                  47.8673    62.3641            0.0258583\n",
+            "3        0.0300481                   0.535781           1.23223   1.49839            0.625            0.53713   0.76                        0.548228            0.0118483       0.0450237                  23.2227    49.8389            0.0303896\n",
+            "4        0.0408654                   0.53296            1.97156   1.62364            1                0.534259  0.823529                    0.544531            0.021327        0.0663507                  97.1564    62.3641            0.0517166\n",
+            "5        0.0504808                   0.529941           1.47867   1.59603            0.75             0.531235  0.809524                    0.541998            0.014218        0.0805687                  47.8673    59.6028            0.0610565\n",
+            "6        0.100962                    0.524757           1.31438   1.4552             0.666667         0.526932  0.738095                    0.534465            0.0663507       0.146919                   31.4376    45.5202            0.0932609\n",
+            "7        0.15024                     0.520632           1.25026   1.38798            0.634146         0.52268   0.704                       0.530599            0.0616114       0.208531                   25.026     38.7981            0.118287\n",
+            "8        0.200721                    0.517881           1.22049   1.34586            0.619048         0.519103  0.682635                    0.527708            0.0616114       0.270142                   22.0492    34.5858            0.140874\n",
+            "9        0.300481                    0.511211           0.997659  1.23026            0.506024         0.514552  0.624                       0.52334             0.0995261       0.369668                   -0.234112  23.0256            0.1404\n",
+            "10       0.40024                     0.507914           0.973905  1.16636            0.493976         0.509622  0.591592                    0.519921            0.0971564       0.466825                   -2.60949   16.6361            0.135117\n",
+            "11       0.606971                    0.50722            1.05456   1.12828            0.534884         0.507238  0.572277                    0.515601            0.218009        0.684834                   5.45575    12.8281            0.158005\n",
+            "12       0.699519                    0.502135           0.768142  1.08063            0.38961          0.504305  0.54811                     0.514107            0.07109         0.755924                   -23.1858   8.06339            0.114461\n",
+            "13       0.799279                    0.496199           1.11643   1.0851             0.566265         0.499093  0.550376                    0.512233            0.111374        0.867299                   11.6428    8.51014            0.13803\n",
+            "14       0.899038                    0.488554           0.783875  1.05168            0.39759          0.492979  0.533422                    0.510096            0.0781991       0.945498                   -21.6125   5.16765            0.0942781\n",
+            "15       1                           0.465854           0.539833  1                  0.27381          0.481523  0.507212                    0.507212            0.0545024       1                          -46.0167   0                  0\n",
+            "\n",
+            "ModelMetricsBinomialGLM: glm\n",
+            "** Reported on validation data. **\n",
+            "\n",
+            "MSE: 0.24992706207487297\n",
+            "RMSE: 0.4999270567541559\n",
+            "LogLoss: 0.692994412275714\n",
+            "AUC: 0.48699095022624433\n",
+            "AUCPR: 0.5345411623118929\n",
+            "Gini: -0.02601809954751133\n",
+            "Null degrees of freedom: 102\n",
+            "Residual degrees of freedom: -26\n",
+            "Null deviance: 142.7808998601915\n",
+            "Residual deviance: 142.7568489287971\n",
+            "AIC: 400.7568489287971\n",
+            "\n",
+            "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.48200451395305594\n",
+            "       0    1    Error    Rate\n",
+            "-----  ---  ---  -------  ------------\n",
+            "0      4    47   0.9216   (47.0/51.0)\n",
+            "1      1    51   0.0192   (1.0/52.0)\n",
+            "Total  5    98   0.466    (48.0/103.0)\n",
+            "\n",
+            "Maximum Metrics: Maximum metrics at their respective thresholds\n",
+            "metric                       threshold    value     idx\n",
+            "---------------------------  -----------  --------  -----\n",
+            "max f1                       0.482005     0.68      80\n",
+            "max f2                       0.465589     0.836013  85\n",
+            "max f0point5                 0.482005     0.574324  80\n",
+            "max accuracy                 0.524517     0.533981  6\n",
+            "max precision                0.561684     1         0\n",
+            "max recall                   0.465589     1         85\n",
+            "max specificity              0.561684     1         0\n",
+            "max absolute_mcc             0.534663     0.171532  2\n",
+            "max min_per_class_accuracy   0.50722      0.45098   40\n",
+            "max mean_per_class_accuracy  0.524517     0.538084  6\n",
+            "max tns                      0.561684     51        0\n",
+            "max fns                      0.561684     51        0\n",
+            "max fps                      0.477157     51        84\n",
+            "max tps                      0.465589     52        85\n",
+            "max tnr                      0.561684     1         0\n",
+            "max fnr                      0.561684     0.980769  0\n",
+            "max fpr                      0.477157     1         84\n",
+            "max tpr                      0.465589     1         85\n",
+            "\n",
+            "Gains/Lift Table: Avg response rate: 50.49 %, avg score: 50.58 %\n",
+            "group    cumulative_data_fraction    lower_threshold    lift      cumulative_lift    response_rate    score     cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain       cumulative_gain    kolmogorov_smirnov\n",
+            "-------  --------------------------  -----------------  --------  -----------------  ---------------  --------  --------------------------  ------------------  --------------  -------------------------  ---------  -----------------  --------------------\n",
+            "1        0.0194175                   0.545653           1.98077   1.98077            1                0.553781  1                           0.553781            0.0384615       0.0384615                  98.0769    98.0769            0.0384615\n",
+            "2        0.0291262                   0.534465           1.98077   1.98077            1                0.534663  1                           0.547408            0.0192308       0.0576923                  98.0769    98.0769            0.0576923\n",
+            "3        0.038835                    0.52956            0         1.48558            0                0.529722  0.75                        0.542986            0               0.0576923                  -100       48.5577            0.0380845\n",
+            "4        0.0485437                   0.527021           1.98077   1.58462            1                0.527029  0.8                         0.539795            0.0192308       0.0769231                  98.0769    58.4615            0.0573152\n",
+            "5        0.0679612                   0.526935           0.990385  1.41484            0.5              0.526935  0.714286                    0.536121            0.0192308       0.0961538                  -0.961538  41.4835            0.0569382\n",
+            "6        0.106796                    0.521065           0.495192  1.08042            0.25             0.523102  0.545455                    0.531387            0.0192308       0.115385                   -50.4808   8.04196            0.0173454\n",
+            "7        0.15534                     0.517754           0.792308  0.990385           0.4              0.518534  0.5                         0.52737             0.0384615       0.153846                   -20.7692   -0.961538          -0.00301659\n",
+            "8        0.203883                    0.516388           1.18846   1.03755            0.6              0.517281  0.52381                     0.524968            0.0576923       0.211538                   18.8462    3.75458            0.01546\n",
+            "9        0.300971                    0.511349           0.594231  0.894541           0.3              0.513575  0.451613                    0.521293            0.0576923       0.269231                   -40.5769   -10.5459           -0.0641026\n",
+            "10       0.398058                    0.507239           0.990385  0.917917           0.5              0.508921  0.463415                    0.518275            0.0961538       0.365385                   -0.961538  -8.20826           -0.0659879\n",
+            "11       0.563107                    0.50722            1.28167   1.02454            0.647059         0.50722   0.517241                    0.515035            0.211538        0.576923                   28.1674    2.45358            0.0279035\n",
+            "12       0.601942                    0.505897           0.495192  0.990385           0.25             0.506673  0.5                         0.514495            0.0192308       0.596154                   -50.4808   -0.961538          -0.0116893\n",
+            "13       0.699029                    0.49958            0.990385  0.990385           0.5              0.502316  0.5                         0.512804            0.0961538       0.692308                   -0.961538  -0.961538          -0.0135747\n",
+            "14       0.796117                    0.495066           0.990385  0.990385           0.5              0.497745  0.5                         0.510967            0.0961538       0.788462                   -0.961538  -0.961538          -0.01546\n",
+            "15       0.893204                    0.48512            0.990385  0.990385           0.5              0.49094   0.5                         0.508791            0.0961538       0.884615                   -0.961538  -0.961538          -0.0173454\n",
+            "16       1                           0.465589           1.08042   1                  0.545455         0.48043   0.504854                    0.505762            0.115385        1                          8.04196    0                  0\n",
+            "\n",
+            "Scoring History: \n",
+            "    timestamp            duration    iteration    lambda    predictors    deviance_train      deviance_test       alpha    iterations    training_rmse       training_logloss    training_r2           training_auc        training_pr_auc     training_lift      training_classification_error    validation_rmse     validation_logloss    validation_r2           validation_auc       validation_pr_auc    validation_lift     validation_classification_error\n",
+            "--  -------------------  ----------  -----------  --------  ------------  ------------------  ------------------  -------  ------------  ------------------  ------------------  --------------------  ------------------  ------------------  -----------------  -------------------------------  ------------------  --------------------  ----------------------  -------------------  -------------------  ------------------  ---------------------------------\n",
+            "    2025-04-09 15:43:56  0.000 sec   2            4.5       129           1.3731147371235026  1.385988824551428   0.0\n",
+            "    2025-04-09 15:43:56  0.046 sec   4            3.3       129           1.3693083833193973  1.3866106131408604  0.0\n",
+            "    2025-04-09 15:43:55  0.551 sec   5                                                                                     5             0.4966998889264836  0.6865573685617513  0.012951550461947292  0.6203357993295573  0.6311231274689704  1.752501316482359  0.4495192307692308               0.4999270567541559  0.692994412275714     0.00019751072687501647  0.48699095022624433  0.5345411623118929   1.9807692307692308  0.46601941747572817\n",
+            "    2025-04-09 15:43:56  0.098 sec   6            2.4       129           1.3646624139113563  1.3877253613735065  0.0\n",
+            "    2025-04-09 15:43:56  0.135 sec   8            1.7       129           1.3591271656385213  1.3896190261375776  0.0\n",
+            "    2025-04-09 15:43:56  0.176 sec   10           1.3       129           1.3526687474791932  1.392562863401605   0.0\n",
+            "    2025-04-09 15:43:56  0.220 sec   12           0.92      129           1.3452838554523603  1.3969132830901438  0.0\n",
+            "\n",
+            "Variable Importances: \n",
+            "variable             relative_importance     scaled_importance     percentage\n",
+            "-------------------  ----------------------  --------------------  ---------------------\n",
+            "image_embedding_102  0.008855576626956463    1.0                   0.026157338009840447\n",
+            "image_embedding_124  0.007842048071324825    0.8855491179934634    0.023163607603671102\n",
+            "image_embedding_34   0.007366372738033533    0.831834339913024     0.02175857199729748\n",
+            "image_embedding_74   0.007365822792053223    0.8317722382562401    0.021756947583270014\n",
+            "image_embedding_119  0.007316749542951584    0.8262307302134709    0.021611996484311048\n",
+            "image_embedding_64   0.00683643389493227     0.7719919529714302    0.02019325445475055\n",
+            "image_embedding_58   0.006550434045493603    0.7396959364062208    0.01934847663308296\n",
+            "image_embedding_26   0.0063993120566010475   0.7226307586929437    0.018902097011438773\n",
+            "image_embedding_24   0.006248128600418568    0.7055586398969439    0.01845553582954766\n",
+            "image_embedding_105  0.006024055182933807    0.6802555538389814    0.01779367445483745\n",
+            "---                  ---                     ---                   ---\n",
+            "image_embedding_109  0.00038485092227347195  0.04345859546875603   0.001136761171109172\n",
+            "image_embedding_82   0.0003519737219903618   0.0397459969934595    0.0010396494778960224\n",
+            "image_embedding_118  0.0003119232424069196   0.03522336890603173   0.0009213495663203758\n",
+            "image_embedding_89   0.00030231819255277514  0.03413873599518247   0.0008929784566546945\n",
+            "image_embedding_108  0.0002783353556878865   0.031430517448251864  0.0008221386687181117\n",
+            "image_embedding_62   0.00024555076379328966  0.02772837660800435   0.0007253005193997229\n",
+            "image_embedding_72   0.00024344016856048256  0.027490041452461523  0.0007190663061765613\n",
+            "image_embedding_30   0.0002010560710914433   0.022703893779138746  0.000593873423720446\n",
+            "image_embedding_14   0.00014245958300307393  0.016086991169996265  0.0004207928655949109\n",
+            "image_embedding_65   0.00011829416325781494  0.013358154780991487  0.0003494137697941605\n",
+            "[128 rows x 4 columns]\n",
+            "\n",
+            "{'creation_epoch': '1744213434', 'start_epoch': '1744213434', 'start_XGBoost_def_2': '1744213434', 'start_GLM_def_1': '1744213436', 'start_GBM_def_5': '1744213437', 'start_XGBoost_def_1': '1744213437', 'start_DRF_def_1': '1744213439', 'start_GBM_def_2': '1744213440', 'start_GBM_def_3': '1744213442', 'start_GBM_def_4': '1744213445', 'start_XGBoost_def_3': '1744213447', 'start_DRF_XRT': '1744213448', 'start_GBM_def_1': '1744213449', 'start_DeepLearning_def_1': '1744213450', 'start_XGBoost_grid_1': '1744213451', 'start_GBM_grid_1': '1744213455', 'start_DeepLearning_grid_1': '1744213458', 'start_DeepLearning_grid_2': '1744213488', 'start_DeepLearning_grid_3': '1744213520', 'stop_epoch': '1744213554', 'duration_secs': '119'}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "aml.leader.model_id"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "id": "orRokDpZrDpf",
+        "outputId": "62fa2615-b7e4-4286-f657-d13d6cf703d8"
+      },
+      "execution_count": 52,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'XRT_1_AutoML_4_20250409_154354'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 52
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "preds_leader = aml.leader.predict(h2o_test)\n",
+        "preds_leader\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 406
+        },
+        "id": "oV3lUV26rE7O",
+        "outputId": "b7fc894d-0c91-41b4-86eb-ef09d40776f6"
+      },
+      "execution_count": 53,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "drf prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "  predict        p0        p1\n",
+              "---------  --------  --------\n",
+              "        1  0.575     0.425\n",
+              "        1  0.202551  0.797449\n",
+              "        1  0.470319  0.529681\n",
+              "        1  0.35      0.65\n",
+              "        1  0.470319  0.529681\n",
+              "        1  0.5875    0.4125\n",
+              "        1  0.425     0.575\n",
+              "        1  0.7       0.3\n",
+              "        1  0.470319  0.529681\n",
+              "        1  0.55      0.45\n",
+              "[107 rows x 3 columns]\n"
+            ],
+            "text/html": [
+              "<table class='dataframe'>\n",
+              "<thead>\n",
+              "<tr><th style=\"text-align: right;\">  predict</th><th style=\"text-align: right;\">      p0</th><th style=\"text-align: right;\">      p1</th></tr>\n",
+              "</thead>\n",
+              "<tbody>\n",
+              "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.575   </td><td style=\"text-align: right;\">0.425   </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.202551</td><td style=\"text-align: right;\">0.797449</td></tr>\n",
+              "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.470319</td><td style=\"text-align: right;\">0.529681</td></tr>\n",
+              "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.35    </td><td style=\"text-align: right;\">0.65    </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.470319</td><td style=\"text-align: right;\">0.529681</td></tr>\n",
+              "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.5875  </td><td style=\"text-align: right;\">0.4125  </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.425   </td><td style=\"text-align: right;\">0.575   </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.7     </td><td style=\"text-align: right;\">0.3     </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.470319</td><td style=\"text-align: right;\">0.529681</td></tr>\n",
+              "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.55    </td><td style=\"text-align: right;\">0.45    </td></tr>\n",
+              "</tbody>\n",
+              "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[107 rows x 3 columns]</pre>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 53
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"\\n TRAIN SET \\n\")\n",
+        "train_predictions = aml.leader.predict(h2o_train).as_data_frame()['p1'].to_list()\n",
+        "\n",
+        "\n",
+        "print(\"\\n VALID SET \\n\")\n",
+        "valid_predictions = aml.leader.predict(h2o_valid).as_data_frame()['p1'].to_list()\n",
+        "\n",
+        "print(\"\\n TEST SET \\n\")\n",
+        "test_predictions = aml.leader.predict(h2o_test).as_data_frame()['p1'].to_list()\n",
+        "\n",
+        "image_only_preds = {}\n",
+        "image_only_preds['train_predictions'] = train_predictions\n",
+        "image_only_preds['valid_predictions'] = valid_predictions\n",
+        "image_only_preds['test_predictions'] = test_predictions"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "x-_f6tugrOgC",
+        "outputId": "61c8a979-0080-4bb3-b093-19a5316f3eab"
+      },
+      "execution_count": 54,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " TRAIN SET \n",
+            "\n",
+            "drf prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n",
+            "\n",
+            " VALID SET \n",
+            "\n",
+            "drf prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n",
+            "\n",
+            " TEST SET \n",
+            "\n",
+            "drf prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_col = 'RESULT DATE+1 open price' #'TARGET-2 REGRESSION NORMALIZED'\n",
+        "x_cols_to_use = ['RESULT DATE open price', 'SMA20', 'SMA50', 'RSI14',\n",
+        "                 'gdp_growth', 'inflation', 'NIFTY50_open', 'NIFTY50_close', 'NIFTY50_volume',\n",
+        "                 'Sales', 'Expenses', 'Operating Profit', 'OPM %', 'Other Income', 'Interest',\n",
+        "                 'Depreciation', 'Profit before tax', 'Tax %', 'Net Profit',\n",
+        "                 'EPS in Rs', 'Dividend Payout %', 'Equity Capital', 'Reserves',\n",
+        "                 'Borrowings', 'Other Liabilities', 'Total Liabilities',\n",
+        "                 'Fixed Assets', 'CWIP', 'Investments', 'Other Assets',\n",
+        "                 'Total Assets', 'Cash from Operating Activity',\n",
+        "                 'Cash from Investing Activity', 'Cash from Financing Activity',\n",
+        "                 'Net Cash Flow', 'Revenue', 'Financing Profit', 'Financing Margin %',\n",
+        "                 'Deposits', 'Borrowing'] + ['text_probab'] + ['image_probab']\n",
+        "\n",
+        "train['text_probab'] = text_only_preds['train_predictions']\n",
+        "valid['text_probab'] = text_only_preds['valid_predictions']\n",
+        "test['text_probab'] = text_only_preds['test_predictions']\n",
+        "\n",
+        "train['image_probab'] = image_only_preds['train_predictions']\n",
+        "valid['image_probab'] = image_only_preds['valid_predictions']\n",
+        "test['image_probab'] = image_only_preds['test_predictions']\n",
+        "\n",
+        "h2o_train = h2o.H2OFrame(train[x_cols_to_use])  # Convert train DataFrame to H2OFrame\n",
+        "h2o_valid = h2o.H2OFrame(valid[x_cols_to_use])  # Convert valid DataFrame to H2OFrame\n",
+        "h2o_test = h2o.H2OFrame(test[x_cols_to_use])  # Convert test DataFrame to H2OFrame\n",
+        "\n",
+        "h2o_train[y_col] = h2o.H2OFrame(train[y_col].to_list())  # Convert Pandas Series to H2OFrame before assigning\n",
+        "h2o_valid[y_col] = h2o.H2OFrame(valid[y_col].to_list())  # Convert Pandas Series to H2OFrame before assigning\n",
+        "h2o_test[y_col] = h2o.H2OFrame(test[y_col].to_list())  # Convert Pandas Series to H2OFrame before assigning\n",
+        "\n",
+        "aml = H2OAutoML(max_models=100, seed=1,nfolds = 0)\n",
+        "aml.train(x=x_cols_to_use, y=y_col, training_frame=h2o_train,validation_frame=h2o_valid)\n",
+        "\n",
+        "# View the AutoML Leaderboard\n",
+        "lb = aml.leaderboard\n",
+        "lb.head(rows=lb.nrows)"
+      ],
+      "metadata": {
+        "id": "KRR4zK3hziQP",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "99c23151-f36b-4443-c1df-deb5459b98c3"
+      },
+      "execution_count": 71,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
+            "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
+            "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
+            "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
+            "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
+            "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
+            "AutoML progress: |███████████████████████████████████████████████████████████████| (done) 100%\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "model_id                                                   rmse               mse        mae        rmsle    mean_residual_deviance\n",
+              "-----------------------------------------------------  --------  ----------------  ---------  -----------  ------------------------\n",
+              "DeepLearning_grid_1_AutoML_7_20250409_160125_model_9    100.024   10004.8            64.5561    0.266664            10004.8\n",
+              "DeepLearning_grid_1_AutoML_7_20250409_160125_model_1    134.704   18145.1            90.0874    0.188638            18145.1\n",
+              "DeepLearning_grid_1_AutoML_7_20250409_160125_model_4    135.642   18398.7           100.929     0.194114            18398.7\n",
+              "DeepLearning_grid_1_AutoML_7_20250409_160125_model_10   149.507   22352.2            89.8572  nan                   22352.2\n",
+              "DeepLearning_grid_1_AutoML_7_20250409_160125_model_5    164.876   27184.1           113.692     0.253921            27184.1\n",
+              "DeepLearning_grid_2_AutoML_7_20250409_160125_model_4    168.761   28480.3           136.099     0.283903            28480.3\n",
+              "DeepLearning_grid_1_AutoML_7_20250409_160125_model_2    188.361   35479.9           138.995   nan                   35479.9\n",
+              "DeepLearning_grid_2_AutoML_7_20250409_160125_model_10   193.53    37453.7           139.606   nan                   37453.7\n",
+              "DeepLearning_grid_3_AutoML_7_20250409_160125_model_4    203.619   41460.6           118.388     0.214535            41460.6\n",
+              "DeepLearning_grid_3_AutoML_7_20250409_160125_model_7    205.314   42154             121.346     0.22253             42154\n",
+              "DeepLearning_grid_3_AutoML_7_20250409_160125_model_1    207.528   43067.9           116.164     0.314633            43067.9\n",
+              "DeepLearning_grid_3_AutoML_7_20250409_160125_model_10   215.478   46430.7           114.794     0.193609            46430.7\n",
+              "DeepLearning_grid_1_AutoML_7_20250409_160125_model_7    217.389   47258             147.873   nan                   47258\n",
+              "DeepLearning_grid_1_AutoML_7_20250409_160125_model_6    221.629   49119.5           144.504     0.25002             49119.5\n",
+              "DeepLearning_grid_2_AutoML_7_20250409_160125_model_1    235.728   55567.5           186.976     0.349211            55567.5\n",
+              "DeepLearning_grid_2_AutoML_7_20250409_160125_model_7    248.525   61764.6           181.495   nan                   61764.6\n",
+              "DeepLearning_grid_1_AutoML_7_20250409_160125_model_3    257.242   66173.4           169.275   nan                   66173.4\n",
+              "DeepLearning_grid_1_AutoML_7_20250409_160125_model_11   257.672   66395.1           161.105   nan                   66395.1\n",
+              "DeepLearning_grid_3_AutoML_7_20250409_160125_model_8    275.723   76023             186.465   nan                   76023\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_8         322.575  104055             118.816     0.061503           104055\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_13        323.824  104862             122.203     0.0652133          104862\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_23        334.453  111859             119.161     0.0863272          111859\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_31        343.315  117865             126.802     0.102759           117865\n",
+              "DeepLearning_1_AutoML_7_20250409_160125                 345.776  119561             180.892   nan                  119561\n",
+              "DeepLearning_grid_2_AutoML_7_20250409_160125_model_6    350.948  123165             253.962     0.571391           123165\n",
+              "DeepLearning_grid_1_AutoML_7_20250409_160125_model_8    352.587  124317             263.534   nan                  124317\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_27        352.811  124476             145.251     0.0781429          124476\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_25        354.925  125972             126.807     0.098505           125972\n",
+              "XGBoost_3_AutoML_7_20250409_160125                      356.462  127065             133.239     0.0937253          127065\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_24        361.243  130496             149.89      0.0855626          130496\n",
+              "DeepLearning_grid_2_AutoML_7_20250409_160125_model_9    362.184  131177             213.631     0.30945            131177\n",
+              "GBM_5_AutoML_7_20250409_160125                          366.99   134682             121.127     0.0585085          134682\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_10        371.605  138090             124.646     0.0737216          138090\n",
+              "GBM_grid_1_AutoML_7_20250409_160125_model_12            372.355  138648             112.975     0.0578716          138648\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_29        373.223  139296             133.716     0.0896737          139296\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_2         373.411  139436             154.186     0.0982836          139436\n",
+              "GBM_grid_1_AutoML_7_20250409_160125_model_21            373.766  139701             136.83      0.0625747          139701\n",
+              "GBM_grid_1_AutoML_7_20250409_160125_model_2             373.793  139721             131.995     0.0843552          139721\n",
+              "XGBoost_2_AutoML_7_20250409_160125                      381.087  145227             138.912     0.0829641          145227\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_3         381.373  145445             121.161     0.0683393          145445\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_4         382.775  146517             132.798     0.0945484          146517\n",
+              "GBM_grid_1_AutoML_7_20250409_160125_model_13            384.045  147490             146.04      0.0883831          147490\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_33        386.962  149739             131.287     0.0720799          149739\n",
+              "GBM_grid_1_AutoML_7_20250409_160125_model_8             387.56   150202             144.694     0.107274           150202\n",
+              "DeepLearning_grid_2_AutoML_7_20250409_160125_model_8    389.366  151606             288.426   nan                  151606\n",
+              "GBM_grid_1_AutoML_7_20250409_160125_model_17            392.039  153694             143.235     0.114527           153694\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_12        394.288  155463             129.674     0.0695782          155463\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_16        395.37   156318             178.41      0.202227           156318\n",
+              "DeepLearning_grid_2_AutoML_7_20250409_160125_model_3    398.281  158628             223.427     0.281647           158628\n",
+              "GBM_4_AutoML_7_20250409_160125                          398.305  158647             123.528     0.0877705          158647\n",
+              "GBM_3_AutoML_7_20250409_160125                          403.436  162761             127.608     0.0824873          162761\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_17        403.476  162793             131.504     0.0816832          162793\n",
+              "GBM_2_AutoML_7_20250409_160125                          403.593  162887             122.134     0.074959           162887\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_1         409.332  167553             165.164     0.0996374          167553\n",
+              "XGBoost_1_AutoML_7_20250409_160125                      410.198  168263             166.545     0.103922           168263\n",
+              "GBM_grid_1_AutoML_7_20250409_160125_model_3             414.628  171916             139.648     0.116314           171916\n",
+              "GBM_grid_1_AutoML_7_20250409_160125_model_1             415.403  172560             150.605     0.156217           172560\n",
+              "DeepLearning_grid_2_AutoML_7_20250409_160125_model_11   417.624  174410             265.895     0.475627           174410\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_7         423.496  179349             156.584     0.1058             179349\n",
+              "GBM_grid_1_AutoML_7_20250409_160125_model_5             425.309  180887             139.146     0.0773054          180887\n",
+              "GBM_grid_1_AutoML_7_20250409_160125_model_7             427.33   182611             181.637     0.159149           182611\n",
+              "XRT_1_AutoML_7_20250409_160125                          429.632  184583             148.454     0.0765111          184583\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_20        430.564  185386             131.065     0.0865655          185386\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_11        433.739  188130             147.004     0.0764827          188130\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_14        436.157  190233             157.822     0.128808           190233\n",
+              "GBM_grid_1_AutoML_7_20250409_160125_model_9             437.171  191119             181.273     0.193966           191119\n",
+              "GBM_grid_1_AutoML_7_20250409_160125_model_10            445.206  198209             189.802     0.279025           198209\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_6         445.258  198255             175.764     0.11972            198255\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_22        454.969  206997             166.684     0.0899557          206997\n",
+              "DRF_1_AutoML_7_20250409_160125                          463.945  215245             157.149     0.0849622          215245\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_18        472.034  222816             181.679     0.295749           222816\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_5         474.409  225064             204.879     0.142061           225064\n",
+              "GBM_grid_1_AutoML_7_20250409_160125_model_18            476.677  227221             233.897     0.308515           227221\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_28        479.908  230311             167.367     0.0970178          230311\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_19        483.866  234126             171.465     0.0940305          234126\n",
+              "GBM_grid_1_AutoML_7_20250409_160125_model_22            485.57   235778             208.016     0.355136           235778\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_30        487.878  238025             192.236     0.218711           238025\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_26        498.399  248401             189.226     0.264324           248401\n",
+              "DeepLearning_grid_3_AutoML_7_20250409_160125_model_5    502.841  252849             283.578     0.449787           252849\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_9         512.179  262327             191.581     0.142146           262327\n",
+              "DeepLearning_grid_3_AutoML_7_20250409_160125_model_6    520.06   270463             314.781     0.559899           270463\n",
+              "DeepLearning_grid_3_AutoML_7_20250409_160125_model_3    534.976  286200             288.428     0.53734            286200\n",
+              "DeepLearning_grid_2_AutoML_7_20250409_160125_model_5    550.304  302834             359.712     0.646087           302834\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_32        562.155  316019             209.692     0.104479           316019\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_15        564.745  318937             198.665     0.132069           318937\n",
+              "GBM_grid_1_AutoML_7_20250409_160125_model_20            567.379  321919             230.968   nan                  321919\n",
+              "XGBoost_grid_1_AutoML_7_20250409_160125_model_21        580.055  336463             217.43      0.129691           336463\n",
+              "DeepLearning_grid_3_AutoML_7_20250409_160125_model_9    582.034  338763             322.652     0.346926           338763\n",
+              "GBM_grid_1_AutoML_7_20250409_160125_model_11            610.317  372487             277.712     0.330178           372487\n",
+              "GBM_grid_1_AutoML_7_20250409_160125_model_15            627.069  393215             361.711     0.377148           393215\n",
+              "GBM_grid_1_AutoML_7_20250409_160125_model_4             654.448  428303             230.922   nan                  428303\n",
+              "DeepLearning_grid_3_AutoML_7_20250409_160125_model_11   686.392  471134             390.937     0.619126           471134\n",
+              "DeepLearning_grid_2_AutoML_7_20250409_160125_model_2    729.073  531548             366.812     0.552833           531548\n",
+              "DeepLearning_grid_3_AutoML_7_20250409_160125_model_2    814.185  662896             434.196     0.49391            662896\n",
+              "GBM_grid_1_AutoML_7_20250409_160125_model_14           1139.42        1.29828e+06   510.838   nan                       1.29828e+06\n",
+              "GBM_grid_1_AutoML_7_20250409_160125_model_6            1147.89        1.31765e+06   474.836   nan                       1.31765e+06\n",
+              "GBM_grid_1_AutoML_7_20250409_160125_model_16           1152.12        1.32738e+06   474.541   nan                       1.32738e+06\n",
+              "GBM_1_AutoML_7_20250409_160125                         1164.63        1.35636e+06   464.474   nan                       1.35636e+06\n",
+              "GBM_grid_1_AutoML_7_20250409_160125_model_19           1170.14        1.36923e+06   486.197   nan                       1.36923e+06\n",
+              "GLM_1_AutoML_7_20250409_160125                         2474.66        6.12395e+06  1514.8       1.18928                 6.12395e+06\n",
+              "[100 rows x 6 columns]\n"
+            ],
+            "text/html": [
+              "<table class='dataframe'>\n",
+              "<thead>\n",
+              "<tr><th>model_id                                             </th><th style=\"text-align: right;\">    rmse</th><th style=\"text-align: right;\">             mse</th><th style=\"text-align: right;\">      mae</th><th style=\"text-align: right;\">      rmsle</th><th style=\"text-align: right;\">  mean_residual_deviance</th></tr>\n",
+              "</thead>\n",
+              "<tbody>\n",
+              "<tr><td>DeepLearning_grid_1_AutoML_7_20250409_160125_model_9 </td><td style=\"text-align: right;\"> 100.024</td><td style=\"text-align: right;\"> 10004.8        </td><td style=\"text-align: right;\">  64.5561</td><td style=\"text-align: right;\">  0.266664 </td><td style=\"text-align: right;\">         10004.8        </td></tr>\n",
+              "<tr><td>DeepLearning_grid_1_AutoML_7_20250409_160125_model_1 </td><td style=\"text-align: right;\"> 134.704</td><td style=\"text-align: right;\"> 18145.1        </td><td style=\"text-align: right;\">  90.0874</td><td style=\"text-align: right;\">  0.188638 </td><td style=\"text-align: right;\">         18145.1        </td></tr>\n",
+              "<tr><td>DeepLearning_grid_1_AutoML_7_20250409_160125_model_4 </td><td style=\"text-align: right;\"> 135.642</td><td style=\"text-align: right;\"> 18398.7        </td><td style=\"text-align: right;\"> 100.929 </td><td style=\"text-align: right;\">  0.194114 </td><td style=\"text-align: right;\">         18398.7        </td></tr>\n",
+              "<tr><td>DeepLearning_grid_1_AutoML_7_20250409_160125_model_10</td><td style=\"text-align: right;\"> 149.507</td><td style=\"text-align: right;\"> 22352.2        </td><td style=\"text-align: right;\">  89.8572</td><td style=\"text-align: right;\">nan        </td><td style=\"text-align: right;\">         22352.2        </td></tr>\n",
+              "<tr><td>DeepLearning_grid_1_AutoML_7_20250409_160125_model_5 </td><td style=\"text-align: right;\"> 164.876</td><td style=\"text-align: right;\"> 27184.1        </td><td style=\"text-align: right;\"> 113.692 </td><td style=\"text-align: right;\">  0.253921 </td><td style=\"text-align: right;\">         27184.1        </td></tr>\n",
+              "<tr><td>DeepLearning_grid_2_AutoML_7_20250409_160125_model_4 </td><td style=\"text-align: right;\"> 168.761</td><td style=\"text-align: right;\"> 28480.3        </td><td style=\"text-align: right;\"> 136.099 </td><td style=\"text-align: right;\">  0.283903 </td><td style=\"text-align: right;\">         28480.3        </td></tr>\n",
+              "<tr><td>DeepLearning_grid_1_AutoML_7_20250409_160125_model_2 </td><td style=\"text-align: right;\"> 188.361</td><td style=\"text-align: right;\"> 35479.9        </td><td style=\"text-align: right;\"> 138.995 </td><td style=\"text-align: right;\">nan        </td><td style=\"text-align: right;\">         35479.9        </td></tr>\n",
+              "<tr><td>DeepLearning_grid_2_AutoML_7_20250409_160125_model_10</td><td style=\"text-align: right;\"> 193.53 </td><td style=\"text-align: right;\"> 37453.7        </td><td style=\"text-align: right;\"> 139.606 </td><td style=\"text-align: right;\">nan        </td><td style=\"text-align: right;\">         37453.7        </td></tr>\n",
+              "<tr><td>DeepLearning_grid_3_AutoML_7_20250409_160125_model_4 </td><td style=\"text-align: right;\"> 203.619</td><td style=\"text-align: right;\"> 41460.6        </td><td style=\"text-align: right;\"> 118.388 </td><td style=\"text-align: right;\">  0.214535 </td><td style=\"text-align: right;\">         41460.6        </td></tr>\n",
+              "<tr><td>DeepLearning_grid_3_AutoML_7_20250409_160125_model_7 </td><td style=\"text-align: right;\"> 205.314</td><td style=\"text-align: right;\"> 42154          </td><td style=\"text-align: right;\"> 121.346 </td><td style=\"text-align: right;\">  0.22253  </td><td style=\"text-align: right;\">         42154          </td></tr>\n",
+              "<tr><td>DeepLearning_grid_3_AutoML_7_20250409_160125_model_1 </td><td style=\"text-align: right;\"> 207.528</td><td style=\"text-align: right;\"> 43067.9        </td><td style=\"text-align: right;\"> 116.164 </td><td style=\"text-align: right;\">  0.314633 </td><td style=\"text-align: right;\">         43067.9        </td></tr>\n",
+              "<tr><td>DeepLearning_grid_3_AutoML_7_20250409_160125_model_10</td><td style=\"text-align: right;\"> 215.478</td><td style=\"text-align: right;\"> 46430.7        </td><td style=\"text-align: right;\"> 114.794 </td><td style=\"text-align: right;\">  0.193609 </td><td style=\"text-align: right;\">         46430.7        </td></tr>\n",
+              "<tr><td>DeepLearning_grid_1_AutoML_7_20250409_160125_model_7 </td><td style=\"text-align: right;\"> 217.389</td><td style=\"text-align: right;\"> 47258          </td><td style=\"text-align: right;\"> 147.873 </td><td style=\"text-align: right;\">nan        </td><td style=\"text-align: right;\">         47258          </td></tr>\n",
+              "<tr><td>DeepLearning_grid_1_AutoML_7_20250409_160125_model_6 </td><td style=\"text-align: right;\"> 221.629</td><td style=\"text-align: right;\"> 49119.5        </td><td style=\"text-align: right;\"> 144.504 </td><td style=\"text-align: right;\">  0.25002  </td><td style=\"text-align: right;\">         49119.5        </td></tr>\n",
+              "<tr><td>DeepLearning_grid_2_AutoML_7_20250409_160125_model_1 </td><td style=\"text-align: right;\"> 235.728</td><td style=\"text-align: right;\"> 55567.5        </td><td style=\"text-align: right;\"> 186.976 </td><td style=\"text-align: right;\">  0.349211 </td><td style=\"text-align: right;\">         55567.5        </td></tr>\n",
+              "<tr><td>DeepLearning_grid_2_AutoML_7_20250409_160125_model_7 </td><td style=\"text-align: right;\"> 248.525</td><td style=\"text-align: right;\"> 61764.6        </td><td style=\"text-align: right;\"> 181.495 </td><td style=\"text-align: right;\">nan        </td><td style=\"text-align: right;\">         61764.6        </td></tr>\n",
+              "<tr><td>DeepLearning_grid_1_AutoML_7_20250409_160125_model_3 </td><td style=\"text-align: right;\"> 257.242</td><td style=\"text-align: right;\"> 66173.4        </td><td style=\"text-align: right;\"> 169.275 </td><td style=\"text-align: right;\">nan        </td><td style=\"text-align: right;\">         66173.4        </td></tr>\n",
+              "<tr><td>DeepLearning_grid_1_AutoML_7_20250409_160125_model_11</td><td style=\"text-align: right;\"> 257.672</td><td style=\"text-align: right;\"> 66395.1        </td><td style=\"text-align: right;\"> 161.105 </td><td style=\"text-align: right;\">nan        </td><td style=\"text-align: right;\">         66395.1        </td></tr>\n",
+              "<tr><td>DeepLearning_grid_3_AutoML_7_20250409_160125_model_8 </td><td style=\"text-align: right;\"> 275.723</td><td style=\"text-align: right;\"> 76023          </td><td style=\"text-align: right;\"> 186.465 </td><td style=\"text-align: right;\">nan        </td><td style=\"text-align: right;\">         76023          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_8      </td><td style=\"text-align: right;\"> 322.575</td><td style=\"text-align: right;\">104055          </td><td style=\"text-align: right;\"> 118.816 </td><td style=\"text-align: right;\">  0.061503 </td><td style=\"text-align: right;\">        104055          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_13     </td><td style=\"text-align: right;\"> 323.824</td><td style=\"text-align: right;\">104862          </td><td style=\"text-align: right;\"> 122.203 </td><td style=\"text-align: right;\">  0.0652133</td><td style=\"text-align: right;\">        104862          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_23     </td><td style=\"text-align: right;\"> 334.453</td><td style=\"text-align: right;\">111859          </td><td style=\"text-align: right;\"> 119.161 </td><td style=\"text-align: right;\">  0.0863272</td><td style=\"text-align: right;\">        111859          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_31     </td><td style=\"text-align: right;\"> 343.315</td><td style=\"text-align: right;\">117865          </td><td style=\"text-align: right;\"> 126.802 </td><td style=\"text-align: right;\">  0.102759 </td><td style=\"text-align: right;\">        117865          </td></tr>\n",
+              "<tr><td>DeepLearning_1_AutoML_7_20250409_160125              </td><td style=\"text-align: right;\"> 345.776</td><td style=\"text-align: right;\">119561          </td><td style=\"text-align: right;\"> 180.892 </td><td style=\"text-align: right;\">nan        </td><td style=\"text-align: right;\">        119561          </td></tr>\n",
+              "<tr><td>DeepLearning_grid_2_AutoML_7_20250409_160125_model_6 </td><td style=\"text-align: right;\"> 350.948</td><td style=\"text-align: right;\">123165          </td><td style=\"text-align: right;\"> 253.962 </td><td style=\"text-align: right;\">  0.571391 </td><td style=\"text-align: right;\">        123165          </td></tr>\n",
+              "<tr><td>DeepLearning_grid_1_AutoML_7_20250409_160125_model_8 </td><td style=\"text-align: right;\"> 352.587</td><td style=\"text-align: right;\">124317          </td><td style=\"text-align: right;\"> 263.534 </td><td style=\"text-align: right;\">nan        </td><td style=\"text-align: right;\">        124317          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_27     </td><td style=\"text-align: right;\"> 352.811</td><td style=\"text-align: right;\">124476          </td><td style=\"text-align: right;\"> 145.251 </td><td style=\"text-align: right;\">  0.0781429</td><td style=\"text-align: right;\">        124476          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_25     </td><td style=\"text-align: right;\"> 354.925</td><td style=\"text-align: right;\">125972          </td><td style=\"text-align: right;\"> 126.807 </td><td style=\"text-align: right;\">  0.098505 </td><td style=\"text-align: right;\">        125972          </td></tr>\n",
+              "<tr><td>XGBoost_3_AutoML_7_20250409_160125                   </td><td style=\"text-align: right;\"> 356.462</td><td style=\"text-align: right;\">127065          </td><td style=\"text-align: right;\"> 133.239 </td><td style=\"text-align: right;\">  0.0937253</td><td style=\"text-align: right;\">        127065          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_24     </td><td style=\"text-align: right;\"> 361.243</td><td style=\"text-align: right;\">130496          </td><td style=\"text-align: right;\"> 149.89  </td><td style=\"text-align: right;\">  0.0855626</td><td style=\"text-align: right;\">        130496          </td></tr>\n",
+              "<tr><td>DeepLearning_grid_2_AutoML_7_20250409_160125_model_9 </td><td style=\"text-align: right;\"> 362.184</td><td style=\"text-align: right;\">131177          </td><td style=\"text-align: right;\"> 213.631 </td><td style=\"text-align: right;\">  0.30945  </td><td style=\"text-align: right;\">        131177          </td></tr>\n",
+              "<tr><td>GBM_5_AutoML_7_20250409_160125                       </td><td style=\"text-align: right;\"> 366.99 </td><td style=\"text-align: right;\">134682          </td><td style=\"text-align: right;\"> 121.127 </td><td style=\"text-align: right;\">  0.0585085</td><td style=\"text-align: right;\">        134682          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_10     </td><td style=\"text-align: right;\"> 371.605</td><td style=\"text-align: right;\">138090          </td><td style=\"text-align: right;\"> 124.646 </td><td style=\"text-align: right;\">  0.0737216</td><td style=\"text-align: right;\">        138090          </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_7_20250409_160125_model_12         </td><td style=\"text-align: right;\"> 372.355</td><td style=\"text-align: right;\">138648          </td><td style=\"text-align: right;\"> 112.975 </td><td style=\"text-align: right;\">  0.0578716</td><td style=\"text-align: right;\">        138648          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_29     </td><td style=\"text-align: right;\"> 373.223</td><td style=\"text-align: right;\">139296          </td><td style=\"text-align: right;\"> 133.716 </td><td style=\"text-align: right;\">  0.0896737</td><td style=\"text-align: right;\">        139296          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_2      </td><td style=\"text-align: right;\"> 373.411</td><td style=\"text-align: right;\">139436          </td><td style=\"text-align: right;\"> 154.186 </td><td style=\"text-align: right;\">  0.0982836</td><td style=\"text-align: right;\">        139436          </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_7_20250409_160125_model_21         </td><td style=\"text-align: right;\"> 373.766</td><td style=\"text-align: right;\">139701          </td><td style=\"text-align: right;\"> 136.83  </td><td style=\"text-align: right;\">  0.0625747</td><td style=\"text-align: right;\">        139701          </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_7_20250409_160125_model_2          </td><td style=\"text-align: right;\"> 373.793</td><td style=\"text-align: right;\">139721          </td><td style=\"text-align: right;\"> 131.995 </td><td style=\"text-align: right;\">  0.0843552</td><td style=\"text-align: right;\">        139721          </td></tr>\n",
+              "<tr><td>XGBoost_2_AutoML_7_20250409_160125                   </td><td style=\"text-align: right;\"> 381.087</td><td style=\"text-align: right;\">145227          </td><td style=\"text-align: right;\"> 138.912 </td><td style=\"text-align: right;\">  0.0829641</td><td style=\"text-align: right;\">        145227          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_3      </td><td style=\"text-align: right;\"> 381.373</td><td style=\"text-align: right;\">145445          </td><td style=\"text-align: right;\"> 121.161 </td><td style=\"text-align: right;\">  0.0683393</td><td style=\"text-align: right;\">        145445          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_4      </td><td style=\"text-align: right;\"> 382.775</td><td style=\"text-align: right;\">146517          </td><td style=\"text-align: right;\"> 132.798 </td><td style=\"text-align: right;\">  0.0945484</td><td style=\"text-align: right;\">        146517          </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_7_20250409_160125_model_13         </td><td style=\"text-align: right;\"> 384.045</td><td style=\"text-align: right;\">147490          </td><td style=\"text-align: right;\"> 146.04  </td><td style=\"text-align: right;\">  0.0883831</td><td style=\"text-align: right;\">        147490          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_33     </td><td style=\"text-align: right;\"> 386.962</td><td style=\"text-align: right;\">149739          </td><td style=\"text-align: right;\"> 131.287 </td><td style=\"text-align: right;\">  0.0720799</td><td style=\"text-align: right;\">        149739          </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_7_20250409_160125_model_8          </td><td style=\"text-align: right;\"> 387.56 </td><td style=\"text-align: right;\">150202          </td><td style=\"text-align: right;\"> 144.694 </td><td style=\"text-align: right;\">  0.107274 </td><td style=\"text-align: right;\">        150202          </td></tr>\n",
+              "<tr><td>DeepLearning_grid_2_AutoML_7_20250409_160125_model_8 </td><td style=\"text-align: right;\"> 389.366</td><td style=\"text-align: right;\">151606          </td><td style=\"text-align: right;\"> 288.426 </td><td style=\"text-align: right;\">nan        </td><td style=\"text-align: right;\">        151606          </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_7_20250409_160125_model_17         </td><td style=\"text-align: right;\"> 392.039</td><td style=\"text-align: right;\">153694          </td><td style=\"text-align: right;\"> 143.235 </td><td style=\"text-align: right;\">  0.114527 </td><td style=\"text-align: right;\">        153694          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_12     </td><td style=\"text-align: right;\"> 394.288</td><td style=\"text-align: right;\">155463          </td><td style=\"text-align: right;\"> 129.674 </td><td style=\"text-align: right;\">  0.0695782</td><td style=\"text-align: right;\">        155463          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_16     </td><td style=\"text-align: right;\"> 395.37 </td><td style=\"text-align: right;\">156318          </td><td style=\"text-align: right;\"> 178.41  </td><td style=\"text-align: right;\">  0.202227 </td><td style=\"text-align: right;\">        156318          </td></tr>\n",
+              "<tr><td>DeepLearning_grid_2_AutoML_7_20250409_160125_model_3 </td><td style=\"text-align: right;\"> 398.281</td><td style=\"text-align: right;\">158628          </td><td style=\"text-align: right;\"> 223.427 </td><td style=\"text-align: right;\">  0.281647 </td><td style=\"text-align: right;\">        158628          </td></tr>\n",
+              "<tr><td>GBM_4_AutoML_7_20250409_160125                       </td><td style=\"text-align: right;\"> 398.305</td><td style=\"text-align: right;\">158647          </td><td style=\"text-align: right;\"> 123.528 </td><td style=\"text-align: right;\">  0.0877705</td><td style=\"text-align: right;\">        158647          </td></tr>\n",
+              "<tr><td>GBM_3_AutoML_7_20250409_160125                       </td><td style=\"text-align: right;\"> 403.436</td><td style=\"text-align: right;\">162761          </td><td style=\"text-align: right;\"> 127.608 </td><td style=\"text-align: right;\">  0.0824873</td><td style=\"text-align: right;\">        162761          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_17     </td><td style=\"text-align: right;\"> 403.476</td><td style=\"text-align: right;\">162793          </td><td style=\"text-align: right;\"> 131.504 </td><td style=\"text-align: right;\">  0.0816832</td><td style=\"text-align: right;\">        162793          </td></tr>\n",
+              "<tr><td>GBM_2_AutoML_7_20250409_160125                       </td><td style=\"text-align: right;\"> 403.593</td><td style=\"text-align: right;\">162887          </td><td style=\"text-align: right;\"> 122.134 </td><td style=\"text-align: right;\">  0.074959 </td><td style=\"text-align: right;\">        162887          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_1      </td><td style=\"text-align: right;\"> 409.332</td><td style=\"text-align: right;\">167553          </td><td style=\"text-align: right;\"> 165.164 </td><td style=\"text-align: right;\">  0.0996374</td><td style=\"text-align: right;\">        167553          </td></tr>\n",
+              "<tr><td>XGBoost_1_AutoML_7_20250409_160125                   </td><td style=\"text-align: right;\"> 410.198</td><td style=\"text-align: right;\">168263          </td><td style=\"text-align: right;\"> 166.545 </td><td style=\"text-align: right;\">  0.103922 </td><td style=\"text-align: right;\">        168263          </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_7_20250409_160125_model_3          </td><td style=\"text-align: right;\"> 414.628</td><td style=\"text-align: right;\">171916          </td><td style=\"text-align: right;\"> 139.648 </td><td style=\"text-align: right;\">  0.116314 </td><td style=\"text-align: right;\">        171916          </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_7_20250409_160125_model_1          </td><td style=\"text-align: right;\"> 415.403</td><td style=\"text-align: right;\">172560          </td><td style=\"text-align: right;\"> 150.605 </td><td style=\"text-align: right;\">  0.156217 </td><td style=\"text-align: right;\">        172560          </td></tr>\n",
+              "<tr><td>DeepLearning_grid_2_AutoML_7_20250409_160125_model_11</td><td style=\"text-align: right;\"> 417.624</td><td style=\"text-align: right;\">174410          </td><td style=\"text-align: right;\"> 265.895 </td><td style=\"text-align: right;\">  0.475627 </td><td style=\"text-align: right;\">        174410          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_7      </td><td style=\"text-align: right;\"> 423.496</td><td style=\"text-align: right;\">179349          </td><td style=\"text-align: right;\"> 156.584 </td><td style=\"text-align: right;\">  0.1058   </td><td style=\"text-align: right;\">        179349          </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_7_20250409_160125_model_5          </td><td style=\"text-align: right;\"> 425.309</td><td style=\"text-align: right;\">180887          </td><td style=\"text-align: right;\"> 139.146 </td><td style=\"text-align: right;\">  0.0773054</td><td style=\"text-align: right;\">        180887          </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_7_20250409_160125_model_7          </td><td style=\"text-align: right;\"> 427.33 </td><td style=\"text-align: right;\">182611          </td><td style=\"text-align: right;\"> 181.637 </td><td style=\"text-align: right;\">  0.159149 </td><td style=\"text-align: right;\">        182611          </td></tr>\n",
+              "<tr><td>XRT_1_AutoML_7_20250409_160125                       </td><td style=\"text-align: right;\"> 429.632</td><td style=\"text-align: right;\">184583          </td><td style=\"text-align: right;\"> 148.454 </td><td style=\"text-align: right;\">  0.0765111</td><td style=\"text-align: right;\">        184583          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_20     </td><td style=\"text-align: right;\"> 430.564</td><td style=\"text-align: right;\">185386          </td><td style=\"text-align: right;\"> 131.065 </td><td style=\"text-align: right;\">  0.0865655</td><td style=\"text-align: right;\">        185386          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_11     </td><td style=\"text-align: right;\"> 433.739</td><td style=\"text-align: right;\">188130          </td><td style=\"text-align: right;\"> 147.004 </td><td style=\"text-align: right;\">  0.0764827</td><td style=\"text-align: right;\">        188130          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_14     </td><td style=\"text-align: right;\"> 436.157</td><td style=\"text-align: right;\">190233          </td><td style=\"text-align: right;\"> 157.822 </td><td style=\"text-align: right;\">  0.128808 </td><td style=\"text-align: right;\">        190233          </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_7_20250409_160125_model_9          </td><td style=\"text-align: right;\"> 437.171</td><td style=\"text-align: right;\">191119          </td><td style=\"text-align: right;\"> 181.273 </td><td style=\"text-align: right;\">  0.193966 </td><td style=\"text-align: right;\">        191119          </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_7_20250409_160125_model_10         </td><td style=\"text-align: right;\"> 445.206</td><td style=\"text-align: right;\">198209          </td><td style=\"text-align: right;\"> 189.802 </td><td style=\"text-align: right;\">  0.279025 </td><td style=\"text-align: right;\">        198209          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_6      </td><td style=\"text-align: right;\"> 445.258</td><td style=\"text-align: right;\">198255          </td><td style=\"text-align: right;\"> 175.764 </td><td style=\"text-align: right;\">  0.11972  </td><td style=\"text-align: right;\">        198255          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_22     </td><td style=\"text-align: right;\"> 454.969</td><td style=\"text-align: right;\">206997          </td><td style=\"text-align: right;\"> 166.684 </td><td style=\"text-align: right;\">  0.0899557</td><td style=\"text-align: right;\">        206997          </td></tr>\n",
+              "<tr><td>DRF_1_AutoML_7_20250409_160125                       </td><td style=\"text-align: right;\"> 463.945</td><td style=\"text-align: right;\">215245          </td><td style=\"text-align: right;\"> 157.149 </td><td style=\"text-align: right;\">  0.0849622</td><td style=\"text-align: right;\">        215245          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_18     </td><td style=\"text-align: right;\"> 472.034</td><td style=\"text-align: right;\">222816          </td><td style=\"text-align: right;\"> 181.679 </td><td style=\"text-align: right;\">  0.295749 </td><td style=\"text-align: right;\">        222816          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_5      </td><td style=\"text-align: right;\"> 474.409</td><td style=\"text-align: right;\">225064          </td><td style=\"text-align: right;\"> 204.879 </td><td style=\"text-align: right;\">  0.142061 </td><td style=\"text-align: right;\">        225064          </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_7_20250409_160125_model_18         </td><td style=\"text-align: right;\"> 476.677</td><td style=\"text-align: right;\">227221          </td><td style=\"text-align: right;\"> 233.897 </td><td style=\"text-align: right;\">  0.308515 </td><td style=\"text-align: right;\">        227221          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_28     </td><td style=\"text-align: right;\"> 479.908</td><td style=\"text-align: right;\">230311          </td><td style=\"text-align: right;\"> 167.367 </td><td style=\"text-align: right;\">  0.0970178</td><td style=\"text-align: right;\">        230311          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_19     </td><td style=\"text-align: right;\"> 483.866</td><td style=\"text-align: right;\">234126          </td><td style=\"text-align: right;\"> 171.465 </td><td style=\"text-align: right;\">  0.0940305</td><td style=\"text-align: right;\">        234126          </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_7_20250409_160125_model_22         </td><td style=\"text-align: right;\"> 485.57 </td><td style=\"text-align: right;\">235778          </td><td style=\"text-align: right;\"> 208.016 </td><td style=\"text-align: right;\">  0.355136 </td><td style=\"text-align: right;\">        235778          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_30     </td><td style=\"text-align: right;\"> 487.878</td><td style=\"text-align: right;\">238025          </td><td style=\"text-align: right;\"> 192.236 </td><td style=\"text-align: right;\">  0.218711 </td><td style=\"text-align: right;\">        238025          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_26     </td><td style=\"text-align: right;\"> 498.399</td><td style=\"text-align: right;\">248401          </td><td style=\"text-align: right;\"> 189.226 </td><td style=\"text-align: right;\">  0.264324 </td><td style=\"text-align: right;\">        248401          </td></tr>\n",
+              "<tr><td>DeepLearning_grid_3_AutoML_7_20250409_160125_model_5 </td><td style=\"text-align: right;\"> 502.841</td><td style=\"text-align: right;\">252849          </td><td style=\"text-align: right;\"> 283.578 </td><td style=\"text-align: right;\">  0.449787 </td><td style=\"text-align: right;\">        252849          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_9      </td><td style=\"text-align: right;\"> 512.179</td><td style=\"text-align: right;\">262327          </td><td style=\"text-align: right;\"> 191.581 </td><td style=\"text-align: right;\">  0.142146 </td><td style=\"text-align: right;\">        262327          </td></tr>\n",
+              "<tr><td>DeepLearning_grid_3_AutoML_7_20250409_160125_model_6 </td><td style=\"text-align: right;\"> 520.06 </td><td style=\"text-align: right;\">270463          </td><td style=\"text-align: right;\"> 314.781 </td><td style=\"text-align: right;\">  0.559899 </td><td style=\"text-align: right;\">        270463          </td></tr>\n",
+              "<tr><td>DeepLearning_grid_3_AutoML_7_20250409_160125_model_3 </td><td style=\"text-align: right;\"> 534.976</td><td style=\"text-align: right;\">286200          </td><td style=\"text-align: right;\"> 288.428 </td><td style=\"text-align: right;\">  0.53734  </td><td style=\"text-align: right;\">        286200          </td></tr>\n",
+              "<tr><td>DeepLearning_grid_2_AutoML_7_20250409_160125_model_5 </td><td style=\"text-align: right;\"> 550.304</td><td style=\"text-align: right;\">302834          </td><td style=\"text-align: right;\"> 359.712 </td><td style=\"text-align: right;\">  0.646087 </td><td style=\"text-align: right;\">        302834          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_32     </td><td style=\"text-align: right;\"> 562.155</td><td style=\"text-align: right;\">316019          </td><td style=\"text-align: right;\"> 209.692 </td><td style=\"text-align: right;\">  0.104479 </td><td style=\"text-align: right;\">        316019          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_15     </td><td style=\"text-align: right;\"> 564.745</td><td style=\"text-align: right;\">318937          </td><td style=\"text-align: right;\"> 198.665 </td><td style=\"text-align: right;\">  0.132069 </td><td style=\"text-align: right;\">        318937          </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_7_20250409_160125_model_20         </td><td style=\"text-align: right;\"> 567.379</td><td style=\"text-align: right;\">321919          </td><td style=\"text-align: right;\"> 230.968 </td><td style=\"text-align: right;\">nan        </td><td style=\"text-align: right;\">        321919          </td></tr>\n",
+              "<tr><td>XGBoost_grid_1_AutoML_7_20250409_160125_model_21     </td><td style=\"text-align: right;\"> 580.055</td><td style=\"text-align: right;\">336463          </td><td style=\"text-align: right;\"> 217.43  </td><td style=\"text-align: right;\">  0.129691 </td><td style=\"text-align: right;\">        336463          </td></tr>\n",
+              "<tr><td>DeepLearning_grid_3_AutoML_7_20250409_160125_model_9 </td><td style=\"text-align: right;\"> 582.034</td><td style=\"text-align: right;\">338763          </td><td style=\"text-align: right;\"> 322.652 </td><td style=\"text-align: right;\">  0.346926 </td><td style=\"text-align: right;\">        338763          </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_7_20250409_160125_model_11         </td><td style=\"text-align: right;\"> 610.317</td><td style=\"text-align: right;\">372487          </td><td style=\"text-align: right;\"> 277.712 </td><td style=\"text-align: right;\">  0.330178 </td><td style=\"text-align: right;\">        372487          </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_7_20250409_160125_model_15         </td><td style=\"text-align: right;\"> 627.069</td><td style=\"text-align: right;\">393215          </td><td style=\"text-align: right;\"> 361.711 </td><td style=\"text-align: right;\">  0.377148 </td><td style=\"text-align: right;\">        393215          </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_7_20250409_160125_model_4          </td><td style=\"text-align: right;\"> 654.448</td><td style=\"text-align: right;\">428303          </td><td style=\"text-align: right;\"> 230.922 </td><td style=\"text-align: right;\">nan        </td><td style=\"text-align: right;\">        428303          </td></tr>\n",
+              "<tr><td>DeepLearning_grid_3_AutoML_7_20250409_160125_model_11</td><td style=\"text-align: right;\"> 686.392</td><td style=\"text-align: right;\">471134          </td><td style=\"text-align: right;\"> 390.937 </td><td style=\"text-align: right;\">  0.619126 </td><td style=\"text-align: right;\">        471134          </td></tr>\n",
+              "<tr><td>DeepLearning_grid_2_AutoML_7_20250409_160125_model_2 </td><td style=\"text-align: right;\"> 729.073</td><td style=\"text-align: right;\">531548          </td><td style=\"text-align: right;\"> 366.812 </td><td style=\"text-align: right;\">  0.552833 </td><td style=\"text-align: right;\">        531548          </td></tr>\n",
+              "<tr><td>DeepLearning_grid_3_AutoML_7_20250409_160125_model_2 </td><td style=\"text-align: right;\"> 814.185</td><td style=\"text-align: right;\">662896          </td><td style=\"text-align: right;\"> 434.196 </td><td style=\"text-align: right;\">  0.49391  </td><td style=\"text-align: right;\">        662896          </td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_7_20250409_160125_model_14         </td><td style=\"text-align: right;\">1139.42 </td><td style=\"text-align: right;\">     1.29828e+06</td><td style=\"text-align: right;\"> 510.838 </td><td style=\"text-align: right;\">nan        </td><td style=\"text-align: right;\">             1.29828e+06</td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_7_20250409_160125_model_6          </td><td style=\"text-align: right;\">1147.89 </td><td style=\"text-align: right;\">     1.31765e+06</td><td style=\"text-align: right;\"> 474.836 </td><td style=\"text-align: right;\">nan        </td><td style=\"text-align: right;\">             1.31765e+06</td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_7_20250409_160125_model_16         </td><td style=\"text-align: right;\">1152.12 </td><td style=\"text-align: right;\">     1.32738e+06</td><td style=\"text-align: right;\"> 474.541 </td><td style=\"text-align: right;\">nan        </td><td style=\"text-align: right;\">             1.32738e+06</td></tr>\n",
+              "<tr><td>GBM_1_AutoML_7_20250409_160125                       </td><td style=\"text-align: right;\">1164.63 </td><td style=\"text-align: right;\">     1.35636e+06</td><td style=\"text-align: right;\"> 464.474 </td><td style=\"text-align: right;\">nan        </td><td style=\"text-align: right;\">             1.35636e+06</td></tr>\n",
+              "<tr><td>GBM_grid_1_AutoML_7_20250409_160125_model_19         </td><td style=\"text-align: right;\">1170.14 </td><td style=\"text-align: right;\">     1.36923e+06</td><td style=\"text-align: right;\"> 486.197 </td><td style=\"text-align: right;\">nan        </td><td style=\"text-align: right;\">             1.36923e+06</td></tr>\n",
+              "<tr><td>GLM_1_AutoML_7_20250409_160125                       </td><td style=\"text-align: right;\">2474.66 </td><td style=\"text-align: right;\">     6.12395e+06</td><td style=\"text-align: right;\">1514.8   </td><td style=\"text-align: right;\">  1.18928  </td><td style=\"text-align: right;\">             6.12395e+06</td></tr>\n",
+              "</tbody>\n",
+              "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[100 rows x 6 columns]</pre>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 71
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "lb = h2o.automl.get_leaderboard(aml, extra_columns = \"ALL\")\n",
+        "lb"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 409
+        },
+        "id": "3iX8Rw16O86m",
+        "outputId": "eba966d5-7bd2-44bd-c3d3-1337ce882b7b"
+      },
+      "execution_count": 72,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "model_id                                                  rmse      mse       mae       rmsle    mean_residual_deviance    training_time_ms    predict_time_per_row_ms  algo\n",
+              "-----------------------------------------------------  -------  -------  --------  ----------  ------------------------  ------------------  -------------------------  ------------\n",
+              "DeepLearning_grid_1_AutoML_7_20250409_160125_model_9   100.024  10004.8   64.5561    0.266664                   10004.8               40893                   0.051003  DeepLearning\n",
+              "DeepLearning_grid_1_AutoML_7_20250409_160125_model_1   134.704  18145.1   90.0874    0.188638                   18145.1               43225                   0.067006  DeepLearning\n",
+              "DeepLearning_grid_1_AutoML_7_20250409_160125_model_4   135.642  18398.7  100.929     0.194114                   18398.7              102919                   0.063598  DeepLearning\n",
+              "DeepLearning_grid_1_AutoML_7_20250409_160125_model_10  149.507  22352.2   89.8572  nan                          22352.2               40231                   0.054317  DeepLearning\n",
+              "DeepLearning_grid_1_AutoML_7_20250409_160125_model_5   164.876  27184.1  113.692     0.253921                   27184.1               31895                   0.075369  DeepLearning\n",
+              "DeepLearning_grid_2_AutoML_7_20250409_160125_model_4   168.761  28480.3  136.099     0.283903                   28480.3               59139                   0.085656  DeepLearning\n",
+              "DeepLearning_grid_1_AutoML_7_20250409_160125_model_2   188.361  35479.9  138.995   nan                          35479.9               46488                   0.038126  DeepLearning\n",
+              "DeepLearning_grid_2_AutoML_7_20250409_160125_model_10  193.53   37453.7  139.606   nan                          37453.7               36671                   0.066524  DeepLearning\n",
+              "DeepLearning_grid_3_AutoML_7_20250409_160125_model_4   203.619  41460.6  118.388     0.214535                   41460.6               62840                   0.079879  DeepLearning\n",
+              "DeepLearning_grid_3_AutoML_7_20250409_160125_model_7   205.314  42154    121.346     0.22253                    42154                106315                   0.082181  DeepLearning\n",
+              "[100 rows x 9 columns]\n"
+            ],
+            "text/html": [
+              "<table class='dataframe'>\n",
+              "<thead>\n",
+              "<tr><th>model_id                                             </th><th style=\"text-align: right;\">   rmse</th><th style=\"text-align: right;\">    mse</th><th style=\"text-align: right;\">     mae</th><th style=\"text-align: right;\">     rmsle</th><th style=\"text-align: right;\">  mean_residual_deviance</th><th style=\"text-align: right;\">  training_time_ms</th><th style=\"text-align: right;\">  predict_time_per_row_ms</th><th>algo        </th></tr>\n",
+              "</thead>\n",
+              "<tbody>\n",
+              "<tr><td>DeepLearning_grid_1_AutoML_7_20250409_160125_model_9 </td><td style=\"text-align: right;\">100.024</td><td style=\"text-align: right;\">10004.8</td><td style=\"text-align: right;\"> 64.5561</td><td style=\"text-align: right;\">  0.266664</td><td style=\"text-align: right;\">                 10004.8</td><td style=\"text-align: right;\">             40893</td><td style=\"text-align: right;\">                 0.051003</td><td>DeepLearning</td></tr>\n",
+              "<tr><td>DeepLearning_grid_1_AutoML_7_20250409_160125_model_1 </td><td style=\"text-align: right;\">134.704</td><td style=\"text-align: right;\">18145.1</td><td style=\"text-align: right;\"> 90.0874</td><td style=\"text-align: right;\">  0.188638</td><td style=\"text-align: right;\">                 18145.1</td><td style=\"text-align: right;\">             43225</td><td style=\"text-align: right;\">                 0.067006</td><td>DeepLearning</td></tr>\n",
+              "<tr><td>DeepLearning_grid_1_AutoML_7_20250409_160125_model_4 </td><td style=\"text-align: right;\">135.642</td><td style=\"text-align: right;\">18398.7</td><td style=\"text-align: right;\">100.929 </td><td style=\"text-align: right;\">  0.194114</td><td style=\"text-align: right;\">                 18398.7</td><td style=\"text-align: right;\">            102919</td><td style=\"text-align: right;\">                 0.063598</td><td>DeepLearning</td></tr>\n",
+              "<tr><td>DeepLearning_grid_1_AutoML_7_20250409_160125_model_10</td><td style=\"text-align: right;\">149.507</td><td style=\"text-align: right;\">22352.2</td><td style=\"text-align: right;\"> 89.8572</td><td style=\"text-align: right;\">nan       </td><td style=\"text-align: right;\">                 22352.2</td><td style=\"text-align: right;\">             40231</td><td style=\"text-align: right;\">                 0.054317</td><td>DeepLearning</td></tr>\n",
+              "<tr><td>DeepLearning_grid_1_AutoML_7_20250409_160125_model_5 </td><td style=\"text-align: right;\">164.876</td><td style=\"text-align: right;\">27184.1</td><td style=\"text-align: right;\">113.692 </td><td style=\"text-align: right;\">  0.253921</td><td style=\"text-align: right;\">                 27184.1</td><td style=\"text-align: right;\">             31895</td><td style=\"text-align: right;\">                 0.075369</td><td>DeepLearning</td></tr>\n",
+              "<tr><td>DeepLearning_grid_2_AutoML_7_20250409_160125_model_4 </td><td style=\"text-align: right;\">168.761</td><td style=\"text-align: right;\">28480.3</td><td style=\"text-align: right;\">136.099 </td><td style=\"text-align: right;\">  0.283903</td><td style=\"text-align: right;\">                 28480.3</td><td style=\"text-align: right;\">             59139</td><td style=\"text-align: right;\">                 0.085656</td><td>DeepLearning</td></tr>\n",
+              "<tr><td>DeepLearning_grid_1_AutoML_7_20250409_160125_model_2 </td><td style=\"text-align: right;\">188.361</td><td style=\"text-align: right;\">35479.9</td><td style=\"text-align: right;\">138.995 </td><td style=\"text-align: right;\">nan       </td><td style=\"text-align: right;\">                 35479.9</td><td style=\"text-align: right;\">             46488</td><td style=\"text-align: right;\">                 0.038126</td><td>DeepLearning</td></tr>\n",
+              "<tr><td>DeepLearning_grid_2_AutoML_7_20250409_160125_model_10</td><td style=\"text-align: right;\">193.53 </td><td style=\"text-align: right;\">37453.7</td><td style=\"text-align: right;\">139.606 </td><td style=\"text-align: right;\">nan       </td><td style=\"text-align: right;\">                 37453.7</td><td style=\"text-align: right;\">             36671</td><td style=\"text-align: right;\">                 0.066524</td><td>DeepLearning</td></tr>\n",
+              "<tr><td>DeepLearning_grid_3_AutoML_7_20250409_160125_model_4 </td><td style=\"text-align: right;\">203.619</td><td style=\"text-align: right;\">41460.6</td><td style=\"text-align: right;\">118.388 </td><td style=\"text-align: right;\">  0.214535</td><td style=\"text-align: right;\">                 41460.6</td><td style=\"text-align: right;\">             62840</td><td style=\"text-align: right;\">                 0.079879</td><td>DeepLearning</td></tr>\n",
+              "<tr><td>DeepLearning_grid_3_AutoML_7_20250409_160125_model_7 </td><td style=\"text-align: right;\">205.314</td><td style=\"text-align: right;\">42154  </td><td style=\"text-align: right;\">121.346 </td><td style=\"text-align: right;\">  0.22253 </td><td style=\"text-align: right;\">                 42154  </td><td style=\"text-align: right;\">            106315</td><td style=\"text-align: right;\">                 0.082181</td><td>DeepLearning</td></tr>\n",
+              "</tbody>\n",
+              "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[100 rows x 9 columns]</pre>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 72
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Get the best model using the metric\n",
+        "m = aml.leader\n",
+        "print(m)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0A80RCfVPsA3",
+        "outputId": "ea11fb9e-ceaf-47f3-bb95-4ce73eb45881"
+      },
+      "execution_count": 73,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model Details\n",
+            "=============\n",
+            "H2ODeepLearningEstimator : Deep Learning\n",
+            "Model Key: DeepLearning_grid_1_AutoML_7_20250409_160125_model_9\n",
+            "\n",
+            "\n",
+            "Status of Neuron Layers: predicting RESULT DATE+1 open price, regression, gaussian distribution, Quadratic loss, 5,661 weights/biases, 81.9 KB, 2,254,720 training samples, mini-batch size 1\n",
+            "    layer    units    type              dropout    l1    l2    mean_rate             rate_rms              momentum    mean_weight            weight_rms           mean_bias            bias_rms\n",
+            "--  -------  -------  ----------------  ---------  ----  ----  --------------------  --------------------  ----------  ---------------------  -------------------  -------------------  -----------------------\n",
+            "    1        281      Input             0.0\n",
+            "    2        20       RectifierDropout  10.0       0.0   0.0   0.1224201583713347    0.24743318557739258   0.0         -0.003040129768628973  0.10864239931106567  0.31249810220787977  0.49442625045776367\n",
+            "    3        1        Linear                       0.0   0.0   0.003484321317228023  0.004025295376777649  0.0         -0.004980345163494349  0.08620524406433105  -0.3716514757246579  1.0971281125650402e-154\n",
+            "\n",
+            "ModelMetricsRegression: deeplearning\n",
+            "** Reported on train data. **\n",
+            "\n",
+            "MSE: 4384.805144886077\n",
+            "RMSE: 66.21786122252875\n",
+            "MAE: 39.35300687872675\n",
+            "RMSLE: NaN\n",
+            "Mean Residual Deviance: 4384.805144886077\n",
+            "\n",
+            "ModelMetricsRegression: deeplearning\n",
+            "** Reported on validation data. **\n",
+            "\n",
+            "MSE: 10004.799000384448\n",
+            "RMSE: 100.02399212381222\n",
+            "MAE: 64.55607092312168\n",
+            "RMSLE: 0.26666437297003515\n",
+            "Mean Residual Deviance: 10004.799000384448\n",
+            "\n",
+            "Scoring History: \n",
+            "    timestamp            duration          training_speed    epochs    iterations    samples      training_rmse    training_deviance    training_mae    training_r2    validation_rmse    validation_deviance    validation_mae    validation_r2\n",
+            "--  -------------------  ----------------  ----------------  --------  ------------  -----------  ---------------  -------------------  --------------  -------------  -----------------  ---------------------  ----------------  ---------------\n",
+            "    2025-04-09 16:09:56  0.000 sec                           0         0             0            nan              nan                  nan             nan            nan                nan                    nan               nan\n",
+            "    2025-04-09 16:09:56  6 min 37.012 sec  38165 obs/sec     10        1             8320         240.37           57777.8              137.635         0.978023       294.514            86738.6                221.46            0.984593\n",
+            "    2025-04-09 16:10:01  6 min 42.073 sec  60003 obs/sec     380       38            316160       91.0855          8296.57              49.7045         0.996844       122.101            14908.7                79.0275           0.997352\n",
+            "    2025-04-09 16:10:06  6 min 47.148 sec  53162 obs/sec     660       66            549120       76.6521          5875.54              43.7185         0.997765       113.021            12773.7                70.2205           0.997731\n",
+            "    2025-04-09 16:10:11  6 min 52.260 sec  55013 obs/sec     1020      102           848640       66.2179          4384.81              39.353          0.998332       100.024            10004.8                64.5561           0.998223\n",
+            "    2025-04-09 16:10:17  6 min 57.375 sec  57541 obs/sec     1420      142           1.18144e+06  61.5197          3784.67              35.8641         0.99856        108.075            11680.1                63.681            0.997925\n",
+            "    2025-04-09 16:10:22  7 min  2.404 sec  54715 obs/sec     1680      168           1.39776e+06  60.9563          3715.67              35.8824         0.998587       107.104            11471.3                64.1603           0.997962\n",
+            "    2025-04-09 16:10:27  7 min  7.456 sec  56030 obs/sec     2060      206           1.71392e+06  61.1253          3736.3               34.7526         0.998579       118.973            14154.7                71.2795           0.997486\n",
+            "    2025-04-09 16:10:32  7 min 12.547 sec  57379 obs/sec     2460      246           2.04672e+06  68.0689          4633.37              38.9115         0.998238       132.304            17504.4                78.5939           0.996891\n",
+            "    2025-04-09 16:10:37  7 min 17.654 sec  55306 obs/sec     2710      271           2.25472e+06  58.1131          3377.13              33.0698         0.998715       113.531            12889.2                68.2691           0.997711\n",
+            "    2025-04-09 16:10:37  7 min 17.667 sec  55299 obs/sec     2710      271           2.25472e+06  66.2179          4384.81              39.353          0.998332       100.024            10004.8                64.5561           0.998223\n",
+            "\n",
+            "Variable Importances: \n",
+            "variable                        relative_importance    scaled_importance     percentage\n",
+            "------------------------------  ---------------------  --------------------  ---------------------\n",
+            "RESULT DATE open price          1.0                    1.0                   0.033923893359142504\n",
+            "SMA20                           0.6353387236595154     0.6353387236595154    0.021553163108359107\n",
+            "Investments                     0.3604700565338135     0.3604700565338135    0.012228547757017157\n",
+            "SMA50                           0.3130214214324951     0.3130214214324951    0.010618905319803168\n",
+            "Equity Capital                  0.23126667737960815    0.23126667737960815   0.00784546610094904\n",
+            "Interest                        0.22151115536689758    0.22151115536689758   0.00751452081252708\n",
+            "Total Liabilities               0.19082963466644287    0.19082963466644287   0.006473684176188531\n",
+            "Other Assets                    0.18919093906879425    0.18919093906879425   0.006418093241485803\n",
+            "Other Income                    0.18473364412784576    0.18473364412784576   0.006266884443238821\n",
+            "Reserves                        0.18401896953582764    0.18401896953582764   0.006242639898592709\n",
+            "---                             ---                    ---                   ---\n",
+            "Dividend Payout %               0.047408148646354675   0.047408148646354675  0.001608268979033312\n",
+            "OPM %                           0.034951724112033844   0.034951724112033844  0.0011856985614948057\n",
+            "inflation                       0.031954050064086914   0.031954050064086914  0.001084005786766785\n",
+            "NIFTY50_volume                  0.03183847293257713    0.03183847293257713   0.0010800849604826917\n",
+            "gdp_growth                      0.027104398235678673   0.027104398235678673  0.0009194867153108935\n",
+            "Revenue.missing(NA)             0.0                    0.0                   0.0\n",
+            "Financing Profit.missing(NA)    0.0                    0.0                   0.0\n",
+            "Borrowing.missing(NA)           0.0                    0.0                   0.0\n",
+            "Financing Margin %.missing(NA)  0.0                    0.0                   0.0\n",
+            "Deposits.missing(NA)            0.0                    0.0                   0.0\n",
+            "[281 rows x 4 columns]\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Get training timing info\n",
+        "info = aml.training_info\n",
+        "print(info)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cRlHUJ21PyjX",
+        "outputId": "3b81d02a-3cd1-4a92-dcfd-7ddeea147212"
+      },
+      "execution_count": 74,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{'creation_epoch': '1744214486', 'start_epoch': '1744214486', 'start_XGBoost_def_2': '1744214486', 'start_GLM_def_1': '1744214488', 'start_GBM_def_5': '1744214488', 'start_XGBoost_def_1': '1744214488', 'start_DRF_def_1': '1744214489', 'start_GBM_def_2': '1744214491', 'start_GBM_def_3': '1744214492', 'start_GBM_def_4': '1744214494', 'start_XGBoost_def_3': '1744214495', 'start_DRF_XRT': '1744214496', 'start_GBM_def_1': '1744214499', 'start_DeepLearning_def_1': '1744214501', 'start_XGBoost_grid_1': '1744214501', 'start_GBM_grid_1': '1744214574', 'start_DeepLearning_grid_1': '1744214600', 'start_DeepLearning_grid_2': '1744215129', 'start_DeepLearning_grid_3': '1744215596', 'stop_epoch': '1744216240', 'duration_secs': '1754'}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "aml.leader.model_id"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "id": "iOlRLFu1P-oX",
+        "outputId": "fcdc8f90-ec65-439e-eb3e-8d2f0e42c480"
+      },
+      "execution_count": 75,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'DeepLearning_grid_1_AutoML_7_20250409_160125_model_9'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 75
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "preds_leader = aml.leader.predict(h2o_test)\n",
+        "preds_leader"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 406
+        },
+        "id": "J7mERM2YP_yS",
+        "outputId": "895fe069-cf12-4cd0-f8ce-ff088c68d3ce"
+      },
+      "execution_count": 76,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "deeplearning prediction progress: |██████████████████████████████████████████████| (done) 100%\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   predict\n",
+              "----------\n",
+              "   9.18649\n",
+              "1006.17\n",
+              "1854.82\n",
+              "  81.9125\n",
+              "  97.5076\n",
+              "2888.97\n",
+              "4506.62\n",
+              " 637.33\n",
+              " 617.228\n",
+              "1029.59\n",
+              "[107 rows x 1 column]\n"
+            ],
+            "text/html": [
+              "<table class='dataframe'>\n",
+              "<thead>\n",
+              "<tr><th style=\"text-align: right;\">   predict</th></tr>\n",
+              "</thead>\n",
+              "<tbody>\n",
+              "<tr><td style=\"text-align: right;\">   9.18649</td></tr>\n",
+              "<tr><td style=\"text-align: right;\">1006.17   </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">1854.82   </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">  81.9125 </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">  97.5076 </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">2888.97   </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">4506.62   </td></tr>\n",
+              "<tr><td style=\"text-align: right;\"> 637.33   </td></tr>\n",
+              "<tr><td style=\"text-align: right;\"> 617.228  </td></tr>\n",
+              "<tr><td style=\"text-align: right;\">1029.59   </td></tr>\n",
+              "</tbody>\n",
+              "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[107 rows x 1 column]</pre>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 76
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"\\n TRAIN SET \\n\")\n",
+        "train_predictions = aml.leader.predict(h2o_train).as_data_frame()['predict'].to_list()\n",
+        "print(calculate_regression_metrics(train[y_col], train_predictions))\n",
+        "\n",
+        "print(\"\\n VALID SET \\n\")\n",
+        "valid_predictions = aml.leader.predict(h2o_valid).as_data_frame()['predict'].to_list()\n",
+        "print(calculate_regression_metrics(valid[y_col], valid_predictions))\n",
+        "\n",
+        "print(\"\\n TEST SET \\n\")\n",
+        "test_predictions = aml.leader.predict(h2o_test).as_data_frame()['predict'].to_list()\n",
+        "print(calculate_regression_metrics(test[y_col],test_predictions))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xUHCNpAWSJeX",
+        "outputId": "814a1a0e-5c50-474f-f144-aae295b6c298"
+      },
+      "execution_count": 77,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " TRAIN SET \n",
+            "\n",
+            "deeplearning prediction progress: |██████████████████████████████████████████████| (done) 100%\n",
+            "{'MAE': 39.35300733202008, 'RMSE': np.float64(66.21786015854784), 'MAPE': 0.14468676367502867}\n",
+            "\n",
+            " VALID SET \n",
+            "\n",
+            "deeplearning prediction progress: |██████████████████████████████████████████████| (done) 100%\n",
+            "{'MAE': 64.55607585185871, 'RMSE': np.float64(100.02399540743662), 'MAPE': 0.07541354069196421}\n",
+            "\n",
+            " TEST SET \n",
+            "\n",
+            "deeplearning prediction progress: |█████████████████████████��████████████████████| (done) 100%\n",
+            "{'MAE': 104.78748524053184, 'RMSE': np.float64(188.53744596018586), 'MAPE': 0.333730313262129}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "best_model = aml.get_best_model()\n",
+        "model_path = h2o.save_model(model=best_model, path=path + \"mimic_best_model\", force=True)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "sHnFQEqxcbX-",
+        "outputId": "1126c37a-6abc-4660-d8d6-1c91a17be2ae"
+      },
+      "execution_count": 82,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/content/drive/MyDrive/datasets/Multimodal_EarningCallTranscripts_Analysis_India/mimic_best_model/DeepLearning_grid_1_AutoML_7_20250409_160125_model_9\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# To load the model, use the following commands\n",
+        "# !unzip Best_Performing_Model_on_MiMIC.zip\n",
+        "# loaded_model = h2o.load_model(\"DeepLearning_grid_1_AutoML_7_20250409_160125_model_9\")"
+      ],
+      "metadata": {
+        "id": "ZVNkZoLjdVE7"
+      },
+      "execution_count": null,
+      "outputs": []
     }
   ]
 }
\ No newline at end of file