H2O Machine Learning Tutorial.ipynb

{
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   "metadata": {},
   "outputs": [],
   "source": [
    "# sudo apt install default-jre\n",
    "# pip install requests\n",
    "# pip install tabulate\n",
    "# pip install \"colorama >= 0.3.8\"\n",
    "# pip install future\n",
    "# pip install matplotlib\n",
    "# pip install -f http://h2o-release.s3.amazonaws.com/h2o/latest_stable_Py.html h2o"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1127801a-a299-4883-bc18-f217a2f43bce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "openjdk version \"11.0.20\" 2023-07-18\n",
      "OpenJDK Runtime Environment (build 11.0.20+8-post-Debian-1deb11u1)\n",
      "OpenJDK 64-Bit Server VM (build 11.0.20+8-post-Debian-1deb11u1, mixed mode, sharing)\n"
     ]
    }
   ],
   "source": [
    "!java -version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "631d06f1-7ec3-4dcb-b408-6464c7ada19b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the H2O library\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import h2o\n",
    "import seaborn as sns\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "98977f19-f792-4bf6-a59f-fb87487ab778",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54323..... not found.\n",
      "Attempting to start a local H2O server...\n",
      "  Java Version: openjdk version \"11.0.20\" 2023-07-18; OpenJDK Runtime Environment (build 11.0.20+8-post-Debian-1deb11u1); OpenJDK 64-Bit Server VM (build 11.0.20+8-post-Debian-1deb11u1, mixed mode, sharing)\n",
      "  Starting server from /opt/bitnami/python/lib/python3.11/site-packages/h2o/backend/bin/h2o.jar\n",
      "  Ice root: /tmp/tmpptyyzwws\n",
      "  JVM stdout: /tmp/tmpptyyzwws/h2o_unknownUser_started_from_python.out\n",
      "  JVM stderr: /tmp/tmpptyyzwws/h2o_unknownUser_started_from_python.err\n",
      "  Server is running at http://127.0.0.1:54323\n",
      "Connecting to H2O server at http://127.0.0.1:54323 ... successful.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "\n",
       "#h2o-table-1.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-1 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-1 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-1 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-1 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-1 .h2o-table th,\n",
       "#h2o-table-1 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-1 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-1\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption></caption>\n",
       "    <thead></thead>\n",
       "    <tbody><tr><td>H2O_cluster_uptime:</td>\n",
       "<td>01 secs</td></tr>\n",
       "<tr><td>H2O_cluster_timezone:</td>\n",
       "<td>Etc/UTC</td></tr>\n",
       "<tr><td>H2O_data_parsing_timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O_cluster_version:</td>\n",
       "<td>3.44.0.1</td></tr>\n",
       "<tr><td>H2O_cluster_version_age:</td>\n",
       "<td>24 days</td></tr>\n",
       "<tr><td>H2O_cluster_name:</td>\n",
       "<td>H2O_from_python_unknownUser_pv78ro</td></tr>\n",
       "<tr><td>H2O_cluster_total_nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O_cluster_free_memory:</td>\n",
       "<td>3 Gb</td></tr>\n",
       "<tr><td>H2O_cluster_total_cores:</td>\n",
       "<td>4</td></tr>\n",
       "<tr><td>H2O_cluster_allowed_cores:</td>\n",
       "<td>4</td></tr>\n",
       "<tr><td>H2O_cluster_status:</td>\n",
       "<td>locked, healthy</td></tr>\n",
       "<tr><td>H2O_connection_url:</td>\n",
       "<td>http://127.0.0.1:54323</td></tr>\n",
       "<tr><td>H2O_connection_proxy:</td>\n",
       "<td>{\"http\": null, \"https\": null}</td></tr>\n",
       "<tr><td>H2O_internal_security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>Python_version:</td>\n",
       "<td>3.11.4 final</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n"
      ],
      "text/plain": [
       "--------------------------  ----------------------------------\n",
       "H2O_cluster_uptime:         01 secs\n",
       "H2O_cluster_timezone:       Etc/UTC\n",
       "H2O_data_parsing_timezone:  UTC\n",
       "H2O_cluster_version:        3.44.0.1\n",
       "H2O_cluster_version_age:    24 days\n",
       "H2O_cluster_name:           H2O_from_python_unknownUser_pv78ro\n",
       "H2O_cluster_total_nodes:    1\n",
       "H2O_cluster_free_memory:    3 Gb\n",
       "H2O_cluster_total_cores:    4\n",
       "H2O_cluster_allowed_cores:  4\n",
       "H2O_cluster_status:         locked, healthy\n",
       "H2O_connection_url:         http://127.0.0.1:54323\n",
       "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
       "H2O_internal_security:      False\n",
       "Python_version:             3.11.4 final\n",
       "--------------------------  ----------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize h2o cluster\n",
    "# Number of threads, nthreads = -1, means use all cores on your machine\n",
    "# max_mem_size is the maximum memory (in GB) to allocate to H2O\n",
    "h2o.init(nthreads = -1, max_mem_size = 3, ip = \"localhost\", port = 54323)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "763672a8-7a6f-43db-a91f-18e06ab4f314",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
      "991346 x 24\n"
     ]
    }
   ],
   "source": [
    "dataset_csv = \"smoking_driking_dataset_Ver01.csv\"\n",
    "data = h2o.import_file(dataset_csv, header = 1, sep =\",\")\n",
    "print(str(data.shape[0]) + \" x \" + str(data.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a6db9e4f-f691-4215-ad69-ddc7cd82994a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class='dataframe'>\n",
       "<thead>\n",
       "<tr><th>sex   </th><th style=\"text-align: right;\">  age</th><th style=\"text-align: right;\">  height</th><th style=\"text-align: right;\">  weight</th><th style=\"text-align: right;\">  waistline</th><th style=\"text-align: right;\">  sight_left</th><th style=\"text-align: right;\">  sight_right</th><th style=\"text-align: right;\">  hear_left</th><th style=\"text-align: right;\">  hear_right</th><th style=\"text-align: right;\">  SBP</th><th style=\"text-align: right;\">  DBP</th><th style=\"text-align: right;\">  BLDS</th><th style=\"text-align: right;\">  tot_chole</th><th style=\"text-align: right;\">  HDL_chole</th><th style=\"text-align: right;\">  LDL_chole</th><th style=\"text-align: right;\">  triglyceride</th><th style=\"text-align: right;\">  hemoglobin</th><th style=\"text-align: right;\">  urine_protein</th><th style=\"text-align: right;\">  serum_creatinine</th><th style=\"text-align: right;\">  SGOT_AST</th><th style=\"text-align: right;\">  SGOT_ALT</th><th style=\"text-align: right;\">  gamma_GTP</th><th style=\"text-align: right;\">  SMK_stat_type_cd</th><th>DRK_YN  </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>Male  </td><td style=\"text-align: right;\">   35</td><td style=\"text-align: right;\">     170</td><td style=\"text-align: right;\">      75</td><td style=\"text-align: right;\">       90  </td><td style=\"text-align: right;\">         1  </td><td style=\"text-align: right;\">          1  </td><td style=\"text-align: right;\">          1</td><td style=\"text-align: right;\">           1</td><td style=\"text-align: right;\">  120</td><td style=\"text-align: right;\">   80</td><td style=\"text-align: right;\">    99</td><td style=\"text-align: right;\">        193</td><td style=\"text-align: right;\">         48</td><td style=\"text-align: right;\">        126</td><td style=\"text-align: right;\">            92</td><td style=\"text-align: right;\">        17.1</td><td style=\"text-align: right;\">              1</td><td style=\"text-align: right;\">               1  </td><td style=\"text-align: right;\">        21</td><td style=\"text-align: right;\">        35</td><td style=\"text-align: right;\">         40</td><td style=\"text-align: right;\">                 1</td><td>Y       </td></tr>\n",
       "<tr><td>Male  </td><td style=\"text-align: right;\">   30</td><td style=\"text-align: right;\">     180</td><td style=\"text-align: right;\">      80</td><td style=\"text-align: right;\">       89  </td><td style=\"text-align: right;\">         0.9</td><td style=\"text-align: right;\">          1.2</td><td style=\"text-align: right;\">          1</td><td style=\"text-align: right;\">           1</td><td style=\"text-align: right;\">  130</td><td style=\"text-align: right;\">   82</td><td style=\"text-align: right;\">   106</td><td style=\"text-align: right;\">        228</td><td style=\"text-align: right;\">         55</td><td style=\"text-align: right;\">        148</td><td style=\"text-align: right;\">           121</td><td style=\"text-align: right;\">        15.8</td><td style=\"text-align: right;\">              1</td><td style=\"text-align: right;\">               0.9</td><td style=\"text-align: right;\">        20</td><td style=\"text-align: right;\">        36</td><td style=\"text-align: right;\">         27</td><td style=\"text-align: right;\">                 3</td><td>N       </td></tr>\n",
       "<tr><td>Male  </td><td style=\"text-align: right;\">   40</td><td style=\"text-align: right;\">     165</td><td style=\"text-align: right;\">      75</td><td style=\"text-align: right;\">       91  </td><td style=\"text-align: right;\">         1.2</td><td style=\"text-align: right;\">          1.5</td><td style=\"text-align: right;\">          1</td><td style=\"text-align: right;\">           1</td><td style=\"text-align: right;\">  120</td><td style=\"text-align: right;\">   70</td><td style=\"text-align: right;\">    98</td><td style=\"text-align: right;\">        136</td><td style=\"text-align: right;\">         41</td><td style=\"text-align: right;\">         74</td><td style=\"text-align: right;\">           104</td><td style=\"text-align: right;\">        15.8</td><td style=\"text-align: right;\">              1</td><td style=\"text-align: right;\">               0.9</td><td style=\"text-align: right;\">        47</td><td style=\"text-align: right;\">        32</td><td style=\"text-align: right;\">         68</td><td style=\"text-align: right;\">                 1</td><td>N       </td></tr>\n",
       "<tr><td>Male  </td><td style=\"text-align: right;\">   50</td><td style=\"text-align: right;\">     175</td><td style=\"text-align: right;\">      80</td><td style=\"text-align: right;\">       91  </td><td style=\"text-align: right;\">         1.5</td><td style=\"text-align: right;\">          1.2</td><td style=\"text-align: right;\">          1</td><td style=\"text-align: right;\">           1</td><td style=\"text-align: right;\">  145</td><td style=\"text-align: right;\">   87</td><td style=\"text-align: right;\">    95</td><td style=\"text-align: right;\">        201</td><td style=\"text-align: right;\">         76</td><td style=\"text-align: right;\">        104</td><td style=\"text-align: right;\">           106</td><td style=\"text-align: right;\">        17.6</td><td style=\"text-align: right;\">              1</td><td style=\"text-align: right;\">               1.1</td><td style=\"text-align: right;\">        29</td><td style=\"text-align: right;\">        34</td><td style=\"text-align: right;\">         18</td><td style=\"text-align: right;\">                 1</td><td>N       </td></tr>\n",
       "<tr><td>Male  </td><td style=\"text-align: right;\">   50</td><td style=\"text-align: right;\">     165</td><td style=\"text-align: right;\">      60</td><td style=\"text-align: right;\">       80  </td><td style=\"text-align: right;\">         1  </td><td style=\"text-align: right;\">          1.2</td><td style=\"text-align: right;\">          1</td><td style=\"text-align: right;\">           1</td><td style=\"text-align: right;\">  138</td><td style=\"text-align: right;\">   82</td><td style=\"text-align: right;\">   101</td><td style=\"text-align: right;\">        199</td><td style=\"text-align: right;\">         61</td><td style=\"text-align: right;\">        117</td><td style=\"text-align: right;\">           104</td><td style=\"text-align: right;\">        13.8</td><td style=\"text-align: right;\">              1</td><td style=\"text-align: right;\">               0.8</td><td style=\"text-align: right;\">        19</td><td style=\"text-align: right;\">        12</td><td style=\"text-align: right;\">         25</td><td style=\"text-align: right;\">                 1</td><td>N       </td></tr>\n",
       "<tr><td>Male  </td><td style=\"text-align: right;\">   50</td><td style=\"text-align: right;\">     165</td><td style=\"text-align: right;\">      55</td><td style=\"text-align: right;\">       75  </td><td style=\"text-align: right;\">         1.2</td><td style=\"text-align: right;\">          1.5</td><td style=\"text-align: right;\">          1</td><td style=\"text-align: right;\">           1</td><td style=\"text-align: right;\">  142</td><td style=\"text-align: right;\">   92</td><td style=\"text-align: right;\">    99</td><td style=\"text-align: right;\">        218</td><td style=\"text-align: right;\">         77</td><td style=\"text-align: right;\">         95</td><td style=\"text-align: right;\">           232</td><td style=\"text-align: right;\">        13.8</td><td style=\"text-align: right;\">              3</td><td style=\"text-align: right;\">               0.8</td><td style=\"text-align: right;\">        29</td><td style=\"text-align: right;\">        40</td><td style=\"text-align: right;\">         37</td><td style=\"text-align: right;\">                 3</td><td>Y       </td></tr>\n",
       "<tr><td>Female</td><td style=\"text-align: right;\">   45</td><td style=\"text-align: right;\">     150</td><td style=\"text-align: right;\">      55</td><td style=\"text-align: right;\">       69  </td><td style=\"text-align: right;\">         0.5</td><td style=\"text-align: right;\">          0.4</td><td style=\"text-align: right;\">          1</td><td style=\"text-align: right;\">           1</td><td style=\"text-align: right;\">  101</td><td style=\"text-align: right;\">   58</td><td style=\"text-align: right;\">    89</td><td style=\"text-align: right;\">        196</td><td style=\"text-align: right;\">         66</td><td style=\"text-align: right;\">        115</td><td style=\"text-align: right;\">            75</td><td style=\"text-align: right;\">        12.3</td><td style=\"text-align: right;\">              1</td><td style=\"text-align: right;\">               0.8</td><td style=\"text-align: right;\">        19</td><td style=\"text-align: right;\">        12</td><td style=\"text-align: right;\">         12</td><td style=\"text-align: right;\">                 1</td><td>N       </td></tr>\n",
       "<tr><td>Male  </td><td style=\"text-align: right;\">   35</td><td style=\"text-align: right;\">     175</td><td style=\"text-align: right;\">      65</td><td style=\"text-align: right;\">       84.2</td><td style=\"text-align: right;\">         1.2</td><td style=\"text-align: right;\">          1  </td><td style=\"text-align: right;\">          1</td><td style=\"text-align: right;\">           1</td><td style=\"text-align: right;\">  132</td><td style=\"text-align: right;\">   80</td><td style=\"text-align: right;\">    94</td><td style=\"text-align: right;\">        185</td><td style=\"text-align: right;\">         58</td><td style=\"text-align: right;\">        107</td><td style=\"text-align: right;\">           101</td><td style=\"text-align: right;\">        14.4</td><td style=\"text-align: right;\">              1</td><td style=\"text-align: right;\">               0.8</td><td style=\"text-align: right;\">        18</td><td style=\"text-align: right;\">        18</td><td style=\"text-align: right;\">         35</td><td style=\"text-align: right;\">                 3</td><td>Y       </td></tr>\n",
       "<tr><td>Male  </td><td style=\"text-align: right;\">   55</td><td style=\"text-align: right;\">     170</td><td style=\"text-align: right;\">      75</td><td style=\"text-align: right;\">       84  </td><td style=\"text-align: right;\">         1.2</td><td style=\"text-align: right;\">          0.9</td><td style=\"text-align: right;\">          1</td><td style=\"text-align: right;\">           1</td><td style=\"text-align: right;\">  145</td><td style=\"text-align: right;\">   85</td><td style=\"text-align: right;\">   104</td><td style=\"text-align: right;\">        217</td><td style=\"text-align: right;\">         56</td><td style=\"text-align: right;\">        141</td><td style=\"text-align: right;\">           100</td><td style=\"text-align: right;\">        15.1</td><td style=\"text-align: right;\">              1</td><td style=\"text-align: right;\">               0.8</td><td style=\"text-align: right;\">        32</td><td style=\"text-align: right;\">        23</td><td style=\"text-align: right;\">         26</td><td style=\"text-align: right;\">                 1</td><td>Y       </td></tr>\n",
       "<tr><td>Male  </td><td style=\"text-align: right;\">   40</td><td style=\"text-align: right;\">     175</td><td style=\"text-align: right;\">      75</td><td style=\"text-align: right;\">       82  </td><td style=\"text-align: right;\">         1.5</td><td style=\"text-align: right;\">          1.5</td><td style=\"text-align: right;\">          1</td><td style=\"text-align: right;\">           1</td><td style=\"text-align: right;\">  132</td><td style=\"text-align: right;\">  105</td><td style=\"text-align: right;\">   100</td><td style=\"text-align: right;\">        195</td><td style=\"text-align: right;\">         60</td><td style=\"text-align: right;\">        118</td><td style=\"text-align: right;\">            83</td><td style=\"text-align: right;\">        13.9</td><td style=\"text-align: right;\">              1</td><td style=\"text-align: right;\">               0.9</td><td style=\"text-align: right;\">        21</td><td style=\"text-align: right;\">        38</td><td style=\"text-align: right;\">         16</td><td style=\"text-align: right;\">                 2</td><td>Y       </td></tr>\n",
       "</tbody>\n",
       "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[991346 rows x 24 columns]</pre>"
      ],
      "text/plain": [
       "sex       age    height    weight    waistline    sight_left    sight_right    hear_left    hear_right    SBP    DBP    BLDS    tot_chole    HDL_chole    LDL_chole    triglyceride    hemoglobin    urine_protein    serum_creatinine    SGOT_AST    SGOT_ALT    gamma_GTP    SMK_stat_type_cd  DRK_YN\n",
       "------  -----  --------  --------  -----------  ------------  -------------  -----------  ------------  -----  -----  ------  -----------  -----------  -----------  --------------  ------------  ---------------  ------------------  ----------  ----------  -----------  ------------------  --------\n",
       "Male       35       170        75         90             1              1              1             1    120     80      99          193           48          126              92          17.1                1                 1            21          35           40                   1  Y\n",
       "Male       30       180        80         89             0.9            1.2            1             1    130     82     106          228           55          148             121          15.8                1                 0.9          20          36           27                   3  N\n",
       "Male       40       165        75         91             1.2            1.5            1             1    120     70      98          136           41           74             104          15.8                1                 0.9          47          32           68                   1  N\n",
       "Male       50       175        80         91             1.5            1.2            1             1    145     87      95          201           76          104             106          17.6                1                 1.1          29          34           18                   1  N\n",
       "Male       50       165        60         80             1              1.2            1             1    138     82     101          199           61          117             104          13.8                1                 0.8          19          12           25                   1  N\n",
       "Male       50       165        55         75             1.2            1.5            1             1    142     92      99          218           77           95             232          13.8                3                 0.8          29          40           37                   3  Y\n",
       "Female     45       150        55         69             0.5            0.4            1             1    101     58      89          196           66          115              75          12.3                1                 0.8          19          12           12                   1  N\n",
       "Male       35       175        65         84.2           1.2            1              1             1    132     80      94          185           58          107             101          14.4                1                 0.8          18          18           35                   3  Y\n",
       "Male       55       170        75         84             1.2            0.9            1             1    145     85     104          217           56          141             100          15.1                1                 0.8          32          23           26                   1  Y\n",
       "Male       40       175        75         82             1.5            1.5            1             1    132    105     100          195           60          118              83          13.9                1                 0.9          21          38           16                   2  Y\n",
       "[991346 rows x 24 columns]\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "52f9a2c5-b9c5-4156-852c-39c3cd66f2da",
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_percent(df, var):\n",
    "    df_agg = df.group_by(var).count().get_frame()\n",
    "    total = df_agg['nrow'].sum()\n",
    "    df_agg['percentage'] = df_agg['nrow'] / total\n",
    "    return df_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "541148b9-4f14-4412-a26d-0b34d2bad8e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class='dataframe'>\n",
       "<thead>\n",
       "<tr><th>DRK_YN  </th><th style=\"text-align: right;\">  nrow</th><th style=\"text-align: right;\">  percentage</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>N       </td><td style=\"text-align: right;\">495858</td><td style=\"text-align: right;\">    0.500187</td></tr>\n",
       "<tr><td>Y       </td><td style=\"text-align: right;\">495488</td><td style=\"text-align: right;\">    0.499813</td></tr>\n",
       "</tbody>\n",
       "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[2 rows x 3 columns]</pre>"
      ],
      "text/plain": [
       "DRK_YN      nrow    percentage\n",
       "--------  ------  ------------\n",
       "N         495858      0.500187\n",
       "Y         495488      0.499813\n",
       "[2 rows x 3 columns]\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(count_percent(data, 'DRK_YN'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "caa6d1a5-5482-4da0-8b6e-bd6666cfd10d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age_range</th>\n",
       "      <th>nrow</th>\n",
       "      <th>min_age</th>\n",
       "      <th>max_age</th>\n",
       "      <th>bad</th>\n",
       "      <th>good</th>\n",
       "      <th>percentage</th>\n",
       "      <th>bad_rate</th>\n",
       "      <th>good_rate</th>\n",
       "      <th>perc_events</th>\n",
       "      <th>perc_non_events</th>\n",
       "      <th>woe</th>\n",
       "      <th>iv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[20.0,30.0]</td>\n",
       "      <td>163941</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>108704</td>\n",
       "      <td>55237</td>\n",
       "      <td>0.165372</td>\n",
       "      <td>0.663068</td>\n",
       "      <td>0.336932</td>\n",
       "      <td>0.219388</td>\n",
       "      <td>0.111397</td>\n",
       "      <td>-0.677742</td>\n",
       "      <td>0.073190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(30.0,35.0]</td>\n",
       "      <td>84726</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>56220</td>\n",
       "      <td>28506</td>\n",
       "      <td>0.085466</td>\n",
       "      <td>0.663551</td>\n",
       "      <td>0.336449</td>\n",
       "      <td>0.113464</td>\n",
       "      <td>0.057488</td>\n",
       "      <td>-0.679904</td>\n",
       "      <td>0.038058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(35.0,40.0]</td>\n",
       "      <td>130385</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>78186</td>\n",
       "      <td>52199</td>\n",
       "      <td>0.131523</td>\n",
       "      <td>0.599655</td>\n",
       "      <td>0.400345</td>\n",
       "      <td>0.157796</td>\n",
       "      <td>0.105270</td>\n",
       "      <td>-0.404774</td>\n",
       "      <td>0.021261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(40.0,45.0]</td>\n",
       "      <td>118355</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>66245</td>\n",
       "      <td>52110</td>\n",
       "      <td>0.119388</td>\n",
       "      <td>0.559714</td>\n",
       "      <td>0.440286</td>\n",
       "      <td>0.133696</td>\n",
       "      <td>0.105091</td>\n",
       "      <td>-0.240750</td>\n",
       "      <td>0.006887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(45.0,50.0]</td>\n",
       "      <td>129434</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>64112</td>\n",
       "      <td>65322</td>\n",
       "      <td>0.130564</td>\n",
       "      <td>0.495326</td>\n",
       "      <td>0.504674</td>\n",
       "      <td>0.129392</td>\n",
       "      <td>0.131735</td>\n",
       "      <td>0.017951</td>\n",
       "      <td>0.000042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(50.0,55.0]</td>\n",
       "      <td>111223</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>47984</td>\n",
       "      <td>63239</td>\n",
       "      <td>0.112194</td>\n",
       "      <td>0.431422</td>\n",
       "      <td>0.568578</td>\n",
       "      <td>0.096842</td>\n",
       "      <td>0.127534</td>\n",
       "      <td>0.275307</td>\n",
       "      <td>0.008450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(55.0,60.0]</td>\n",
       "      <td>106063</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>38341</td>\n",
       "      <td>67722</td>\n",
       "      <td>0.106989</td>\n",
       "      <td>0.361493</td>\n",
       "      <td>0.638507</td>\n",
       "      <td>0.077380</td>\n",
       "      <td>0.136575</td>\n",
       "      <td>0.568145</td>\n",
       "      <td>0.033631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(60.0,65.0]</td>\n",
       "      <td>52961</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>16272</td>\n",
       "      <td>36689</td>\n",
       "      <td>0.053423</td>\n",
       "      <td>0.307245</td>\n",
       "      <td>0.692755</td>\n",
       "      <td>0.032840</td>\n",
       "      <td>0.073991</td>\n",
       "      <td>0.812285</td>\n",
       "      <td>0.033426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(65.0,85.0]</td>\n",
       "      <td>94258</td>\n",
       "      <td>70</td>\n",
       "      <td>85</td>\n",
       "      <td>19424</td>\n",
       "      <td>74834</td>\n",
       "      <td>0.095081</td>\n",
       "      <td>0.206073</td>\n",
       "      <td>0.793927</td>\n",
       "      <td>0.039202</td>\n",
       "      <td>0.150918</td>\n",
       "      <td>1.348016</td>\n",
       "      <td>0.150596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age_range    nrow  min_age  max_age     bad   good  percentage  bad_rate  \\\n",
       "0  [20.0,30.0]  163941       20       30  108704  55237    0.165372  0.663068   \n",
       "1  (30.0,35.0]   84726       35       35   56220  28506    0.085466  0.663551   \n",
       "2  (35.0,40.0]  130385       40       40   78186  52199    0.131523  0.599655   \n",
       "3  (40.0,45.0]  118355       45       45   66245  52110    0.119388  0.559714   \n",
       "4  (45.0,50.0]  129434       50       50   64112  65322    0.130564  0.495326   \n",
       "5  (50.0,55.0]  111223       55       55   47984  63239    0.112194  0.431422   \n",
       "6  (55.0,60.0]  106063       60       60   38341  67722    0.106989  0.361493   \n",
       "7  (60.0,65.0]   52961       65       65   16272  36689    0.053423  0.307245   \n",
       "8  (65.0,85.0]   94258       70       85   19424  74834    0.095081  0.206073   \n",
       "\n",
       "   good_rate  perc_events  perc_non_events       woe        iv  \n",
       "0   0.336932     0.219388         0.111397 -0.677742  0.073190  \n",
       "1   0.336449     0.113464         0.057488 -0.679904  0.038058  \n",
       "2   0.400345     0.157796         0.105270 -0.404774  0.021261  \n",
       "3   0.440286     0.133696         0.105091 -0.240750  0.006887  \n",
       "4   0.504674     0.129392         0.131735  0.017951  0.000042  \n",
       "5   0.568578     0.096842         0.127534  0.275307  0.008450  \n",
       "6   0.638507     0.077380         0.136575  0.568145  0.033631  \n",
       "7   0.692755     0.032840         0.073991  0.812285  0.033426  \n",
       "8   0.793927     0.039202         0.150918  1.348016  0.150596  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Information Value: 0.36554\n"
     ]
    }
   ],
   "source": [
    "by_col = \"age\"\n",
    "target_col = \"DRK_YN\"\n",
    "agg_col = by_col + \"_range\"\n",
    "num_quantile = 10\n",
    "df_data = data[[by_col, target_col]]\n",
    "df_data['target'] = (df_data[target_col] == 'Y').ifelse(1, 0)\n",
    "\n",
    "prob = list(np.arange(1, num_quantile, 1)/num_quantile)\n",
    "quantiles = df_data[by_col].quantile(prob = prob)\n",
    "quantil_min = df_data[by_col].min()\n",
    "quantil_max = df_data[by_col].max()\n",
    "quantiles_pd = quantiles[1].as_data_frame()\n",
    "quantiles_list = quantiles_pd.iloc[:, 0].to_list()\n",
    "quantiles_list.append(quantil_min)\n",
    "quantiles_list.append(quantil_max)\n",
    "quantiles_list = list(set(quantiles_list))\n",
    "quantiles_list.sort()\n",
    "\n",
    "df_data[agg_col] = df_data[by_col].cut(quantiles_list, include_lowest = True)\n",
    "\n",
    "df_agg = df_data.group_by(agg_col).count().min(by_col).max(by_col).sum('target').get_frame().as_data_frame()\n",
    "\n",
    "total = df_agg['nrow'].sum()\n",
    "df_agg = df_agg.rename(columns={'sum_target':'bad'})\n",
    "df_agg['good'] = df_agg['nrow'] - df_agg['bad']\n",
    "df_agg['percentage'] = df_agg['nrow'] / total\n",
    "df_agg['bad_rate'] = df_agg['bad'] / df_agg['nrow']\n",
    "df_agg['good_rate'] = df_agg['good'] / df_agg['nrow']\n",
    "total_bad = df_agg['bad'].sum()\n",
    "total_good = df_agg['good'].sum()\n",
    "df_agg['perc_events'] = df_agg['bad'] / total_bad\n",
    "df_agg['perc_non_events'] = df_agg['good'] / total_good\n",
    "df_agg['woe'] = np.log(df_agg['perc_non_events'] / df_agg['perc_events'])\n",
    "df_agg['iv'] = (df_agg['perc_non_events'] - df_agg['perc_events']) * df_agg['woe']\n",
    "iv = df_agg['iv'].sum()\n",
    "display(df_agg)\n",
    "\n",
    "print('Information Value: ' + str(round(iv, 5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1e176a34-d0f5-457b-b6a5-311ac4fc269d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class='dataframe'>\n",
       "<thead>\n",
       "<tr><th>DRK_YN  </th><th style=\"text-align: right;\">  nrow</th><th style=\"text-align: right;\">  percentage</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>N       </td><td style=\"text-align: right;\">347100</td><td style=\"text-align: right;\">     0.50016</td></tr>\n",
       "<tr><td>Y       </td><td style=\"text-align: right;\">346878</td><td style=\"text-align: right;\">     0.49984</td></tr>\n",
       "</tbody>\n",
       "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[2 rows x 3 columns]</pre>"
      ],
      "text/plain": [
       "DRK_YN      nrow    percentage\n",
       "--------  ------  ------------\n",
       "N         347100       0.50016\n",
       "Y         346878       0.49984\n",
       "[2 rows x 3 columns]\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class='dataframe'>\n",
       "<thead>\n",
       "<tr><th>DRK_YN  </th><th style=\"text-align: right;\">  nrow</th><th style=\"text-align: right;\">  percentage</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>N       </td><td style=\"text-align: right;\">148758</td><td style=\"text-align: right;\">    0.500249</td></tr>\n",
       "<tr><td>Y       </td><td style=\"text-align: right;\">148610</td><td style=\"text-align: right;\">    0.499751</td></tr>\n",
       "</tbody>\n",
       "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[2 rows x 3 columns]</pre>"
      ],
      "text/plain": [
       "DRK_YN      nrow    percentage\n",
       "--------  ------  ------------\n",
       "N         148758      0.500249\n",
       "Y         148610      0.499751\n",
       "[2 rows x 3 columns]\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train, valid = data.split_frame(ratios = [0.7], seed = 1234)\n",
    "\n",
    "display(count_percent(train, 'DRK_YN'))\n",
    "\n",
    "display(count_percent(valid, 'DRK_YN'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "73f2c329-f61c-4960-9ce5-ec5843ec239e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = 'DRK_YN'\n",
    "x = list(data.columns)\n",
    "x.remove(y)  #remove the response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9962bb2f-3922-4c21-83c4-aa388602e81f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import H2O GLM:\n",
    "from h2o.estimators.glm import H2OGeneralizedLinearEstimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e346b8dd-0191-4a37-b20b-22dd5ca62da2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "glm Model Build progress: |██████████████████████████████████████████████████████| (done) 100%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style='margin: 1em 0 1em 0;'>Model Details\n",
       "=============\n",
       "H2OGeneralizedLinearEstimator : Generalized Linear Modeling\n",
       "Model Key: GLM_model_python_1699563465891_2\n",
       "</pre>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-2.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-2 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-2 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-2 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-2 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-2 .h2o-table th,\n",
       "#h2o-table-2 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-2 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-2\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>GLM Model: summary</caption>\n",
       "    <thead><tr><th></th>\n",
       "<th>family</th>\n",
       "<th>link</th>\n",
       "<th>regularization</th>\n",
       "<th>number_of_predictors_total</th>\n",
       "<th>number_of_active_predictors</th>\n",
       "<th>number_of_iterations</th>\n",
       "<th>training_frame</th></tr></thead>\n",
       "    <tbody><tr><td></td>\n",
       "<td>binomial</td>\n",
       "<td>logit</td>\n",
       "<td>Elastic Net (alpha = 0.5, lambda = 3.745E-4 )</td>\n",
       "<td>24</td>\n",
       "<td>24</td>\n",
       "<td>4</td>\n",
       "<td>py_17_sid_9352</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div>\n",
       "<div style='margin: 1em 0 1em 0;'><pre style='margin: 1em 0 1em 0;'>ModelMetricsBinomialGLM: glm\n",
       "** Reported on train data. **\n",
       "\n",
       "MSE: 0.18161199755844645\n",
       "RMSE: 0.4261595916536978\n",
       "LogLoss: 0.5427301647441274\n",
       "AUC: 0.8026331114268583\n",
       "AUCPR: 0.7901412154675567\n",
       "Gini: 0.6052662228537167\n",
       "Null degrees of freedom: 693977\n",
       "Residual degrees of freedom: 693953\n",
       "Null deviance: 962057.7171236364\n",
       "Residual deviance: 754901.1045879977\n",
       "AIC: 754951.1045879977</pre>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-3.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-3 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-3 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-3 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-3 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-3 .h2o-table th,\n",
       "#h2o-table-3 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-3 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-3\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.34828097161278276</caption>\n",
       "    <thead><tr><th></th>\n",
       "<th>N</th>\n",
       "<th>Y</th>\n",
       "<th>Error</th>\n",
       "<th>Rate</th></tr></thead>\n",
       "    <tbody><tr><td>N</td>\n",
       "<td>192692.0</td>\n",
       "<td>154408.0</td>\n",
       "<td>0.4449</td>\n",
       "<td> (154408.0/347100.0)</td></tr>\n",
       "<tr><td>Y</td>\n",
       "<td>47061.0</td>\n",
       "<td>299817.0</td>\n",
       "<td>0.1357</td>\n",
       "<td> (47061.0/346878.0)</td></tr>\n",
       "<tr><td>Total</td>\n",
       "<td>239753.0</td>\n",
       "<td>454225.0</td>\n",
       "<td>0.2903</td>\n",
       "<td> (201469.0/693978.0)</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-4.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-4 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-4 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-4 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-4 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-4 .h2o-table th,\n",
       "#h2o-table-4 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-4 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-4\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Maximum Metrics: Maximum metrics at their respective thresholds</caption>\n",
       "    <thead><tr><th>metric</th>\n",
       "<th>threshold</th>\n",
       "<th>value</th>\n",
       "<th>idx</th></tr></thead>\n",
       "    <tbody><tr><td>max f1</td>\n",
       "<td>0.3482810</td>\n",
       "<td>0.7485105</td>\n",
       "<td>265.0</td></tr>\n",
       "<tr><td>max f2</td>\n",
       "<td>0.1976978</td>\n",
       "<td>0.8514464</td>\n",
       "<td>332.0</td></tr>\n",
       "<tr><td>max f0point5</td>\n",
       "<td>0.5707026</td>\n",
       "<td>0.7311292</td>\n",
       "<td>171.0</td></tr>\n",
       "<tr><td>max accuracy</td>\n",
       "<td>0.4850523</td>\n",
       "<td>0.7266196</td>\n",
       "<td>208.0</td></tr>\n",
       "<tr><td>max precision</td>\n",
       "<td>0.9413565</td>\n",
       "<td>0.9400837</td>\n",
       "<td>17.0</td></tr>\n",
       "<tr><td>max recall</td>\n",
       "<td>0.0022037</td>\n",
       "<td>1.0</td>\n",
       "<td>399.0</td></tr>\n",
       "<tr><td>max specificity</td>\n",
       "<td>0.9992715</td>\n",
       "<td>0.9995419</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max absolute_mcc</td>\n",
       "<td>0.4427658</td>\n",
       "<td>0.4542122</td>\n",
       "<td>225.0</td></tr>\n",
       "<tr><td>max min_per_class_accuracy</td>\n",
       "<td>0.4926082</td>\n",
       "<td>0.7260305</td>\n",
       "<td>204.0</td></tr>\n",
       "<tr><td>max mean_per_class_accuracy</td>\n",
       "<td>0.4850523</td>\n",
       "<td>0.7266220</td>\n",
       "<td>208.0</td></tr>\n",
       "<tr><td>max tns</td>\n",
       "<td>0.9992715</td>\n",
       "<td>346941.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max fns</td>\n",
       "<td>0.9992715</td>\n",
       "<td>345214.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max fps</td>\n",
       "<td>0.0022037</td>\n",
       "<td>347100.0</td>\n",
       "<td>399.0</td></tr>\n",
       "<tr><td>max tps</td>\n",
       "<td>0.0022037</td>\n",
       "<td>346878.0</td>\n",
       "<td>399.0</td></tr>\n",
       "<tr><td>max tnr</td>\n",
       "<td>0.9992715</td>\n",
       "<td>0.9995419</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max fnr</td>\n",
       "<td>0.9992715</td>\n",
       "<td>0.9952029</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max fpr</td>\n",
       "<td>0.0022037</td>\n",
       "<td>1.0</td>\n",
       "<td>399.0</td></tr>\n",
       "<tr><td>max tpr</td>\n",
       "<td>0.0022037</td>\n",
       "<td>1.0</td>\n",
       "<td>399.0</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-5.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-5 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-5 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-5 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-5 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-5 .h2o-table th,\n",
       "#h2o-table-5 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-5 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-5\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Gains/Lift Table: Avg response rate: 49.98 %, avg score: 49.98 %</caption>\n",
       "    <thead><tr><th>group</th>\n",
       "<th>cumulative_data_fraction</th>\n",
       "<th>lower_threshold</th>\n",
       "<th>lift</th>\n",
       "<th>cumulative_lift</th>\n",
       "<th>response_rate</th>\n",
       "<th>score</th>\n",
       "<th>cumulative_response_rate</th>\n",
       "<th>cumulative_score</th>\n",
       "<th>capture_rate</th>\n",
       "<th>cumulative_capture_rate</th>\n",
       "<th>gain</th>\n",
       "<th>cumulative_gain</th>\n",
       "<th>kolmogorov_smirnov</th></tr></thead>\n",
       "    <tbody><tr><td>1</td>\n",
       "<td>0.0100003</td>\n",
       "<td>0.9732036</td>\n",
       "<td>1.8614024</td>\n",
       "<td>1.8614024</td>\n",
       "<td>0.9304035</td>\n",
       "<td>0.9892117</td>\n",
       "<td>0.9304035</td>\n",
       "<td>0.9892117</td>\n",
       "<td>0.0186146</td>\n",
       "<td>0.0186146</td>\n",
       "<td>86.1402369</td>\n",
       "<td>86.1402369</td>\n",
       "<td>0.0172231</td></tr>\n",
       "<tr><td>2</td>\n",
       "<td>0.0200006</td>\n",
       "<td>0.9480514</td>\n",
       "<td>1.8983018</td>\n",
       "<td>1.8798521</td>\n",
       "<td>0.9488473</td>\n",
       "<td>0.9598031</td>\n",
       "<td>0.9396254</td>\n",
       "<td>0.9745074</td>\n",
       "<td>0.0189836</td>\n",
       "<td>0.0375982</td>\n",
       "<td>89.8301781</td>\n",
       "<td>87.9852075</td>\n",
       "<td>0.0351839</td></tr>\n",
       "<tr><td>3</td>\n",
       "<td>0.0300010</td>\n",
       "<td>0.9303216</td>\n",
       "<td>1.8761045</td>\n",
       "<td>1.8786029</td>\n",
       "<td>0.9377522</td>\n",
       "<td>0.9388804</td>\n",
       "<td>0.9390010</td>\n",
       "<td>0.9626317</td>\n",
       "<td>0.0187616</td>\n",
       "<td>0.0563599</td>\n",
       "<td>87.6104479</td>\n",
       "<td>87.8602877</td>\n",
       "<td>0.0527010</td></tr>\n",
       "<tr><td>4</td>\n",
       "<td>0.0400013</td>\n",
       "<td>0.9163793</td>\n",
       "<td>1.8501596</td>\n",
       "<td>1.8714921</td>\n",
       "<td>0.9247839</td>\n",
       "<td>0.9231981</td>\n",
       "<td>0.9354467</td>\n",
       "<td>0.9527733</td>\n",
       "<td>0.0185022</td>\n",
       "<td>0.0748621</td>\n",
       "<td>85.0159580</td>\n",
       "<td>87.1492052</td>\n",
       "<td>0.0696993</td></tr>\n",
       "<tr><td>5</td>\n",
       "<td>0.0500001</td>\n",
       "<td>0.9044548</td>\n",
       "<td>1.8221710</td>\n",
       "<td>1.8616290</td>\n",
       "<td>0.9107941</td>\n",
       "<td>0.9102638</td>\n",
       "<td>0.9305167</td>\n",
       "<td>0.9442724</td>\n",
       "<td>0.0182197</td>\n",
       "<td>0.0930817</td>\n",
       "<td>82.2171028</td>\n",
       "<td>86.1628985</td>\n",
       "<td>0.0861356</td></tr>\n",
       "<tr><td>6</td>\n",
       "<td>0.1000003</td>\n",
       "<td>0.8547810</td>\n",
       "<td>1.7397998</td>\n",
       "<td>1.8007144</td>\n",
       "<td>0.8696216</td>\n",
       "<td>0.8788137</td>\n",
       "<td>0.9000692</td>\n",
       "<td>0.9115431</td>\n",
       "<td>0.0869902</td>\n",
       "<td>0.1800720</td>\n",
       "<td>73.9799759</td>\n",
       "<td>80.0714372</td>\n",
       "<td>0.1600921</td></tr>\n",
       "<tr><td>7</td>\n",
       "<td>0.1500004</td>\n",
       "<td>0.8114600</td>\n",
       "<td>1.6437432</td>\n",
       "<td>1.7483907</td>\n",
       "<td>0.8216087</td>\n",
       "<td>0.8330097</td>\n",
       "<td>0.8739157</td>\n",
       "<td>0.8853653</td>\n",
       "<td>0.0821874</td>\n",
       "<td>0.2622594</td>\n",
       "<td>64.3743209</td>\n",
       "<td>74.8390651</td>\n",
       "<td>0.2244460</td></tr>\n",
       "<tr><td>8</td>\n",
       "<td>0.2000006</td>\n",
       "<td>0.7696219</td>\n",
       "<td>1.5619856</td>\n",
       "<td>1.7017894</td>\n",
       "<td>0.7807430</td>\n",
       "<td>0.7904803</td>\n",
       "<td>0.8506225</td>\n",
       "<td>0.8616440</td>\n",
       "<td>0.0780995</td>\n",
       "<td>0.3403589</td>\n",
       "<td>56.1985593</td>\n",
       "<td>70.1789386</td>\n",
       "<td>0.2806268</td></tr>\n",
       "<tr><td>9</td>\n",
       "<td>0.3000009</td>\n",
       "<td>0.6833955</td>\n",
       "<td>1.4441059</td>\n",
       "<td>1.6158949</td>\n",
       "<td>0.7218220</td>\n",
       "<td>0.7270193</td>\n",
       "<td>0.8076890</td>\n",
       "<td>0.8167691</td>\n",
       "<td>0.1444110</td>\n",
       "<td>0.4847699</td>\n",
       "<td>44.4105871</td>\n",
       "<td>61.5894881</td>\n",
       "<td>0.3694198</td></tr>\n",
       "<tr><td>10</td>\n",
       "<td>0.3999997</td>\n",
       "<td>0.5894440</td>\n",
       "<td>1.2854235</td>\n",
       "<td>1.5332779</td>\n",
       "<td>0.6425062</td>\n",
       "<td>0.6369694</td>\n",
       "<td>0.7663937</td>\n",
       "<td>0.7718197</td>\n",
       "<td>0.1285409</td>\n",
       "<td>0.6133107</td>\n",
       "<td>28.5423521</td>\n",
       "<td>53.3277934</td>\n",
       "<td>0.4264856</td></tr>\n",
       "<tr><td>11</td>\n",
       "<td>0.5</td>\n",
       "<td>0.4912574</td>\n",
       "<td>1.1327005</td>\n",
       "<td>1.4531622</td>\n",
       "<td>0.5661691</td>\n",
       "<td>0.5404815</td>\n",
       "<td>0.7263487</td>\n",
       "<td>0.7255519</td>\n",
       "<td>0.1132704</td>\n",
       "<td>0.7265811</td>\n",
       "<td>13.2700453</td>\n",
       "<td>45.3162207</td>\n",
       "<td>0.4530173</td></tr>\n",
       "<tr><td>12</td>\n",
       "<td>0.6000003</td>\n",
       "<td>0.3958836</td>\n",
       "<td>0.9457765</td>\n",
       "<td>1.3685977</td>\n",
       "<td>0.4727370</td>\n",
       "<td>0.4430973</td>\n",
       "<td>0.6840800</td>\n",
       "<td>0.6784760</td>\n",
       "<td>0.0945779</td>\n",
       "<td>0.8211590</td>\n",
       "<td>-5.4223518</td>\n",
       "<td>36.8597716</td>\n",
       "<td>0.4421760</td></tr>\n",
       "<tr><td>13</td>\n",
       "<td>0.6999991</td>\n",
       "<td>0.3090049</td>\n",
       "<td>0.7450256</td>\n",
       "<td>1.2795169</td>\n",
       "<td>0.3723936</td>\n",
       "<td>0.3514138</td>\n",
       "<td>0.6395538</td>\n",
       "<td>0.6317534</td>\n",
       "<td>0.0745017</td>\n",
       "<td>0.8956607</td>\n",
       "<td>-25.4974432</td>\n",
       "<td>27.9516898</td>\n",
       "<td>0.3911980</td></tr>\n",
       "<tr><td>14</td>\n",
       "<td>0.7999994</td>\n",
       "<td>0.2323020</td>\n",
       "<td>0.5524116</td>\n",
       "<td>1.1886284</td>\n",
       "<td>0.2761175</td>\n",
       "<td>0.2700472</td>\n",
       "<td>0.5941241</td>\n",
       "<td>0.5865399</td>\n",
       "<td>0.0552413</td>\n",
       "<td>0.9509020</td>\n",
       "<td>-44.7588352</td>\n",
       "<td>18.8628414</td>\n",
       "<td>0.3017087</td></tr>\n",
       "<tr><td>15</td>\n",
       "<td>0.8999997</td>\n",
       "<td>0.1576588</td>\n",
       "<td>0.3442411</td>\n",
       "<td>1.0948073</td>\n",
       "<td>0.1720655</td>\n",
       "<td>0.1952650</td>\n",
       "<td>0.5472285</td>\n",
       "<td>0.5430648</td>\n",
       "<td>0.0344242</td>\n",
       "<td>0.9853263</td>\n",
       "<td>-65.5758924</td>\n",
       "<td>9.4807298</td>\n",
       "<td>0.1705985</td></tr>\n",
       "<tr><td>16</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0000000</td>\n",
       "<td>0.1467370</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0733451</td>\n",
       "<td>0.1107873</td>\n",
       "<td>0.4998401</td>\n",
       "<td>0.4998369</td>\n",
       "<td>0.0146737</td>\n",
       "<td>1.0</td>\n",
       "<td>-85.3262953</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div></div>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-6.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-6 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-6 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-6 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-6 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-6 .h2o-table th,\n",
       "#h2o-table-6 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-6 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-6\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Scoring History: </caption>\n",
       "    <thead><tr><th></th>\n",
       "<th>timestamp</th>\n",
       "<th>duration</th>\n",
       "<th>iterations</th>\n",
       "<th>negative_log_likelihood</th>\n",
       "<th>objective</th>\n",
       "<th>training_rmse</th>\n",
       "<th>training_logloss</th>\n",
       "<th>training_r2</th>\n",
       "<th>training_auc</th>\n",
       "<th>training_pr_auc</th>\n",
       "<th>training_lift</th>\n",
       "<th>training_classification_error</th></tr></thead>\n",
       "    <tbody><tr><td></td>\n",
       "<td>2023-11-09 20:57:55</td>\n",
       "<td> 0.000 sec</td>\n",
       "<td>0</td>\n",
       "<td>481028.8585618</td>\n",
       "<td>0.6931471</td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td></td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 20:57:55</td>\n",
       "<td> 0.375 sec</td>\n",
       "<td>1</td>\n",
       "<td>384210.9103473</td>\n",
       "<td>0.5543350</td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td></td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 20:57:55</td>\n",
       "<td> 0.534 sec</td>\n",
       "<td>2</td>\n",
       "<td>378031.0794664</td>\n",
       "<td>0.5456495</td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td></td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 20:57:55</td>\n",
       "<td> 0.686 sec</td>\n",
       "<td>3</td>\n",
       "<td>377468.3311488</td>\n",
       "<td>0.5449421</td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td></td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 20:57:55</td>\n",
       "<td> 0.833 sec</td>\n",
       "<td>4</td>\n",
       "<td>377450.5522940</td>\n",
       "<td>0.5449306</td>\n",
       "<td>0.4261596</td>\n",
       "<td>0.5427302</td>\n",
       "<td>0.2735519</td>\n",
       "<td>0.8026331</td>\n",
       "<td>0.7901412</td>\n",
       "<td>1.8614024</td>\n",
       "<td>0.2903104</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-7.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-7 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-7 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-7 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-7 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-7 .h2o-table th,\n",
       "#h2o-table-7 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-7 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-7\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Variable Importances: </caption>\n",
       "    <thead><tr><th>variable</th>\n",
       "<th>relative_importance</th>\n",
       "<th>scaled_importance</th>\n",
       "<th>percentage</th></tr></thead>\n",
       "    <tbody><tr><td>gamma_GTP</td>\n",
       "<td>0.6222330</td>\n",
       "<td>1.0</td>\n",
       "<td>0.1358916</td></tr>\n",
       "<tr><td>age</td>\n",
       "<td>0.5635687</td>\n",
       "<td>0.9057196</td>\n",
       "<td>0.1230797</td></tr>\n",
       "<tr><td>sex.Female</td>\n",
       "<td>0.5157085</td>\n",
       "<td>0.8288028</td>\n",
       "<td>0.1126273</td></tr>\n",
       "<tr><td>sex.Male</td>\n",
       "<td>0.4810171</td>\n",
       "<td>0.7730498</td>\n",
       "<td>0.1050510</td></tr>\n",
       "<tr><td>SGOT_ALT</td>\n",
       "<td>0.4658439</td>\n",
       "<td>0.7486648</td>\n",
       "<td>0.1017372</td></tr>\n",
       "<tr><td>HDL_chole</td>\n",
       "<td>0.4419855</td>\n",
       "<td>0.7103216</td>\n",
       "<td>0.0965267</td></tr>\n",
       "<tr><td>SMK_stat_type_cd</td>\n",
       "<td>0.3834678</td>\n",
       "<td>0.6162769</td>\n",
       "<td>0.0837468</td></tr>\n",
       "<tr><td>SGOT_AST</td>\n",
       "<td>0.2612754</td>\n",
       "<td>0.4198996</td>\n",
       "<td>0.0570608</td></tr>\n",
       "<tr><td>LDL_chole</td>\n",
       "<td>0.1358204</td>\n",
       "<td>0.2182790</td>\n",
       "<td>0.0296623</td></tr>\n",
       "<tr><td>height</td>\n",
       "<td>0.1320439</td>\n",
       "<td>0.2122097</td>\n",
       "<td>0.0288375</td></tr>\n",
       "<tr><td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td></tr>\n",
       "<tr><td>tot_chole</td>\n",
       "<td>0.0593596</td>\n",
       "<td>0.0953977</td>\n",
       "<td>0.0129637</td></tr>\n",
       "<tr><td>hemoglobin</td>\n",
       "<td>0.0334049</td>\n",
       "<td>0.0536855</td>\n",
       "<td>0.0072954</td></tr>\n",
       "<tr><td>SBP</td>\n",
       "<td>0.0229388</td>\n",
       "<td>0.0368653</td>\n",
       "<td>0.0050097</td></tr>\n",
       "<tr><td>hear_right</td>\n",
       "<td>0.0134106</td>\n",
       "<td>0.0215524</td>\n",
       "<td>0.0029288</td></tr>\n",
       "<tr><td>urine_protein</td>\n",
       "<td>0.0104427</td>\n",
       "<td>0.0167827</td>\n",
       "<td>0.0022806</td></tr>\n",
       "<tr><td>hear_left</td>\n",
       "<td>0.0095062</td>\n",
       "<td>0.0152775</td>\n",
       "<td>0.0020761</td></tr>\n",
       "<tr><td>waistline</td>\n",
       "<td>0.0086293</td>\n",
       "<td>0.0138683</td>\n",
       "<td>0.0018846</td></tr>\n",
       "<tr><td>BLDS</td>\n",
       "<td>0.0084474</td>\n",
       "<td>0.0135760</td>\n",
       "<td>0.0018449</td></tr>\n",
       "<tr><td>sight_left</td>\n",
       "<td>0.0023347</td>\n",
       "<td>0.0037522</td>\n",
       "<td>0.0005099</td></tr>\n",
       "<tr><td>sight_right</td>\n",
       "<td>0.0020300</td>\n",
       "<td>0.0032624</td>\n",
       "<td>0.0004433</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "<pre style='font-size: smaller; margin-bottom: 1em;'>[24 rows x 4 columns]</pre></div><pre style=\"font-size: smaller; margin: 1em 0 0 0;\">\n",
       "\n",
       "[tips]\n",
       "Use `model.explain()` to inspect the model.\n",
       "--\n",
       "Use `h2o.display.toggle_user_tips()` to switch on/off this section.</pre>"
      ],
      "text/plain": [
       "Model Details\n",
       "=============\n",
       "H2OGeneralizedLinearEstimator : Generalized Linear Modeling\n",
       "Model Key: GLM_model_python_1699563465891_2\n",
       "\n",
       "\n",
       "GLM Model: summary\n",
       "    family    link    regularization                                 number_of_predictors_total    number_of_active_predictors    number_of_iterations    training_frame\n",
       "--  --------  ------  ---------------------------------------------  ----------------------------  -----------------------------  ----------------------  ----------------\n",
       "    binomial  logit   Elastic Net (alpha = 0.5, lambda = 3.745E-4 )  24                            24                             4                       py_17_sid_9352\n",
       "\n",
       "ModelMetricsBinomialGLM: glm\n",
       "** Reported on train data. **\n",
       "\n",
       "MSE: 0.18161199755844645\n",
       "RMSE: 0.4261595916536978\n",
       "LogLoss: 0.5427301647441274\n",
       "AUC: 0.8026331114268583\n",
       "AUCPR: 0.7901412154675567\n",
       "Gini: 0.6052662228537167\n",
       "Null degrees of freedom: 693977\n",
       "Residual degrees of freedom: 693953\n",
       "Null deviance: 962057.7171236364\n",
       "Residual deviance: 754901.1045879977\n",
       "AIC: 754951.1045879977\n",
       "\n",
       "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.34828097161278276\n",
       "       N       Y       Error    Rate\n",
       "-----  ------  ------  -------  -------------------\n",
       "N      192692  154408  0.4449   (154408.0/347100.0)\n",
       "Y      47061   299817  0.1357   (47061.0/346878.0)\n",
       "Total  239753  454225  0.2903   (201469.0/693978.0)\n",
       "\n",
       "Maximum Metrics: Maximum metrics at their respective thresholds\n",
       "metric                       threshold    value     idx\n",
       "---------------------------  -----------  --------  -----\n",
       "max f1                       0.348281     0.74851   265\n",
       "max f2                       0.197698     0.851446  332\n",
       "max f0point5                 0.570703     0.731129  171\n",
       "max accuracy                 0.485052     0.72662   208\n",
       "max precision                0.941357     0.940084  17\n",
       "max recall                   0.00220374   1         399\n",
       "max specificity              0.999271     0.999542  0\n",
       "max absolute_mcc             0.442766     0.454212  225\n",
       "max min_per_class_accuracy   0.492608     0.72603   204\n",
       "max mean_per_class_accuracy  0.485052     0.726622  208\n",
       "max tns                      0.999271     346941    0\n",
       "max fns                      0.999271     345214    0\n",
       "max fps                      0.00220374   347100    399\n",
       "max tps                      0.00220374   346878    399\n",
       "max tnr                      0.999271     0.999542  0\n",
       "max fnr                      0.999271     0.995203  0\n",
       "max fpr                      0.00220374   1         399\n",
       "max tpr                      0.00220374   1         399\n",
       "\n",
       "Gains/Lift Table: Avg response rate: 49.98 %, avg score: 49.98 %\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        0.0100003                   0.973204           1.8614    1.8614             0.930403         0.989212  0.930403                    0.989212            0.0186146       0.0186146                  86.1402   86.1402            0.0172231\n",
       "2        0.0200006                   0.948051           1.8983    1.87985            0.948847         0.959803  0.939625                    0.974507            0.0189836       0.0375982                  89.8302   87.9852            0.0351839\n",
       "3        0.030001                    0.930322           1.8761    1.8786             0.937752         0.93888   0.939001                    0.962632            0.0187616       0.0563599                  87.6104   87.8603            0.052701\n",
       "4        0.0400013                   0.916379           1.85016   1.87149            0.924784         0.923198  0.935447                    0.952773            0.0185022       0.0748621                  85.016    87.1492            0.0696993\n",
       "5        0.0500001                   0.904455           1.82217   1.86163            0.910794         0.910264  0.930517                    0.944272            0.0182197       0.0930817                  82.2171   86.1629            0.0861356\n",
       "6        0.1                         0.854781           1.7398    1.80071            0.869622         0.878814  0.900069                    0.911543            0.0869902       0.180072                   73.98     80.0714            0.160092\n",
       "7        0.15                        0.81146            1.64374   1.74839            0.821609         0.83301   0.873916                    0.885365            0.0821874       0.262259                   64.3743   74.8391            0.224446\n",
       "8        0.200001                    0.769622           1.56199   1.70179            0.780743         0.79048   0.850622                    0.861644            0.0780995       0.340359                   56.1986   70.1789            0.280627\n",
       "9        0.300001                    0.683396           1.44411   1.61589            0.721822         0.727019  0.807689                    0.816769            0.144411        0.48477                    44.4106   61.5895            0.36942\n",
       "10       0.4                         0.589444           1.28542   1.53328            0.642506         0.636969  0.766394                    0.77182             0.128541        0.613311                   28.5424   53.3278            0.426486\n",
       "11       0.5                         0.491257           1.1327    1.45316            0.566169         0.540482  0.726349                    0.725552            0.11327         0.726581                   13.27     45.3162            0.453017\n",
       "12       0.6                         0.395884           0.945776  1.3686             0.472737         0.443097  0.68408                     0.678476            0.0945779       0.821159                   -5.42235  36.8598            0.442176\n",
       "13       0.699999                    0.309005           0.745026  1.27952            0.372394         0.351414  0.639554                    0.631753            0.0745017       0.895661                   -25.4974  27.9517            0.391198\n",
       "14       0.799999                    0.232302           0.552412  1.18863            0.276117         0.270047  0.594124                    0.58654             0.0552413       0.950902                   -44.7588  18.8628            0.301709\n",
       "15       0.9                         0.157659           0.344241  1.09481            0.172065         0.195265  0.547229                    0.543065            0.0344242       0.985326                   -65.5759  9.48073            0.170599\n",
       "16       1                           2.61473e-47        0.146737  1                  0.0733451        0.110787  0.49984                     0.499837            0.0146737       1                          -85.3263  0                  0\n",
       "\n",
       "Scoring History: \n",
       "    timestamp            duration    iterations    negative_log_likelihood    objective    training_rmse       training_logloss    training_r2          training_auc        training_pr_auc     training_lift      training_classification_error\n",
       "--  -------------------  ----------  ------------  -------------------------  -----------  ------------------  ------------------  -------------------  ------------------  ------------------  -----------------  -------------------------------\n",
       "    2023-11-09 20:57:55  0.000 sec   0             481029                     0.693147\n",
       "    2023-11-09 20:57:55  0.375 sec   1             384211                     0.554335\n",
       "    2023-11-09 20:57:55  0.534 sec   2             378031                     0.54565\n",
       "    2023-11-09 20:57:55  0.686 sec   3             377468                     0.544942\n",
       "    2023-11-09 20:57:55  0.833 sec   4             377451                     0.544931     0.4261595916536978  0.5427301647441274  0.27355193542680356  0.8026331114268583  0.7901412154675567  1.861402369489905  0.2903103556596895\n",
       "\n",
       "Variable Importances: \n",
       "variable          relative_importance    scaled_importance      percentage\n",
       "----------------  ---------------------  ---------------------  ---------------------\n",
       "gamma_GTP         0.6222330331802368     1.0                    0.13589158393757586\n",
       "age               0.563568651676178      0.9057195963958634     0.12307967055753581\n",
       "sex.Female        0.5157085061073303     0.82880284171276       0.11262733093231092\n",
       "sex.Male          0.4810171127319336     0.7730497853407946     0.1050509597925636\n",
       "SGOT_ALT          0.46584394574165344    0.7486647620759094     0.10173724035674371\n",
       "HDL_chole         0.441985547542572      0.7103215740308437     0.09652672380008341\n",
       "SMK_stat_type_cd  0.38346782326698303    0.6162768654487478     0.08374683938991466\n",
       "SGOT_AST          0.26127541065216064    0.4198996143242033     0.05706082368529321\n",
       "LDL_chole         0.13582037389278412    0.21827895121318996    0.02966227242059323\n",
       "height            0.13204389810562134    0.2122097205780673     0.028837515056303956\n",
       "---               ---                    ---                    ---\n",
       "tot_chole         0.05935962125658989    0.09539773379308152    0.012963749149197054\n",
       "hemoglobin        0.033404912799596786   0.053685534226404014   0.007295412280560983\n",
       "SBP               0.02293883077800274    0.03686533750990724    0.005009689106614624\n",
       "hear_right        0.013410615734755993   0.021552400820339376   0.0029287898851334273\n",
       "urine_protein     0.01044274028390646    0.016782683861275495   0.0022806254926323187\n",
       "hear_left         0.009506171569228172   0.015277510293277218   0.0020760850723760605\n",
       "waistline         0.00862930528819561    0.013868285397982133   0.0018845832692301466\n",
       "BLDS              0.008447404950857162   0.0135759506493611     0.0018448574372000415\n",
       "sight_left        0.002334748161956668   0.0037522086380142113  0.0005098935750840055\n",
       "sight_right       0.002029964467510581   0.00326238621105572    0.0004433308296364685\n",
       "[24 rows x 4 columns]\n",
       "\n",
       "\n",
       "[tips]\n",
       "Use `model.explain()` to inspect the model.\n",
       "--\n",
       "Use `h2o.display.toggle_user_tips()` to switch on/off this section."
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = H2OGeneralizedLinearEstimator(family = \"binomial\")\n",
    "model.train(y = y, x = x, training_frame = train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "65ff4f5a-7e74-43f8-bfd0-03bbf6e6c45a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.80224\n",
      "Gini: 0.60447\n"
     ]
    }
   ],
   "source": [
    "# Eval performance:\n",
    "valid_perf1 = model.model_performance(valid)\n",
    "\n",
    "# Retreive test set AUC and Gini\n",
    "print(\"AUC: \" + str(round(valid_perf1.auc(), 5)))\n",
    "print(\"Gini: \" + str(round(valid_perf1.gini(), 5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0e591861-f81d-4ca6-b1f5-1a84da72e7cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "glm prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n",
      "Prediction      N       Y\n",
      "Original                 \n",
      "N           82543   66215\n",
      "Y           20177  128433\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Prediction', ylabel='Original'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = 'DRK_YN'\n",
    "y_df = valid[y].as_data_frame()\n",
    "y_orig = y_df[y]\n",
    "\n",
    "valid_pred = model.predict(valid).as_data_frame()\n",
    "y_pred = valid_pred['predict']\n",
    "\n",
    "ct_target = pd.crosstab(y_orig, y_pred, rownames = [\"Original\"], colnames = [\"Prediction\"])\n",
    "print(ct_target)\n",
    "ct_normalize = pd.crosstab(y_orig, y_pred, rownames = [\"Original\"], colnames = [\"Prediction\"], normalize = True)\n",
    "sns.heatmap(ct_normalize, cmap='coolwarm', annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6b47681c-77bb-4d9e-b386-a75ca37ae8b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from h2o.estimators import H2ODeepLearningEstimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cb4ea7fe-639d-4218-9c0e-e1343183fa6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning Model Build progress: |█████████████████████████████████████████████| (done) 100%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style='margin: 1em 0 1em 0;'>Model Details\n",
       "=============\n",
       "H2ODeepLearningEstimator : Deep Learning\n",
       "Model Key: DeepLearning_model_python_1699563465891_5\n",
       "</pre>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-8.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-8 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-8 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-8 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-8 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-8 .h2o-table th,\n",
       "#h2o-table-8 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-8 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-8\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Status of Neuron Layers: predicting DRK_YN, 2-class classification, bernoulli distribution, CrossEntropy loss, 25,502 weights/biases, 309.6 KB, 10,698,478 training samples, mini-batch size 1</caption>\n",
       "    <thead><tr><th></th>\n",
       "<th>layer</th>\n",
       "<th>units</th>\n",
       "<th>type</th>\n",
       "<th>dropout</th>\n",
       "<th>l1</th>\n",
       "<th>l2</th>\n",
       "<th>mean_rate</th>\n",
       "<th>rate_rms</th>\n",
       "<th>momentum</th>\n",
       "<th>mean_weight</th>\n",
       "<th>weight_rms</th>\n",
       "<th>mean_bias</th>\n",
       "<th>bias_rms</th></tr></thead>\n",
       "    <tbody><tr><td></td>\n",
       "<td>1</td>\n",
       "<td>25</td>\n",
       "<td>Input</td>\n",
       "<td>0.0</td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td></td></tr>\n",
       "<tr><td></td>\n",
       "<td>2</td>\n",
       "<td>200</td>\n",
       "<td>Rectifier</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0507984</td>\n",
       "<td>0.1974166</td>\n",
       "<td>0.0</td>\n",
       "<td>-0.0702604</td>\n",
       "<td>0.4442164</td>\n",
       "<td>-0.5752126</td>\n",
       "<td>0.4019700</td></tr>\n",
       "<tr><td></td>\n",
       "<td>3</td>\n",
       "<td>100</td>\n",
       "<td>Rectifier</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0894833</td>\n",
       "<td>0.1048682</td>\n",
       "<td>0.0</td>\n",
       "<td>-0.0737636</td>\n",
       "<td>0.1506706</td>\n",
       "<td>0.0912807</td>\n",
       "<td>0.2789736</td></tr>\n",
       "<tr><td></td>\n",
       "<td>4</td>\n",
       "<td>2</td>\n",
       "<td>Softmax</td>\n",
       "<td></td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0081332</td>\n",
       "<td>0.0050645</td>\n",
       "<td>0.0</td>\n",
       "<td>-0.0357213</td>\n",
       "<td>0.4314431</td>\n",
       "<td>0.0407364</td>\n",
       "<td>0.2137738</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div>\n",
       "<div style='margin: 1em 0 1em 0;'><pre style='margin: 1em 0 1em 0;'>ModelMetricsBinomial: deeplearning\n",
       "** Reported on train data. **\n",
       "\n",
       "MSE: 0.1710379054556747\n",
       "RMSE: 0.41356729253614183\n",
       "LogLoss: 0.5137337313144018\n",
       "Mean Per-Class Error: 0.2770641478773106\n",
       "AUC: 0.82481990656104\n",
       "AUCPR: 0.8262594945163201\n",
       "Gini: 0.64963981312208</pre>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-9.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-9 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-9 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-9 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-9 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-9 .h2o-table th,\n",
       "#h2o-table-9 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-9 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-9\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.35880479040318636</caption>\n",
       "    <thead><tr><th></th>\n",
       "<th>N</th>\n",
       "<th>Y</th>\n",
       "<th>Error</th>\n",
       "<th>Rate</th></tr></thead>\n",
       "    <tbody><tr><td>N</td>\n",
       "<td>2900.0</td>\n",
       "<td>2023.0</td>\n",
       "<td>0.4109</td>\n",
       "<td> (2023.0/4923.0)</td></tr>\n",
       "<tr><td>Y</td>\n",
       "<td>716.0</td>\n",
       "<td>4284.0</td>\n",
       "<td>0.1432</td>\n",
       "<td> (716.0/5000.0)</td></tr>\n",
       "<tr><td>Total</td>\n",
       "<td>3616.0</td>\n",
       "<td>6307.0</td>\n",
       "<td>0.276</td>\n",
       "<td> (2739.0/9923.0)</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-10.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-10 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-10 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-10 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-10 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-10 .h2o-table th,\n",
       "#h2o-table-10 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-10 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-10\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Maximum Metrics: Maximum metrics at their respective thresholds</caption>\n",
       "    <thead><tr><th>metric</th>\n",
       "<th>threshold</th>\n",
       "<th>value</th>\n",
       "<th>idx</th></tr></thead>\n",
       "    <tbody><tr><td>max f1</td>\n",
       "<td>0.3588048</td>\n",
       "<td>0.7577607</td>\n",
       "<td>260.0</td></tr>\n",
       "<tr><td>max f2</td>\n",
       "<td>0.1235820</td>\n",
       "<td>0.8564782</td>\n",
       "<td>349.0</td></tr>\n",
       "<tr><td>max f0point5</td>\n",
       "<td>0.6048966</td>\n",
       "<td>0.7612243</td>\n",
       "<td>158.0</td></tr>\n",
       "<tr><td>max accuracy</td>\n",
       "<td>0.5049249</td>\n",
       "<td>0.7449360</td>\n",
       "<td>200.0</td></tr>\n",
       "<tr><td>max precision</td>\n",
       "<td>0.9987594</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max recall</td>\n",
       "<td>0.0100392</td>\n",
       "<td>1.0</td>\n",
       "<td>397.0</td></tr>\n",
       "<tr><td>max specificity</td>\n",
       "<td>0.9987594</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max absolute_mcc</td>\n",
       "<td>0.5049249</td>\n",
       "<td>0.4900585</td>\n",
       "<td>200.0</td></tr>\n",
       "<tr><td>max min_per_class_accuracy</td>\n",
       "<td>0.4964393</td>\n",
       "<td>0.7440585</td>\n",
       "<td>204.0</td></tr>\n",
       "<tr><td>max mean_per_class_accuracy</td>\n",
       "<td>0.5049249</td>\n",
       "<td>0.7450090</td>\n",
       "<td>200.0</td></tr>\n",
       "<tr><td>max tns</td>\n",
       "<td>0.9987594</td>\n",
       "<td>4923.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max fns</td>\n",
       "<td>0.9987594</td>\n",
       "<td>4991.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max fps</td>\n",
       "<td>0.0031625</td>\n",
       "<td>4923.0</td>\n",
       "<td>399.0</td></tr>\n",
       "<tr><td>max tps</td>\n",
       "<td>0.0100392</td>\n",
       "<td>5000.0</td>\n",
       "<td>397.0</td></tr>\n",
       "<tr><td>max tnr</td>\n",
       "<td>0.9987594</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max fnr</td>\n",
       "<td>0.9987594</td>\n",
       "<td>0.9982</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max fpr</td>\n",
       "<td>0.0031625</td>\n",
       "<td>1.0</td>\n",
       "<td>399.0</td></tr>\n",
       "<tr><td>max tpr</td>\n",
       "<td>0.0100392</td>\n",
       "<td>1.0</td>\n",
       "<td>397.0</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-11.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-11 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-11 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-11 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-11 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-11 .h2o-table th,\n",
       "#h2o-table-11 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-11 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-11\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Gains/Lift Table: Avg response rate: 50.39 %, avg score: 49.08 %</caption>\n",
       "    <thead><tr><th>group</th>\n",
       "<th>cumulative_data_fraction</th>\n",
       "<th>lower_threshold</th>\n",
       "<th>lift</th>\n",
       "<th>cumulative_lift</th>\n",
       "<th>response_rate</th>\n",
       "<th>score</th>\n",
       "<th>cumulative_response_rate</th>\n",
       "<th>cumulative_score</th>\n",
       "<th>capture_rate</th>\n",
       "<th>cumulative_capture_rate</th>\n",
       "<th>gain</th>\n",
       "<th>cumulative_gain</th>\n",
       "<th>kolmogorov_smirnov</th></tr></thead>\n",
       "    <tbody><tr><td>1</td>\n",
       "<td>0.0100776</td>\n",
       "<td>0.9733905</td>\n",
       "<td>1.925062</td>\n",
       "<td>1.925062</td>\n",
       "<td>0.97</td>\n",
       "<td>0.9842775</td>\n",
       "<td>0.97</td>\n",
       "<td>0.9842775</td>\n",
       "<td>0.0194</td>\n",
       "<td>0.0194</td>\n",
       "<td>92.5062</td>\n",
       "<td>92.5062</td>\n",
       "<td>0.0187906</td></tr>\n",
       "<tr><td>2</td>\n",
       "<td>0.0200544</td>\n",
       "<td>0.9605196</td>\n",
       "<td>1.9244606</td>\n",
       "<td>1.9247628</td>\n",
       "<td>0.9696970</td>\n",
       "<td>0.9666305</td>\n",
       "<td>0.9698492</td>\n",
       "<td>0.9754983</td>\n",
       "<td>0.0192</td>\n",
       "<td>0.0386</td>\n",
       "<td>92.4460606</td>\n",
       "<td>92.4762814</td>\n",
       "<td>0.0373812</td></tr>\n",
       "<tr><td>3</td>\n",
       "<td>0.0300312</td>\n",
       "<td>0.9491019</td>\n",
       "<td>1.9044141</td>\n",
       "<td>1.9180027</td>\n",
       "<td>0.9595960</td>\n",
       "<td>0.9546041</td>\n",
       "<td>0.9664430</td>\n",
       "<td>0.9685570</td>\n",
       "<td>0.019</td>\n",
       "<td>0.0576</td>\n",
       "<td>90.4414141</td>\n",
       "<td>91.8002685</td>\n",
       "<td>0.0555687</td></tr>\n",
       "<tr><td>4</td>\n",
       "<td>0.0400081</td>\n",
       "<td>0.9402763</td>\n",
       "<td>1.9244606</td>\n",
       "<td>1.9196131</td>\n",
       "<td>0.9696970</td>\n",
       "<td>0.9449258</td>\n",
       "<td>0.9672544</td>\n",
       "<td>0.9626641</td>\n",
       "<td>0.0192</td>\n",
       "<td>0.0768</td>\n",
       "<td>92.4460606</td>\n",
       "<td>91.9613098</td>\n",
       "<td>0.0741593</td></tr>\n",
       "<tr><td>5</td>\n",
       "<td>0.0500857</td>\n",
       "<td>0.9321217</td>\n",
       "<td>1.8456780</td>\n",
       "<td>1.9047368</td>\n",
       "<td>0.93</td>\n",
       "<td>0.9362813</td>\n",
       "<td>0.9597586</td>\n",
       "<td>0.9573556</td>\n",
       "<td>0.0186</td>\n",
       "<td>0.0954</td>\n",
       "<td>84.5678000</td>\n",
       "<td>90.4736821</td>\n",
       "<td>0.0913374</td></tr>\n",
       "<tr><td>6</td>\n",
       "<td>0.1000705</td>\n",
       "<td>0.8884911</td>\n",
       "<td>1.8285528</td>\n",
       "<td>1.8666832</td>\n",
       "<td>0.9213710</td>\n",
       "<td>0.9101899</td>\n",
       "<td>0.9405841</td>\n",
       "<td>0.9337965</td>\n",
       "<td>0.0914</td>\n",
       "<td>0.1868</td>\n",
       "<td>82.8552823</td>\n",
       "<td>86.6683182</td>\n",
       "<td>0.1748154</td></tr>\n",
       "<tr><td>7</td>\n",
       "<td>0.1500554</td>\n",
       "<td>0.8401706</td>\n",
       "<td>1.7565310</td>\n",
       "<td>1.8299905</td>\n",
       "<td>0.8850806</td>\n",
       "<td>0.8650450</td>\n",
       "<td>0.9220954</td>\n",
       "<td>0.9108948</td>\n",
       "<td>0.0878</td>\n",
       "<td>0.2746</td>\n",
       "<td>75.6531048</td>\n",
       "<td>82.9990463</td>\n",
       "<td>0.2510371</td></tr>\n",
       "<tr><td>8</td>\n",
       "<td>0.2000403</td>\n",
       "<td>0.7926690</td>\n",
       "<td>1.6645032</td>\n",
       "<td>1.7886395</td>\n",
       "<td>0.8387097</td>\n",
       "<td>0.8168859</td>\n",
       "<td>0.9012594</td>\n",
       "<td>0.8874044</td>\n",
       "<td>0.0832</td>\n",
       "<td>0.3578</td>\n",
       "<td>66.4503226</td>\n",
       "<td>78.8639496</td>\n",
       "<td>0.3179869</td></tr>\n",
       "<tr><td>9</td>\n",
       "<td>0.3000101</td>\n",
       "<td>0.6877427</td>\n",
       "<td>1.4924512</td>\n",
       "<td>1.6899432</td>\n",
       "<td>0.7520161</td>\n",
       "<td>0.7413596</td>\n",
       "<td>0.8515284</td>\n",
       "<td>0.8387392</td>\n",
       "<td>0.1492</td>\n",
       "<td>0.507</td>\n",
       "<td>49.2451210</td>\n",
       "<td>68.9943231</td>\n",
       "<td>0.4172173</td></tr>\n",
       "<tr><td>10</td>\n",
       "<td>0.3999798</td>\n",
       "<td>0.5877986</td>\n",
       "<td>1.2743853</td>\n",
       "<td>1.5860799</td>\n",
       "<td>0.6421371</td>\n",
       "<td>0.6383710</td>\n",
       "<td>0.7991938</td>\n",
       "<td>0.7886597</td>\n",
       "<td>0.1274</td>\n",
       "<td>0.6344</td>\n",
       "<td>27.4385282</td>\n",
       "<td>58.6079919</td>\n",
       "<td>0.4725068</td></tr>\n",
       "<tr><td>11</td>\n",
       "<td>0.5000504</td>\n",
       "<td>0.4983443</td>\n",
       "<td>1.0792387</td>\n",
       "<td>1.4846504</td>\n",
       "<td>0.5438066</td>\n",
       "<td>0.5405498</td>\n",
       "<td>0.7480854</td>\n",
       "<td>0.7390078</td>\n",
       "<td>0.108</td>\n",
       "<td>0.7424</td>\n",
       "<td>7.9238671</td>\n",
       "<td>48.4650383</td>\n",
       "<td>0.4884898</td></tr>\n",
       "<tr><td>12</td>\n",
       "<td>0.6000202</td>\n",
       "<td>0.3908746</td>\n",
       "<td>0.8622607</td>\n",
       "<td>1.3809536</td>\n",
       "<td>0.4344758</td>\n",
       "<td>0.4432044</td>\n",
       "<td>0.6958347</td>\n",
       "<td>0.6897238</td>\n",
       "<td>0.0862</td>\n",
       "<td>0.8286</td>\n",
       "<td>-13.7739315</td>\n",
       "<td>38.0953611</td>\n",
       "<td>0.4607349</td></tr>\n",
       "<tr><td>13</td>\n",
       "<td>0.6999899</td>\n",
       "<td>0.2941549</td>\n",
       "<td>0.7262196</td>\n",
       "<td>1.2874471</td>\n",
       "<td>0.3659274</td>\n",
       "<td>0.3436684</td>\n",
       "<td>0.6487187</td>\n",
       "<td>0.6403015</td>\n",
       "<td>0.0726</td>\n",
       "<td>0.9012</td>\n",
       "<td>-27.3780444</td>\n",
       "<td>28.7447106</td>\n",
       "<td>0.4055673</td></tr>\n",
       "<tr><td>14</td>\n",
       "<td>0.7999597</td>\n",
       "<td>0.1902475</td>\n",
       "<td>0.5221579</td>\n",
       "<td>1.1918101</td>\n",
       "<td>0.2631048</td>\n",
       "<td>0.2414772</td>\n",
       "<td>0.6005291</td>\n",
       "<td>0.5904610</td>\n",
       "<td>0.0522</td>\n",
       "<td>0.9534</td>\n",
       "<td>-47.7842137</td>\n",
       "<td>19.1810053</td>\n",
       "<td>0.3092806</td></tr>\n",
       "<tr><td>15</td>\n",
       "<td>0.8999295</td>\n",
       "<td>0.0879891</td>\n",
       "<td>0.3321004</td>\n",
       "<td>1.0963082</td>\n",
       "<td>0.1673387</td>\n",
       "<td>0.1378345</td>\n",
       "<td>0.5524076</td>\n",
       "<td>0.5401805</td>\n",
       "<td>0.0332</td>\n",
       "<td>0.9866</td>\n",
       "<td>-66.7899597</td>\n",
       "<td>9.6308152</td>\n",
       "<td>0.1746967</td></tr>\n",
       "<tr><td>16</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0000001</td>\n",
       "<td>0.1339055</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0674723</td>\n",
       "<td>0.0462266</td>\n",
       "<td>0.5038799</td>\n",
       "<td>0.4907502</td>\n",
       "<td>0.0134</td>\n",
       "<td>1.0</td>\n",
       "<td>-86.6094461</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div></div>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-12.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-12 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-12 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-12 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-12 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-12 .h2o-table th,\n",
       "#h2o-table-12 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-12 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-12\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Scoring History: </caption>\n",
       "    <thead><tr><th></th>\n",
       "<th>timestamp</th>\n",
       "<th>duration</th>\n",
       "<th>training_speed</th>\n",
       "<th>epochs</th>\n",
       "<th>iterations</th>\n",
       "<th>samples</th>\n",
       "<th>training_rmse</th>\n",
       "<th>training_logloss</th>\n",
       "<th>training_r2</th>\n",
       "<th>training_auc</th>\n",
       "<th>training_pr_auc</th>\n",
       "<th>training_lift</th>\n",
       "<th>training_classification_error</th></tr></thead>\n",
       "    <tbody><tr><td></td>\n",
       "<td>2023-11-09 20:57:58</td>\n",
       "<td> 0.000 sec</td>\n",
       "<td>None</td>\n",
       "<td>0.0</td>\n",
       "<td>0</td>\n",
       "<td>0.0</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 20:58:00</td>\n",
       "<td> 2.969 sec</td>\n",
       "<td>43734 obs/sec</td>\n",
       "<td>0.1441890</td>\n",
       "<td>1</td>\n",
       "<td>100064.0</td>\n",
       "<td>0.4214957</td>\n",
       "<td>0.5321973</td>\n",
       "<td>0.2893227</td>\n",
       "<td>0.8133389</td>\n",
       "<td>0.8134093</td>\n",
       "<td>1.88537</td>\n",
       "<td>0.2945682</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 20:58:06</td>\n",
       "<td> 8.135 sec</td>\n",
       "<td>67860 obs/sec</td>\n",
       "<td>0.7216526</td>\n",
       "<td>5</td>\n",
       "<td>500811.0</td>\n",
       "<td>0.4183144</td>\n",
       "<td>0.5279712</td>\n",
       "<td>0.3000101</td>\n",
       "<td>0.8171398</td>\n",
       "<td>0.8193699</td>\n",
       "<td>1.925062</td>\n",
       "<td>0.2761262</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 20:58:12</td>\n",
       "<td>14.051 sec</td>\n",
       "<td>75659 obs/sec</td>\n",
       "<td>1.4430198</td>\n",
       "<td>10</td>\n",
       "<td>1001424.0</td>\n",
       "<td>0.4168423</td>\n",
       "<td>0.5218773</td>\n",
       "<td>0.3049283</td>\n",
       "<td>0.8198717</td>\n",
       "<td>0.8229799</td>\n",
       "<td>1.925062</td>\n",
       "<td>0.2702812</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 20:58:17</td>\n",
       "<td>19.525 sec</td>\n",
       "<td>80677 obs/sec</td>\n",
       "<td>2.1628884</td>\n",
       "<td>15</td>\n",
       "<td>1500997.0</td>\n",
       "<td>0.4167054</td>\n",
       "<td>0.5238950</td>\n",
       "<td>0.3053845</td>\n",
       "<td>0.8196822</td>\n",
       "<td>0.8212312</td>\n",
       "<td>1.925062</td>\n",
       "<td>0.2948705</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 20:58:23</td>\n",
       "<td>25.449 sec</td>\n",
       "<td>86128 obs/sec</td>\n",
       "<td>3.0274865</td>\n",
       "<td>21</td>\n",
       "<td>2101009.0</td>\n",
       "<td>0.4155590</td>\n",
       "<td>0.5195606</td>\n",
       "<td>0.3092014</td>\n",
       "<td>0.8218643</td>\n",
       "<td>0.8239958</td>\n",
       "<td>1.925062</td>\n",
       "<td>0.2893278</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 20:58:28</td>\n",
       "<td>30.787 sec</td>\n",
       "<td>87673 obs/sec</td>\n",
       "<td>3.7478537</td>\n",
       "<td>26</td>\n",
       "<td>2600928.0</td>\n",
       "<td>0.4152643</td>\n",
       "<td>0.5203957</td>\n",
       "<td>0.3101808</td>\n",
       "<td>0.8219544</td>\n",
       "<td>0.8242155</td>\n",
       "<td>1.905216</td>\n",
       "<td>0.2869092</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 20:58:34</td>\n",
       "<td>36.056 sec</td>\n",
       "<td>91739 obs/sec</td>\n",
       "<td>4.6113104</td>\n",
       "<td>32</td>\n",
       "<td>3200148.0</td>\n",
       "<td>0.4150428</td>\n",
       "<td>0.5175503</td>\n",
       "<td>0.3109163</td>\n",
       "<td>0.8222044</td>\n",
       "<td>0.8242950</td>\n",
       "<td>1.905216</td>\n",
       "<td>0.2768316</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 20:58:39</td>\n",
       "<td>41.292 sec</td>\n",
       "<td>94860 obs/sec</td>\n",
       "<td>5.4770756</td>\n",
       "<td>38</td>\n",
       "<td>3800970.0</td>\n",
       "<td>0.4148827</td>\n",
       "<td>0.5190103</td>\n",
       "<td>0.3114479</td>\n",
       "<td>0.8225747</td>\n",
       "<td>0.8249866</td>\n",
       "<td>1.944908</td>\n",
       "<td>0.2690719</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 20:58:44</td>\n",
       "<td>46.437 sec</td>\n",
       "<td>97440 obs/sec</td>\n",
       "<td>6.3394719</td>\n",
       "<td>44</td>\n",
       "<td>4399454.0</td>\n",
       "<td>0.4154049</td>\n",
       "<td>0.5207836</td>\n",
       "<td>0.3097135</td>\n",
       "<td>0.8232444</td>\n",
       "<td>0.8253728</td>\n",
       "<td>1.905216</td>\n",
       "<td>0.2746145</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 20:58:49</td>\n",
       "<td>51.512 sec</td>\n",
       "<td>99651 obs/sec</td>\n",
       "<td>7.2036822</td>\n",
       "<td>50</td>\n",
       "<td>4999197.0</td>\n",
       "<td>0.4138430</td>\n",
       "<td>0.5145960</td>\n",
       "<td>0.3148945</td>\n",
       "<td>0.8246510</td>\n",
       "<td>0.8266139</td>\n",
       "<td>1.925062</td>\n",
       "<td>0.2824751</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 20:58:54</td>\n",
       "<td>56.781 sec</td>\n",
       "<td>102900 obs/sec</td>\n",
       "<td>8.2119548</td>\n",
       "<td>57</td>\n",
       "<td>5698916.0</td>\n",
       "<td>0.4143879</td>\n",
       "<td>0.5156180</td>\n",
       "<td>0.3130893</td>\n",
       "<td>0.8231681</td>\n",
       "<td>0.8244900</td>\n",
       "<td>1.925062</td>\n",
       "<td>0.2758238</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 20:59:00</td>\n",
       "<td> 1 min  2.287 sec</td>\n",
       "<td>105192 obs/sec</td>\n",
       "<td>9.2211396</td>\n",
       "<td>64</td>\n",
       "<td>6399268.0</td>\n",
       "<td>0.4151440</td>\n",
       "<td>0.5198903</td>\n",
       "<td>0.3105803</td>\n",
       "<td>0.8228569</td>\n",
       "<td>0.8231903</td>\n",
       "<td>1.905216</td>\n",
       "<td>0.2766301</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 20:59:05</td>\n",
       "<td> 1 min  7.668 sec</td>\n",
       "<td>107297 obs/sec</td>\n",
       "<td>10.2288473</td>\n",
       "<td>71</td>\n",
       "<td>7098595.0</td>\n",
       "<td>0.4150013</td>\n",
       "<td>0.5202131</td>\n",
       "<td>0.3110542</td>\n",
       "<td>0.8232028</td>\n",
       "<td>0.8248345</td>\n",
       "<td>1.925062</td>\n",
       "<td>0.2695757</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 20:59:10</td>\n",
       "<td> 1 min 12.896 sec</td>\n",
       "<td>109315 obs/sec</td>\n",
       "<td>11.2359354</td>\n",
       "<td>78</td>\n",
       "<td>7797492.0</td>\n",
       "<td>0.4148937</td>\n",
       "<td>0.5163342</td>\n",
       "<td>0.3114115</td>\n",
       "<td>0.8224988</td>\n",
       "<td>0.8246335</td>\n",
       "<td>1.925062</td>\n",
       "<td>0.2770332</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 20:59:16</td>\n",
       "<td> 1 min 18.102 sec</td>\n",
       "<td>111099 obs/sec</td>\n",
       "<td>12.2446518</td>\n",
       "<td>85</td>\n",
       "<td>8497519.0</td>\n",
       "<td>0.4149350</td>\n",
       "<td>0.5180271</td>\n",
       "<td>0.3112744</td>\n",
       "<td>0.8221671</td>\n",
       "<td>0.8236192</td>\n",
       "<td>1.925062</td>\n",
       "<td>0.2793510</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 20:59:21</td>\n",
       "<td> 1 min 23.307 sec</td>\n",
       "<td>112659 obs/sec</td>\n",
       "<td>13.2530628</td>\n",
       "<td>92</td>\n",
       "<td>9197334.0</td>\n",
       "<td>0.4135673</td>\n",
       "<td>0.5137337</td>\n",
       "<td>0.3158072</td>\n",
       "<td>0.8248199</td>\n",
       "<td>0.8262595</td>\n",
       "<td>1.925062</td>\n",
       "<td>0.2760254</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 20:59:27</td>\n",
       "<td> 1 min 29.027 sec</td>\n",
       "<td>114518 obs/sec</td>\n",
       "<td>14.4066786</td>\n",
       "<td>100</td>\n",
       "<td>9997918.0</td>\n",
       "<td>0.4139151</td>\n",
       "<td>0.5154586</td>\n",
       "<td>0.3146559</td>\n",
       "<td>0.8242329</td>\n",
       "<td>0.8263336</td>\n",
       "<td>1.944908</td>\n",
       "<td>0.2811650</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 20:59:32</td>\n",
       "<td> 1 min 34.109 sec</td>\n",
       "<td>115864 obs/sec</td>\n",
       "<td>15.4161630</td>\n",
       "<td>107</td>\n",
       "<td>10698478.0000000</td>\n",
       "<td>0.4143100</td>\n",
       "<td>0.5141616</td>\n",
       "<td>0.3133476</td>\n",
       "<td>0.8233227</td>\n",
       "<td>0.8246155</td>\n",
       "<td>1.9647540</td>\n",
       "<td>0.2911418</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 20:59:32</td>\n",
       "<td> 1 min 34.172 sec</td>\n",
       "<td>115857 obs/sec</td>\n",
       "<td>15.4161630</td>\n",
       "<td>107</td>\n",
       "<td>10698478.0000000</td>\n",
       "<td>0.4135673</td>\n",
       "<td>0.5137337</td>\n",
       "<td>0.3158072</td>\n",
       "<td>0.8248199</td>\n",
       "<td>0.8262595</td>\n",
       "<td>1.925062</td>\n",
       "<td>0.2760254</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-13.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-13 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-13 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-13 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-13 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-13 .h2o-table th,\n",
       "#h2o-table-13 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-13 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-13\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Variable Importances: </caption>\n",
       "    <thead><tr><th>variable</th>\n",
       "<th>relative_importance</th>\n",
       "<th>scaled_importance</th>\n",
       "<th>percentage</th></tr></thead>\n",
       "    <tbody><tr><td>gamma_GTP</td>\n",
       "<td>1.0</td>\n",
       "<td>1.0</td>\n",
       "<td>0.1134386</td></tr>\n",
       "<tr><td>sex.Female</td>\n",
       "<td>0.9487224</td>\n",
       "<td>0.9487224</td>\n",
       "<td>0.1076217</td></tr>\n",
       "<tr><td>SGOT_ALT</td>\n",
       "<td>0.6106018</td>\n",
       "<td>0.6106018</td>\n",
       "<td>0.0692658</td></tr>\n",
       "<tr><td>sex.Male</td>\n",
       "<td>0.5875087</td>\n",
       "<td>0.5875087</td>\n",
       "<td>0.0666461</td></tr>\n",
       "<tr><td>SGOT_AST</td>\n",
       "<td>0.5737367</td>\n",
       "<td>0.5737367</td>\n",
       "<td>0.0650839</td></tr>\n",
       "<tr><td>HDL_chole</td>\n",
       "<td>0.5560055</td>\n",
       "<td>0.5560055</td>\n",
       "<td>0.0630725</td></tr>\n",
       "<tr><td>SMK_stat_type_cd</td>\n",
       "<td>0.5261788</td>\n",
       "<td>0.5261788</td>\n",
       "<td>0.0596890</td></tr>\n",
       "<tr><td>age</td>\n",
       "<td>0.4101186</td>\n",
       "<td>0.4101186</td>\n",
       "<td>0.0465233</td></tr>\n",
       "<tr><td>triglyceride</td>\n",
       "<td>0.3243141</td>\n",
       "<td>0.3243141</td>\n",
       "<td>0.0367897</td></tr>\n",
       "<tr><td>hear_right</td>\n",
       "<td>0.3114271</td>\n",
       "<td>0.3114271</td>\n",
       "<td>0.0353278</td></tr>\n",
       "<tr><td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td></tr>\n",
       "<tr><td>waistline</td>\n",
       "<td>0.2313083</td>\n",
       "<td>0.2313083</td>\n",
       "<td>0.0262393</td></tr>\n",
       "<tr><td>LDL_chole</td>\n",
       "<td>0.2268184</td>\n",
       "<td>0.2268184</td>\n",
       "<td>0.0257299</td></tr>\n",
       "<tr><td>height</td>\n",
       "<td>0.2250747</td>\n",
       "<td>0.2250747</td>\n",
       "<td>0.0255321</td></tr>\n",
       "<tr><td>BLDS</td>\n",
       "<td>0.2017134</td>\n",
       "<td>0.2017134</td>\n",
       "<td>0.0228821</td></tr>\n",
       "<tr><td>hemoglobin</td>\n",
       "<td>0.1838028</td>\n",
       "<td>0.1838028</td>\n",
       "<td>0.0208503</td></tr>\n",
       "<tr><td>sight_left</td>\n",
       "<td>0.1427522</td>\n",
       "<td>0.1427522</td>\n",
       "<td>0.0161936</td></tr>\n",
       "<tr><td>sight_right</td>\n",
       "<td>0.1425119</td>\n",
       "<td>0.1425119</td>\n",
       "<td>0.0161663</td></tr>\n",
       "<tr><td>DBP</td>\n",
       "<td>0.1368541</td>\n",
       "<td>0.1368541</td>\n",
       "<td>0.0155245</td></tr>\n",
       "<tr><td>SBP</td>\n",
       "<td>0.1241539</td>\n",
       "<td>0.1241539</td>\n",
       "<td>0.0140838</td></tr>\n",
       "<tr><td>sex.missing(NA)</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "<pre style='font-size: smaller; margin-bottom: 1em;'>[25 rows x 4 columns]</pre></div><pre style=\"font-size: smaller; margin: 1em 0 0 0;\">\n",
       "\n",
       "[tips]\n",
       "Use `model.explain()` to inspect the model.\n",
       "--\n",
       "Use `h2o.display.toggle_user_tips()` to switch on/off this section.</pre>"
      ],
      "text/plain": [
       "Model Details\n",
       "=============\n",
       "H2ODeepLearningEstimator : Deep Learning\n",
       "Model Key: DeepLearning_model_python_1699563465891_5\n",
       "\n",
       "\n",
       "Status of Neuron Layers: predicting DRK_YN, 2-class classification, bernoulli distribution, CrossEntropy loss, 25,502 weights/biases, 309.6 KB, 10,698,478 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        25       Input      0.0\n",
       "    2        200      Rectifier  0.0        0.0   0.0   0.050798383578314676  0.19741660356521606   0.0         -0.07026040756153015  0.44421637058258057  -0.5752126015134382  0.4019700288772583\n",
       "    3        100      Rectifier  0.0        0.0   0.0   0.08948327934874468   0.10486823320388794   0.0         -0.0737635919454367   0.15067064762115479  0.09128070619619055  0.2789735794067383\n",
       "    4        2        Softmax               0.0   0.0   0.008133187558214558  0.005064528435468674  0.0         -0.03572129577689338  0.43144309520721436  0.04073638426429656  0.21377378702163696\n",
       "\n",
       "ModelMetricsBinomial: deeplearning\n",
       "** Reported on train data. **\n",
       "\n",
       "MSE: 0.1710379054556747\n",
       "RMSE: 0.41356729253614183\n",
       "LogLoss: 0.5137337313144018\n",
       "Mean Per-Class Error: 0.2770641478773106\n",
       "AUC: 0.82481990656104\n",
       "AUCPR: 0.8262594945163201\n",
       "Gini: 0.64963981312208\n",
       "\n",
       "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.35880479040318636\n",
       "       N     Y     Error    Rate\n",
       "-----  ----  ----  -------  ---------------\n",
       "N      2900  2023  0.4109   (2023.0/4923.0)\n",
       "Y      716   4284  0.1432   (716.0/5000.0)\n",
       "Total  3616  6307  0.276    (2739.0/9923.0)\n",
       "\n",
       "Maximum Metrics: Maximum metrics at their respective thresholds\n",
       "metric                       threshold    value     idx\n",
       "---------------------------  -----------  --------  -----\n",
       "max f1                       0.358805     0.757761  260\n",
       "max f2                       0.123582     0.856478  349\n",
       "max f0point5                 0.604897     0.761224  158\n",
       "max accuracy                 0.504925     0.744936  200\n",
       "max precision                0.998759     1         0\n",
       "max recall                   0.0100392    1         397\n",
       "max specificity              0.998759     1         0\n",
       "max absolute_mcc             0.504925     0.490059  200\n",
       "max min_per_class_accuracy   0.496439     0.744059  204\n",
       "max mean_per_class_accuracy  0.504925     0.745009  200\n",
       "max tns                      0.998759     4923      0\n",
       "max fns                      0.998759     4991      0\n",
       "max fps                      0.00316249   4923      399\n",
       "max tps                      0.0100392    5000      397\n",
       "max tnr                      0.998759     1         0\n",
       "max fnr                      0.998759     0.9982    0\n",
       "max fpr                      0.00316249   1         399\n",
       "max tpr                      0.0100392    1         397\n",
       "\n",
       "Gains/Lift Table: Avg response rate: 50.39 %, avg score: 49.08 %\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        0.0100776                   0.973391           1.92506   1.92506            0.97             0.984277   0.97                        0.984277            0.0194          0.0194                     92.5062   92.5062            0.0187906\n",
       "2        0.0200544                   0.96052            1.92446   1.92476            0.969697         0.966631   0.969849                    0.975498            0.0192          0.0386                     92.4461   92.4763            0.0373812\n",
       "3        0.0300312                   0.949102           1.90441   1.918              0.959596         0.954604   0.966443                    0.968557            0.019           0.0576                     90.4414   91.8003            0.0555687\n",
       "4        0.0400081                   0.940276           1.92446   1.91961            0.969697         0.944926   0.967254                    0.962664            0.0192          0.0768                     92.4461   91.9613            0.0741593\n",
       "5        0.0500857                   0.932122           1.84568   1.90474            0.93             0.936281   0.959759                    0.957356            0.0186          0.0954                     84.5678   90.4737            0.0913374\n",
       "6        0.100071                    0.888491           1.82855   1.86668            0.921371         0.91019    0.940584                    0.933797            0.0914          0.1868                     82.8553   86.6683            0.174815\n",
       "7        0.150055                    0.840171           1.75653   1.82999            0.885081         0.865045   0.922095                    0.910895            0.0878          0.2746                     75.6531   82.999             0.251037\n",
       "8        0.20004                     0.792669           1.6645    1.78864            0.83871          0.816886   0.901259                    0.887404            0.0832          0.3578                     66.4503   78.8639            0.317987\n",
       "9        0.30001                     0.687743           1.49245   1.68994            0.752016         0.74136    0.851528                    0.838739            0.1492          0.507                      49.2451   68.9943            0.417217\n",
       "10       0.39998                     0.587799           1.27439   1.58608            0.642137         0.638371   0.799194                    0.78866             0.1274          0.6344                     27.4385   58.608             0.472507\n",
       "11       0.50005                     0.498344           1.07924   1.48465            0.543807         0.54055    0.748085                    0.739008            0.108           0.7424                     7.92387   48.465             0.48849\n",
       "12       0.60002                     0.390875           0.862261  1.38095            0.434476         0.443204   0.695835                    0.689724            0.0862          0.8286                     -13.7739  38.0954            0.460735\n",
       "13       0.69999                     0.294155           0.72622   1.28745            0.365927         0.343668   0.648719                    0.640302            0.0726          0.9012                     -27.378   28.7447            0.405567\n",
       "14       0.79996                     0.190248           0.522158  1.19181            0.263105         0.241477   0.600529                    0.590461            0.0522          0.9534                     -47.7842  19.181             0.309281\n",
       "15       0.899929                    0.0879891          0.3321    1.09631            0.167339         0.137835   0.552408                    0.54018             0.0332          0.9866                     -66.79    9.63082            0.174697\n",
       "16       1                           6.06215e-08        0.133906  1                  0.0674723        0.0462266  0.50388                     0.49075             0.0134          1                          -86.6094  0                  0\n",
       "\n",
       "Scoring History: \n",
       "    timestamp            duration          training_speed    epochs    iterations    samples      training_rmse    training_logloss    training_r2    training_auc    training_pr_auc    training_lift    training_classification_error\n",
       "--  -------------------  ----------------  ----------------  --------  ------------  -----------  ---------------  ------------------  -------------  --------------  -----------------  ---------------  -------------------------------\n",
       "    2023-11-09 20:57:58  0.000 sec                           0         0             0            nan              nan                 nan            nan             nan                nan              nan\n",
       "    2023-11-09 20:58:00  2.969 sec         43734 obs/sec     0.144189  1             100064       0.421496         0.532197            0.289323       0.813339        0.813409           1.88537          0.294568\n",
       "    2023-11-09 20:58:06  8.135 sec         67860 obs/sec     0.721653  5             500811       0.418314         0.527971            0.30001        0.81714         0.81937            1.92506          0.276126\n",
       "    2023-11-09 20:58:12  14.051 sec        75659 obs/sec     1.44302   10            1.00142e+06  0.416842         0.521877            0.304928       0.819872        0.82298            1.92506          0.270281\n",
       "    2023-11-09 20:58:17  19.525 sec        80677 obs/sec     2.16289   15            1.501e+06    0.416705         0.523895            0.305384       0.819682        0.821231           1.92506          0.294871\n",
       "    2023-11-09 20:58:23  25.449 sec        86128 obs/sec     3.02749   21            2.10101e+06  0.415559         0.519561            0.309201       0.821864        0.823996           1.92506          0.289328\n",
       "    2023-11-09 20:58:28  30.787 sec        87673 obs/sec     3.74785   26            2.60093e+06  0.415264         0.520396            0.310181       0.821954        0.824216           1.90522          0.286909\n",
       "    2023-11-09 20:58:34  36.056 sec        91739 obs/sec     4.61131   32            3.20015e+06  0.415043         0.51755             0.310916       0.822204        0.824295           1.90522          0.276832\n",
       "    2023-11-09 20:58:39  41.292 sec        94860 obs/sec     5.47708   38            3.80097e+06  0.414883         0.51901             0.311448       0.822575        0.824987           1.94491          0.269072\n",
       "    2023-11-09 20:58:44  46.437 sec        97440 obs/sec     6.33947   44            4.39945e+06  0.415405         0.520784            0.309713       0.823244        0.825373           1.90522          0.274615\n",
       "    2023-11-09 20:58:49  51.512 sec        99651 obs/sec     7.20368   50            4.9992e+06   0.413843         0.514596            0.314894       0.824651        0.826614           1.92506          0.282475\n",
       "    2023-11-09 20:58:54  56.781 sec        102900 obs/sec    8.21195   57            5.69892e+06  0.414388         0.515618            0.313089       0.823168        0.82449            1.92506          0.275824\n",
       "    2023-11-09 20:59:00  1 min  2.287 sec  105192 obs/sec    9.22114   64            6.39927e+06  0.415144         0.51989             0.31058        0.822857        0.82319            1.90522          0.27663\n",
       "    2023-11-09 20:59:05  1 min  7.668 sec  107297 obs/sec    10.2288   71            7.0986e+06   0.415001         0.520213            0.311054       0.823203        0.824835           1.92506          0.269576\n",
       "    2023-11-09 20:59:10  1 min 12.896 sec  109315 obs/sec    11.2359   78            7.79749e+06  0.414894         0.516334            0.311411       0.822499        0.824634           1.92506          0.277033\n",
       "    2023-11-09 20:59:16  1 min 18.102 sec  111099 obs/sec    12.2447   85            8.49752e+06  0.414935         0.518027            0.311274       0.822167        0.823619           1.92506          0.279351\n",
       "    2023-11-09 20:59:21  1 min 23.307 sec  112659 obs/sec    13.2531   92            9.19733e+06  0.413567         0.513734            0.315807       0.82482         0.826259           1.92506          0.276025\n",
       "    2023-11-09 20:59:27  1 min 29.027 sec  114518 obs/sec    14.4067   100           9.99792e+06  0.413915         0.515459            0.314656       0.824233        0.826334           1.94491          0.281165\n",
       "    2023-11-09 20:59:32  1 min 34.109 sec  115864 obs/sec    15.4162   107           1.06985e+07  0.41431          0.514162            0.313348       0.823323        0.824615           1.96475          0.291142\n",
       "    2023-11-09 20:59:32  1 min 34.172 sec  115857 obs/sec    15.4162   107           1.06985e+07  0.413567         0.513734            0.315807       0.82482         0.826259           1.92506          0.276025\n",
       "\n",
       "Variable Importances: \n",
       "variable          relative_importance    scaled_importance    percentage\n",
       "----------------  ---------------------  -------------------  --------------------\n",
       "gamma_GTP         1.0                    1.0                  0.11343855970381672\n",
       "sex.Female        0.9487224221229553     0.9487224221229553   0.10762170512434448\n",
       "SGOT_ALT          0.6106018424034119     0.6106018424034119   0.06926579355473993\n",
       "sex.Male          0.5875087380409241     0.5875087380409241   0.06664614505676938\n",
       "SGOT_AST          0.5737367272377014     0.5737367272377014   0.06508386798702641\n",
       "HDL_chole         0.5560055375099182     0.5560055375099182   0.06307246736247157\n",
       "SMK_stat_type_cd  0.526178777217865      0.526178777217865    0.05968896263431006\n",
       "age               0.41011855006217957    0.41011855006217957  0.0465232576268713\n",
       "triglyceride      0.324314147233963      0.324314147233963    0.03678972975379232\n",
       "hear_right        0.3114270567893982     0.3114270567893982   0.03532783677498807\n",
       "---               ---                    ---                  ---\n",
       "waistline         0.23130829632282257    0.23130829632282257  0.02623927998240464\n",
       "LDL_chole         0.22681836783885956    0.22681836783885956  0.025729948962010734\n",
       "height            0.2250746786594391     0.2250746786594391   0.025532147372926146\n",
       "BLDS              0.2017134428024292     0.2017134428024292   0.022882082424405786\n",
       "hemoglobin        0.18380281329154968    0.18380281329154968  0.020850326409302938\n",
       "sight_left        0.14275221526622772    0.14275221526622772  0.016193605694330072\n",
       "sight_right       0.14251190423965454    0.14251190423965454  0.016166345157594665\n",
       "DBP               0.13685405254364014    0.13685405254364014  0.015524526610180993\n",
       "SBP               0.12415388226509094    0.12415388226509094  0.014083837585789152\n",
       "sex.missing(NA)   0.0                    0.0                  0.0\n",
       "[25 rows x 4 columns]\n",
       "\n",
       "\n",
       "[tips]\n",
       "Use `model.explain()` to inspect the model.\n",
       "--\n",
       "Use `h2o.display.toggle_user_tips()` to switch on/off this section."
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize and train the DL estimator:\n",
    "dl_model = H2ODeepLearningEstimator(hidden = [200, 100], epochs = 20.0, stopping_metric = \"auc\")\n",
    "dl_model.train(x = x, y = y, training_frame = train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d38f7fe5-637b-42ec-9c3d-006e8763c9f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.821\n",
      "Gini: 0.64201\n"
     ]
    }
   ],
   "source": [
    "# Eval performance:\n",
    "valid_perf2 = dl_model.model_performance(valid)\n",
    "\n",
    "# Retreive test set AUC and Gini\n",
    "print(\"AUC: \" + str(round(valid_perf2.auc(), 5)))\n",
    "print(\"Gini: \" + str(round(valid_perf2.gini(), 5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a003d27d-7a09-4448-b487-0a5b7f6db795",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning prediction progress: |██████████████████████████████████████████████| (done) 100%\n",
      "Prediction      N       Y\n",
      "Original                 \n",
      "N           87440   61318\n",
      "Y           21423  127187\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Prediction', ylabel='Original'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = 'DRK_YN'\n",
    "y_df = valid[y].as_data_frame()\n",
    "y_orig = y_df[y]\n",
    "\n",
    "valid_pred = dl_model.predict(valid).as_data_frame()\n",
    "y_pred = valid_pred['predict']\n",
    "\n",
    "ct_target = pd.crosstab(y_orig, y_pred, rownames = [\"Original\"], colnames = [\"Prediction\"])\n",
    "print(ct_target)\n",
    "ct_normalize = pd.crosstab(y_orig, y_pred, rownames = [\"Original\"], colnames = [\"Prediction\"], normalize = True)\n",
    "sns.heatmap(ct_normalize, cmap='coolwarm', annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "14096ae5-ba12-4983-926e-19703a8f8786",
   "metadata": {},
   "outputs": [],
   "source": [
    "from h2o.estimators import H2ODecisionTreeEstimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "aa5c526a-0a89-4a02-a46e-10b04bb51e83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dt Model Build progress: |███████████████████████████████████████████████████████| (done) 100%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style='margin: 1em 0 1em 0;'>Model Details\n",
       "=============\n",
       "H2ODecisionTreeEstimator : Decision Tree\n",
       "Model Key: DT_model_python_1699563465891_26\n",
       "\n",
       "No summary for this model</pre><pre style=\"font-size: smaller; margin: 1em 0 0 0;\">\n",
       "\n",
       "[tips]\n",
       "Use `model.explain()` to inspect the model.\n",
       "--\n",
       "Use `h2o.display.toggle_user_tips()` to switch on/off this section.</pre>"
      ],
      "text/plain": [
       "Model Details\n",
       "=============\n",
       "H2ODecisionTreeEstimator : Decision Tree\n",
       "Model Key: DT_model_python_1699563465891_26\n",
       "\n",
       "No summary for this model\n",
       "\n",
       "[tips]\n",
       "Use `model.explain()` to inspect the model.\n",
       "--\n",
       "Use `h2o.display.toggle_user_tips()` to switch on/off this section."
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_tree = H2ODecisionTreeEstimator(max_depth = 7)\n",
    "model_tree.train(x = x, y = y, training_frame = train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d6e07a7d-24de-4db9-ac76-c77346dcfdf9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.6844\n",
      "Gini: 0.36879\n"
     ]
    }
   ],
   "source": [
    "# Eval performance:\n",
    "valid_perf3 = model_tree.model_performance(valid)\n",
    "\n",
    "# Retreive test set AUC and Gini\n",
    "print(\"AUC: \" + str(round(valid_perf3.auc(), 5)))\n",
    "print(\"Gini: \" + str(round(valid_perf3.gini(), 5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b918799b-a7f1-406a-8882-e99f2e718286",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dt prediction progress: |████████████████████████████████████████████████████████| (done) 100%\n",
      "Prediction      N       Y\n",
      "Original                 \n",
      "N           97231   51527\n",
      "Y           42328  106282\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Prediction', ylabel='Original'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = 'DRK_YN'\n",
    "y_df = valid[y].as_data_frame()\n",
    "y_orig = y_df[y]\n",
    "\n",
    "valid_pred = model_tree.predict(valid).as_data_frame()\n",
    "y_pred = valid_pred['predict']\n",
    "\n",
    "ct_target = pd.crosstab(y_orig, y_pred, rownames = [\"Original\"], colnames = [\"Prediction\"])\n",
    "print(ct_target)\n",
    "ct_normalize = pd.crosstab(y_orig, y_pred, rownames = [\"Original\"], colnames = [\"Prediction\"], normalize = True)\n",
    "sns.heatmap(ct_normalize, cmap='coolwarm', annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "38c75171-76bc-4970-b3dc-535dba9ceeca",
   "metadata": {},
   "outputs": [],
   "source": [
    "from h2o.estimators import H2OGradientBoostingEstimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5fa7bc36-4bf5-4708-87d8-17c365702d66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm Model Build progress: |"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/bitnami/python/lib/python3.11/site-packages/h2o/estimators/estimator_base.py:192: RuntimeWarning: early stopping is enabled but neither score_tree_interval or score_each_iteration are defined. Early stopping will not be reproducible!\n",
      "  warnings.warn(mesg[\"message\"], RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "██████████████████████████████████████████████████████| (done) 100%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style='margin: 1em 0 1em 0;'>Model Details\n",
       "=============\n",
       "H2OGradientBoostingEstimator : Gradient Boosting Machine\n",
       "Model Key: GBM_model_python_1699563465891_28\n",
       "</pre>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-14.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-14 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-14 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-14 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-14 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-14 .h2o-table th,\n",
       "#h2o-table-14 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-14 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-14\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Model Summary: </caption>\n",
       "    <thead><tr><th></th>\n",
       "<th>number_of_trees</th>\n",
       "<th>number_of_internal_trees</th>\n",
       "<th>model_size_in_bytes</th>\n",
       "<th>min_depth</th>\n",
       "<th>max_depth</th>\n",
       "<th>mean_depth</th>\n",
       "<th>min_leaves</th>\n",
       "<th>max_leaves</th>\n",
       "<th>mean_leaves</th></tr></thead>\n",
       "    <tbody><tr><td></td>\n",
       "<td>100.0</td>\n",
       "<td>100.0</td>\n",
       "<td>154275.0</td>\n",
       "<td>7.0</td>\n",
       "<td>7.0</td>\n",
       "<td>7.0</td>\n",
       "<td>81.0</td>\n",
       "<td>128.0</td>\n",
       "<td>118.27</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div>\n",
       "<div style='margin: 1em 0 1em 0;'><pre style='margin: 1em 0 1em 0;'>ModelMetricsBinomial: gbm\n",
       "** Reported on train data. **\n",
       "\n",
       "MSE: 0.16863170701986008\n",
       "RMSE: 0.41064791125714994\n",
       "LogLoss: 0.5057681196160003\n",
       "Mean Per-Class Error: 0.2688883006064671\n",
       "AUC: 0.8295815337792323\n",
       "AUCPR: 0.831041283791346\n",
       "Gini: 0.6591630675584645</pre>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-15.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-15 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-15 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-15 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-15 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-15 .h2o-table th,\n",
       "#h2o-table-15 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-15 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-15\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.38537513318477773</caption>\n",
       "    <thead><tr><th></th>\n",
       "<th>N</th>\n",
       "<th>Y</th>\n",
       "<th>Error</th>\n",
       "<th>Rate</th></tr></thead>\n",
       "    <tbody><tr><td>N</td>\n",
       "<td>210874.0</td>\n",
       "<td>136226.0</td>\n",
       "<td>0.3925</td>\n",
       "<td> (136226.0/347100.0)</td></tr>\n",
       "<tr><td>Y</td>\n",
       "<td>50404.0</td>\n",
       "<td>296474.0</td>\n",
       "<td>0.1453</td>\n",
       "<td> (50404.0/346878.0)</td></tr>\n",
       "<tr><td>Total</td>\n",
       "<td>261278.0</td>\n",
       "<td>432700.0</td>\n",
       "<td>0.2689</td>\n",
       "<td> (186630.0/693978.0)</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-16.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-16 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-16 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-16 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-16 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-16 .h2o-table th,\n",
       "#h2o-table-16 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-16 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-16\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Maximum Metrics: Maximum metrics at their respective thresholds</caption>\n",
       "    <thead><tr><th>metric</th>\n",
       "<th>threshold</th>\n",
       "<th>value</th>\n",
       "<th>idx</th></tr></thead>\n",
       "    <tbody><tr><td>max f1</td>\n",
       "<td>0.3853751</td>\n",
       "<td>0.7606012</td>\n",
       "<td>250.0</td></tr>\n",
       "<tr><td>max f2</td>\n",
       "<td>0.1709553</td>\n",
       "<td>0.8561181</td>\n",
       "<td>338.0</td></tr>\n",
       "<tr><td>max f0point5</td>\n",
       "<td>0.6129556</td>\n",
       "<td>0.7583469</td>\n",
       "<td>155.0</td></tr>\n",
       "<tr><td>max accuracy</td>\n",
       "<td>0.4973158</td>\n",
       "<td>0.7460078</td>\n",
       "<td>203.0</td></tr>\n",
       "<tr><td>max precision</td>\n",
       "<td>0.9809075</td>\n",
       "<td>0.9907236</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max recall</td>\n",
       "<td>0.0211488</td>\n",
       "<td>1.0</td>\n",
       "<td>399.0</td></tr>\n",
       "<tr><td>max specificity</td>\n",
       "<td>0.9809075</td>\n",
       "<td>0.9999856</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max absolute_mcc</td>\n",
       "<td>0.4973158</td>\n",
       "<td>0.4921161</td>\n",
       "<td>203.0</td></tr>\n",
       "<tr><td>max min_per_class_accuracy</td>\n",
       "<td>0.5074089</td>\n",
       "<td>0.7454927</td>\n",
       "<td>199.0</td></tr>\n",
       "<tr><td>max mean_per_class_accuracy</td>\n",
       "<td>0.4973158</td>\n",
       "<td>0.7460110</td>\n",
       "<td>203.0</td></tr>\n",
       "<tr><td>max tns</td>\n",
       "<td>0.9809075</td>\n",
       "<td>347095.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max fns</td>\n",
       "<td>0.9809075</td>\n",
       "<td>346344.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max fps</td>\n",
       "<td>0.0211488</td>\n",
       "<td>347100.0</td>\n",
       "<td>399.0</td></tr>\n",
       "<tr><td>max tps</td>\n",
       "<td>0.0211488</td>\n",
       "<td>346878.0</td>\n",
       "<td>399.0</td></tr>\n",
       "<tr><td>max tnr</td>\n",
       "<td>0.9809075</td>\n",
       "<td>0.9999856</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max fnr</td>\n",
       "<td>0.9809075</td>\n",
       "<td>0.9984606</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max fpr</td>\n",
       "<td>0.0211488</td>\n",
       "<td>1.0</td>\n",
       "<td>399.0</td></tr>\n",
       "<tr><td>max tpr</td>\n",
       "<td>0.0211488</td>\n",
       "<td>1.0</td>\n",
       "<td>399.0</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-17.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-17 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-17 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-17 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-17 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-17 .h2o-table th,\n",
       "#h2o-table-17 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-17 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-17\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Gains/Lift Table: Avg response rate: 49.98 %, avg score: 49.98 %</caption>\n",
       "    <thead><tr><th>group</th>\n",
       "<th>cumulative_data_fraction</th>\n",
       "<th>lower_threshold</th>\n",
       "<th>lift</th>\n",
       "<th>cumulative_lift</th>\n",
       "<th>response_rate</th>\n",
       "<th>score</th>\n",
       "<th>cumulative_response_rate</th>\n",
       "<th>cumulative_score</th>\n",
       "<th>capture_rate</th>\n",
       "<th>cumulative_capture_rate</th>\n",
       "<th>gain</th>\n",
       "<th>cumulative_gain</th>\n",
       "<th>kolmogorov_smirnov</th></tr></thead>\n",
       "    <tbody><tr><td>1</td>\n",
       "<td>0.0100003</td>\n",
       "<td>0.9637321</td>\n",
       "<td>1.9795958</td>\n",
       "<td>1.9795958</td>\n",
       "<td>0.9894813</td>\n",
       "<td>0.9709305</td>\n",
       "<td>0.9894813</td>\n",
       "<td>0.9709305</td>\n",
       "<td>0.0197966</td>\n",
       "<td>0.0197966</td>\n",
       "<td>97.9595799</td>\n",
       "<td>97.9595799</td>\n",
       "<td>0.0195863</td></tr>\n",
       "<tr><td>2</td>\n",
       "<td>0.0200006</td>\n",
       "<td>0.9538171</td>\n",
       "<td>1.9712358</td>\n",
       "<td>1.9754158</td>\n",
       "<td>0.9853026</td>\n",
       "<td>0.9586324</td>\n",
       "<td>0.9873919</td>\n",
       "<td>0.9647815</td>\n",
       "<td>0.0197130</td>\n",
       "<td>0.0395096</td>\n",
       "<td>97.1235776</td>\n",
       "<td>97.5415787</td>\n",
       "<td>0.0390054</td></tr>\n",
       "<tr><td>3</td>\n",
       "<td>0.0300010</td>\n",
       "<td>0.9448179</td>\n",
       "<td>1.9516330</td>\n",
       "<td>1.9674882</td>\n",
       "<td>0.9755043</td>\n",
       "<td>0.9492216</td>\n",
       "<td>0.9834294</td>\n",
       "<td>0.9595948</td>\n",
       "<td>0.0195169</td>\n",
       "<td>0.0590265</td>\n",
       "<td>95.1632963</td>\n",
       "<td>96.7488179</td>\n",
       "<td>0.0580326</td></tr>\n",
       "<tr><td>4</td>\n",
       "<td>0.0400013</td>\n",
       "<td>0.9362295</td>\n",
       "<td>1.9354895</td>\n",
       "<td>1.9594885</td>\n",
       "<td>0.9674352</td>\n",
       "<td>0.9405269</td>\n",
       "<td>0.9794308</td>\n",
       "<td>0.9548279</td>\n",
       "<td>0.0193555</td>\n",
       "<td>0.0783820</td>\n",
       "<td>93.5489470</td>\n",
       "<td>95.9488502</td>\n",
       "<td>0.0767370</td></tr>\n",
       "<tr><td>5</td>\n",
       "<td>0.0500001</td>\n",
       "<td>0.9275173</td>\n",
       "<td>1.9262539</td>\n",
       "<td>1.9528423</td>\n",
       "<td>0.9628188</td>\n",
       "<td>0.9319205</td>\n",
       "<td>0.9761088</td>\n",
       "<td>0.9502469</td>\n",
       "<td>0.0192604</td>\n",
       "<td>0.0976424</td>\n",
       "<td>92.6253899</td>\n",
       "<td>95.2842347</td>\n",
       "<td>0.0952540</td></tr>\n",
       "<tr><td>6</td>\n",
       "<td>0.1000003</td>\n",
       "<td>0.8819608</td>\n",
       "<td>1.8641082</td>\n",
       "<td>1.9084753</td>\n",
       "<td>0.9317560</td>\n",
       "<td>0.9050117</td>\n",
       "<td>0.9539324</td>\n",
       "<td>0.9276293</td>\n",
       "<td>0.0932057</td>\n",
       "<td>0.1908481</td>\n",
       "<td>86.4108235</td>\n",
       "<td>90.8475291</td>\n",
       "<td>0.1816375</td></tr>\n",
       "<tr><td>7</td>\n",
       "<td>0.1500004</td>\n",
       "<td>0.8339160</td>\n",
       "<td>1.7506969</td>\n",
       "<td>1.8558825</td>\n",
       "<td>0.8750684</td>\n",
       "<td>0.8580447</td>\n",
       "<td>0.9276444</td>\n",
       "<td>0.9044345</td>\n",
       "<td>0.0875351</td>\n",
       "<td>0.2783832</td>\n",
       "<td>75.0696931</td>\n",
       "<td>85.5882504</td>\n",
       "<td>0.2566834</td></tr>\n",
       "<tr><td>8</td>\n",
       "<td>0.2000006</td>\n",
       "<td>0.7847597</td>\n",
       "<td>1.6460495</td>\n",
       "<td>1.8034243</td>\n",
       "<td>0.8227615</td>\n",
       "<td>0.8093875</td>\n",
       "<td>0.9014237</td>\n",
       "<td>0.8806727</td>\n",
       "<td>0.0823027</td>\n",
       "<td>0.3606859</td>\n",
       "<td>64.6049489</td>\n",
       "<td>80.3424250</td>\n",
       "<td>0.3212679</td></tr>\n",
       "<tr><td>9</td>\n",
       "<td>0.3000009</td>\n",
       "<td>0.6898220</td>\n",
       "<td>1.4765956</td>\n",
       "<td>1.6944814</td>\n",
       "<td>0.7380616</td>\n",
       "<td>0.7369201</td>\n",
       "<td>0.8469697</td>\n",
       "<td>0.8327552</td>\n",
       "<td>0.1476600</td>\n",
       "<td>0.5083459</td>\n",
       "<td>47.6595587</td>\n",
       "<td>69.4481362</td>\n",
       "<td>0.4165568</td></tr>\n",
       "<tr><td>10</td>\n",
       "<td>0.3999997</td>\n",
       "<td>0.5988198</td>\n",
       "<td>1.2846740</td>\n",
       "<td>1.5920306</td>\n",
       "<td>0.6421315</td>\n",
       "<td>0.6442523</td>\n",
       "<td>0.7957607</td>\n",
       "<td>0.7856300</td>\n",
       "<td>0.1284659</td>\n",
       "<td>0.6368118</td>\n",
       "<td>28.4673969</td>\n",
       "<td>59.2030621</td>\n",
       "<td>0.4734727</td></tr>\n",
       "<tr><td>11</td>\n",
       "<td>0.5</td>\n",
       "<td>0.5053366</td>\n",
       "<td>1.0918505</td>\n",
       "<td>1.4919943</td>\n",
       "<td>0.5457506</td>\n",
       "<td>0.5523159</td>\n",
       "<td>0.7457585</td>\n",
       "<td>0.7389670</td>\n",
       "<td>0.1091854</td>\n",
       "<td>0.7459972</td>\n",
       "<td>9.1850473</td>\n",
       "<td>49.1994303</td>\n",
       "<td>0.4918370</td></tr>\n",
       "<tr><td>12</td>\n",
       "<td>0.6000003</td>\n",
       "<td>0.4076426</td>\n",
       "<td>0.9013518</td>\n",
       "<td>1.3935536</td>\n",
       "<td>0.4505317</td>\n",
       "<td>0.4570319</td>\n",
       "<td>0.6965539</td>\n",
       "<td>0.6919777</td>\n",
       "<td>0.0901354</td>\n",
       "<td>0.8361326</td>\n",
       "<td>-9.8648231</td>\n",
       "<td>39.3553645</td>\n",
       "<td>0.4721136</td></tr>\n",
       "<tr><td>13</td>\n",
       "<td>0.6999991</td>\n",
       "<td>0.3085109</td>\n",
       "<td>0.7073750</td>\n",
       "<td>1.2955291</td>\n",
       "<td>0.3535744</td>\n",
       "<td>0.3578145</td>\n",
       "<td>0.6475574</td>\n",
       "<td>0.6442406</td>\n",
       "<td>0.0707367</td>\n",
       "<td>0.9068693</td>\n",
       "<td>-29.2624990</td>\n",
       "<td>29.5529134</td>\n",
       "<td>0.4136080</td></tr>\n",
       "<tr><td>14</td>\n",
       "<td>0.7999994</td>\n",
       "<td>0.2107020</td>\n",
       "<td>0.5087653</td>\n",
       "<td>1.1971833</td>\n",
       "<td>0.2543013</td>\n",
       "<td>0.2598864</td>\n",
       "<td>0.5984002</td>\n",
       "<td>0.5961962</td>\n",
       "<td>0.0508767</td>\n",
       "<td>0.9577460</td>\n",
       "<td>-49.1234695</td>\n",
       "<td>19.7183301</td>\n",
       "<td>0.3153922</td></tr>\n",
       "<tr><td>15</td>\n",
       "<td>0.8999997</td>\n",
       "<td>0.1119585</td>\n",
       "<td>0.3086379</td>\n",
       "<td>1.0984557</td>\n",
       "<td>0.1542696</td>\n",
       "<td>0.1604095</td>\n",
       "<td>0.5490522</td>\n",
       "<td>0.5477753</td>\n",
       "<td>0.0308639</td>\n",
       "<td>0.9886098</td>\n",
       "<td>-69.1362117</td>\n",
       "<td>9.8455716</td>\n",
       "<td>0.1771636</td></tr>\n",
       "<tr><td>16</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0060164</td>\n",
       "<td>0.1139014</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0569325</td>\n",
       "<td>0.0683813</td>\n",
       "<td>0.4998401</td>\n",
       "<td>0.4998357</td>\n",
       "<td>0.0113902</td>\n",
       "<td>1.0</td>\n",
       "<td>-88.6098611</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div></div>\n",
       "<div style='margin: 1em 0 1em 0;'><pre style='margin: 1em 0 1em 0;'>ModelMetricsBinomial: gbm\n",
       "** Reported on cross-validation data. **\n",
       "\n",
       "MSE: 0.1724333366790474\n",
       "RMSE: 0.4152509321832371\n",
       "LogLoss: 0.5152475686346667\n",
       "Mean Per-Class Error: 0.27717523901296864\n",
       "AUC: 0.8216203468926443\n",
       "AUCPR: 0.8222467083702414\n",
       "Gini: 0.6432406937852886</pre>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-18.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-18 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-18 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-18 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-18 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-18 .h2o-table th,\n",
       "#h2o-table-18 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-18 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-18\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.37602342252866316</caption>\n",
       "    <thead><tr><th></th>\n",
       "<th>N</th>\n",
       "<th>Y</th>\n",
       "<th>Error</th>\n",
       "<th>Rate</th></tr></thead>\n",
       "    <tbody><tr><td>N</td>\n",
       "<td>204797.0</td>\n",
       "<td>142303.0</td>\n",
       "<td>0.41</td>\n",
       "<td> (142303.0/347100.0)</td></tr>\n",
       "<tr><td>Y</td>\n",
       "<td>50080.0</td>\n",
       "<td>296798.0</td>\n",
       "<td>0.1444</td>\n",
       "<td> (50080.0/346878.0)</td></tr>\n",
       "<tr><td>Total</td>\n",
       "<td>254877.0</td>\n",
       "<td>439101.0</td>\n",
       "<td>0.2772</td>\n",
       "<td> (192383.0/693978.0)</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-19.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-19 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-19 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-19 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-19 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-19 .h2o-table th,\n",
       "#h2o-table-19 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-19 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-19\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Maximum Metrics: Maximum metrics at their respective thresholds</caption>\n",
       "    <thead><tr><th>metric</th>\n",
       "<th>threshold</th>\n",
       "<th>value</th>\n",
       "<th>idx</th></tr></thead>\n",
       "    <tbody><tr><td>max f1</td>\n",
       "<td>0.3760234</td>\n",
       "<td>0.7552314</td>\n",
       "<td>252.0</td></tr>\n",
       "<tr><td>max f2</td>\n",
       "<td>0.1711539</td>\n",
       "<td>0.8543081</td>\n",
       "<td>336.0</td></tr>\n",
       "<tr><td>max f0point5</td>\n",
       "<td>0.6066868</td>\n",
       "<td>0.7497254</td>\n",
       "<td>157.0</td></tr>\n",
       "<tr><td>max accuracy</td>\n",
       "<td>0.5019304</td>\n",
       "<td>0.7393462</td>\n",
       "<td>200.0</td></tr>\n",
       "<tr><td>max precision</td>\n",
       "<td>0.9702300</td>\n",
       "<td>0.9881313</td>\n",
       "<td>3.0</td></tr>\n",
       "<tr><td>max recall</td>\n",
       "<td>0.0202079</td>\n",
       "<td>1.0</td>\n",
       "<td>399.0</td></tr>\n",
       "<tr><td>max specificity</td>\n",
       "<td>0.9802545</td>\n",
       "<td>0.9999712</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max absolute_mcc</td>\n",
       "<td>0.5019304</td>\n",
       "<td>0.4787174</td>\n",
       "<td>200.0</td></tr>\n",
       "<tr><td>max min_per_class_accuracy</td>\n",
       "<td>0.5080904</td>\n",
       "<td>0.7377551</td>\n",
       "<td>198.0</td></tr>\n",
       "<tr><td>max mean_per_class_accuracy</td>\n",
       "<td>0.5019304</td>\n",
       "<td>0.7393478</td>\n",
       "<td>200.0</td></tr>\n",
       "<tr><td>max tns</td>\n",
       "<td>0.9802545</td>\n",
       "<td>347090.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max fns</td>\n",
       "<td>0.9802545</td>\n",
       "<td>346296.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max fps</td>\n",
       "<td>0.0202079</td>\n",
       "<td>347100.0</td>\n",
       "<td>399.0</td></tr>\n",
       "<tr><td>max tps</td>\n",
       "<td>0.0202079</td>\n",
       "<td>346878.0</td>\n",
       "<td>399.0</td></tr>\n",
       "<tr><td>max tnr</td>\n",
       "<td>0.9802545</td>\n",
       "<td>0.9999712</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max fnr</td>\n",
       "<td>0.9802545</td>\n",
       "<td>0.9983222</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max fpr</td>\n",
       "<td>0.0202079</td>\n",
       "<td>1.0</td>\n",
       "<td>399.0</td></tr>\n",
       "<tr><td>max tpr</td>\n",
       "<td>0.0202079</td>\n",
       "<td>1.0</td>\n",
       "<td>399.0</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-20.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-20 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-20 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-20 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-20 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-20 .h2o-table th,\n",
       "#h2o-table-20 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-20 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-20\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Gains/Lift Table: Avg response rate: 49.98 %, avg score: 49.99 %</caption>\n",
       "    <thead><tr><th>group</th>\n",
       "<th>cumulative_data_fraction</th>\n",
       "<th>lower_threshold</th>\n",
       "<th>lift</th>\n",
       "<th>cumulative_lift</th>\n",
       "<th>response_rate</th>\n",
       "<th>score</th>\n",
       "<th>cumulative_response_rate</th>\n",
       "<th>cumulative_score</th>\n",
       "<th>capture_rate</th>\n",
       "<th>cumulative_capture_rate</th>\n",
       "<th>gain</th>\n",
       "<th>cumulative_gain</th>\n",
       "<th>kolmogorov_smirnov</th></tr></thead>\n",
       "    <tbody><tr><td>1</td>\n",
       "<td>0.0100003</td>\n",
       "<td>0.9632525</td>\n",
       "<td>1.9741185</td>\n",
       "<td>1.9741185</td>\n",
       "<td>0.9867435</td>\n",
       "<td>0.9704765</td>\n",
       "<td>0.9867435</td>\n",
       "<td>0.9704765</td>\n",
       "<td>0.0197418</td>\n",
       "<td>0.0197418</td>\n",
       "<td>97.4118542</td>\n",
       "<td>97.4118542</td>\n",
       "<td>0.0194768</td></tr>\n",
       "<tr><td>2</td>\n",
       "<td>0.0200006</td>\n",
       "<td>0.9533444</td>\n",
       "<td>1.9614344</td>\n",
       "<td>1.9677765</td>\n",
       "<td>0.9804035</td>\n",
       "<td>0.9582060</td>\n",
       "<td>0.9835735</td>\n",
       "<td>0.9643412</td>\n",
       "<td>0.0196150</td>\n",
       "<td>0.0393568</td>\n",
       "<td>96.1434369</td>\n",
       "<td>96.7776456</td>\n",
       "<td>0.0386999</td></tr>\n",
       "<tr><td>3</td>\n",
       "<td>0.0300010</td>\n",
       "<td>0.9444824</td>\n",
       "<td>1.9458674</td>\n",
       "<td>1.9604734</td>\n",
       "<td>0.9726225</td>\n",
       "<td>0.9488874</td>\n",
       "<td>0.9799232</td>\n",
       "<td>0.9591900</td>\n",
       "<td>0.0194593</td>\n",
       "<td>0.0588161</td>\n",
       "<td>94.5867430</td>\n",
       "<td>96.0473447</td>\n",
       "<td>0.0576118</td></tr>\n",
       "<tr><td>4</td>\n",
       "<td>0.0400013</td>\n",
       "<td>0.9357816</td>\n",
       "<td>1.9204991</td>\n",
       "<td>1.9504799</td>\n",
       "<td>0.9599424</td>\n",
       "<td>0.9401572</td>\n",
       "<td>0.9749280</td>\n",
       "<td>0.9544318</td>\n",
       "<td>0.0192056</td>\n",
       "<td>0.0780217</td>\n",
       "<td>92.0499084</td>\n",
       "<td>95.0479856</td>\n",
       "<td>0.0760165</td></tr>\n",
       "<tr><td>5</td>\n",
       "<td>0.0500001</td>\n",
       "<td>0.9271569</td>\n",
       "<td>1.9063599</td>\n",
       "<td>1.9416569</td>\n",
       "<td>0.9528751</td>\n",
       "<td>0.9314556</td>\n",
       "<td>0.9705179</td>\n",
       "<td>0.9498371</td>\n",
       "<td>0.0190615</td>\n",
       "<td>0.0970831</td>\n",
       "<td>90.6359943</td>\n",
       "<td>94.1656891</td>\n",
       "<td>0.0941358</td></tr>\n",
       "<tr><td>6</td>\n",
       "<td>0.1000003</td>\n",
       "<td>0.8820862</td>\n",
       "<td>1.8468111</td>\n",
       "<td>1.8942340</td>\n",
       "<td>0.9231102</td>\n",
       "<td>0.9049606</td>\n",
       "<td>0.9468140</td>\n",
       "<td>0.9273988</td>\n",
       "<td>0.0923408</td>\n",
       "<td>0.1894239</td>\n",
       "<td>84.6811138</td>\n",
       "<td>89.4234014</td>\n",
       "<td>0.1787901</td></tr>\n",
       "<tr><td>7</td>\n",
       "<td>0.1500004</td>\n",
       "<td>0.8342515</td>\n",
       "<td>1.7337458</td>\n",
       "<td>1.8407379</td>\n",
       "<td>0.8665956</td>\n",
       "<td>0.8584115</td>\n",
       "<td>0.9200745</td>\n",
       "<td>0.9044031</td>\n",
       "<td>0.0866875</td>\n",
       "<td>0.2761115</td>\n",
       "<td>73.3745775</td>\n",
       "<td>84.0737934</td>\n",
       "<td>0.2521414</td></tr>\n",
       "<tr><td>8</td>\n",
       "<td>0.2000006</td>\n",
       "<td>0.7856837</td>\n",
       "<td>1.6154913</td>\n",
       "<td>1.7844263</td>\n",
       "<td>0.8074872</td>\n",
       "<td>0.8099625</td>\n",
       "<td>0.8919277</td>\n",
       "<td>0.8807929</td>\n",
       "<td>0.0807748</td>\n",
       "<td>0.3568863</td>\n",
       "<td>61.5491282</td>\n",
       "<td>78.4426271</td>\n",
       "<td>0.3136711</td></tr>\n",
       "<tr><td>9</td>\n",
       "<td>0.3000009</td>\n",
       "<td>0.6904783</td>\n",
       "<td>1.4555796</td>\n",
       "<td>1.6748107</td>\n",
       "<td>0.7275570</td>\n",
       "<td>0.7378039</td>\n",
       "<td>0.8371375</td>\n",
       "<td>0.8331299</td>\n",
       "<td>0.1455584</td>\n",
       "<td>0.5024447</td>\n",
       "<td>45.5579613</td>\n",
       "<td>67.4810718</td>\n",
       "<td>0.4047581</td></tr>\n",
       "<tr><td>10</td>\n",
       "<td>0.3999997</td>\n",
       "<td>0.5985438</td>\n",
       "<td>1.2740361</td>\n",
       "<td>1.5746181</td>\n",
       "<td>0.6368143</td>\n",
       "<td>0.6445497</td>\n",
       "<td>0.7870572</td>\n",
       "<td>0.7859854</td>\n",
       "<td>0.1274021</td>\n",
       "<td>0.6298468</td>\n",
       "<td>27.4036101</td>\n",
       "<td>57.4618147</td>\n",
       "<td>0.4595472</td></tr>\n",
       "<tr><td>11</td>\n",
       "<td>0.5</td>\n",
       "<td>0.5050979</td>\n",
       "<td>1.0949640</td>\n",
       "<td>1.4786870</td>\n",
       "<td>0.5473068</td>\n",
       "<td>0.5521262</td>\n",
       "<td>0.7391070</td>\n",
       "<td>0.7392134</td>\n",
       "<td>0.1094967</td>\n",
       "<td>0.7393435</td>\n",
       "<td>9.4963951</td>\n",
       "<td>47.8687031</td>\n",
       "<td>0.4785340</td></tr>\n",
       "<tr><td>12</td>\n",
       "<td>0.6000003</td>\n",
       "<td>0.4070953</td>\n",
       "<td>0.9050130</td>\n",
       "<td>1.3830745</td>\n",
       "<td>0.4523617</td>\n",
       "<td>0.4564874</td>\n",
       "<td>0.6913160</td>\n",
       "<td>0.6920923</td>\n",
       "<td>0.0905016</td>\n",
       "<td>0.8298451</td>\n",
       "<td>-9.4987012</td>\n",
       "<td>38.3074461</td>\n",
       "<td>0.4595426</td></tr>\n",
       "<tr><td>13</td>\n",
       "<td>0.6999991</td>\n",
       "<td>0.3089684</td>\n",
       "<td>0.7222219</td>\n",
       "<td>1.2886679</td>\n",
       "<td>0.3609954</td>\n",
       "<td>0.3578106</td>\n",
       "<td>0.6441278</td>\n",
       "<td>0.6443383</td>\n",
       "<td>0.0722214</td>\n",
       "<td>0.9020664</td>\n",
       "<td>-27.7778101</td>\n",
       "<td>28.8667924</td>\n",
       "<td>0.4040054</td></tr>\n",
       "<tr><td>14</td>\n",
       "<td>0.7999994</td>\n",
       "<td>0.2109384</td>\n",
       "<td>0.5292912</td>\n",
       "<td>1.1937455</td>\n",
       "<td>0.2645609</td>\n",
       "<td>0.2602423</td>\n",
       "<td>0.5966818</td>\n",
       "<td>0.5963261</td>\n",
       "<td>0.0529293</td>\n",
       "<td>0.9549957</td>\n",
       "<td>-47.0708806</td>\n",
       "<td>19.3745491</td>\n",
       "<td>0.3098934</td></tr>\n",
       "<tr><td>15</td>\n",
       "<td>0.8999997</td>\n",
       "<td>0.1113428</td>\n",
       "<td>0.3265116</td>\n",
       "<td>1.0973859</td>\n",
       "<td>0.1632036</td>\n",
       "<td>0.1606258</td>\n",
       "<td>0.5485174</td>\n",
       "<td>0.5479148</td>\n",
       "<td>0.0326512</td>\n",
       "<td>0.9876470</td>\n",
       "<td>-67.3488450</td>\n",
       "<td>9.7385855</td>\n",
       "<td>0.1752384</td></tr>\n",
       "<tr><td>16</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0078961</td>\n",
       "<td>0.1235301</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0617453</td>\n",
       "<td>0.0681147</td>\n",
       "<td>0.4998401</td>\n",
       "<td>0.4999346</td>\n",
       "<td>0.0123530</td>\n",
       "<td>1.0</td>\n",
       "<td>-87.6469893</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div></div>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-21.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-21 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-21 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-21 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-21 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-21 .h2o-table th,\n",
       "#h2o-table-21 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-21 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-21\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Cross-Validation Metrics Summary: </caption>\n",
       "    <thead><tr><th></th>\n",
       "<th>mean</th>\n",
       "<th>sd</th>\n",
       "<th>cv_1_valid</th>\n",
       "<th>cv_2_valid</th>\n",
       "<th>cv_3_valid</th>\n",
       "<th>cv_4_valid</th>\n",
       "<th>cv_5_valid</th></tr></thead>\n",
       "    <tbody><tr><td>accuracy</td>\n",
       "<td>0.7232119</td>\n",
       "<td>0.0021753</td>\n",
       "<td>0.7236291</td>\n",
       "<td>0.7254841</td>\n",
       "<td>0.7250289</td>\n",
       "<td>0.7206981</td>\n",
       "<td>0.7212195</td></tr>\n",
       "<tr><td>auc</td>\n",
       "<td>0.8216243</td>\n",
       "<td>0.0012262</td>\n",
       "<td>0.8208833</td>\n",
       "<td>0.8225610</td>\n",
       "<td>0.8231792</td>\n",
       "<td>0.8213137</td>\n",
       "<td>0.8201843</td></tr>\n",
       "<tr><td>err</td>\n",
       "<td>0.2767880</td>\n",
       "<td>0.0021753</td>\n",
       "<td>0.2763709</td>\n",
       "<td>0.2745159</td>\n",
       "<td>0.2749710</td>\n",
       "<td>0.2793019</td>\n",
       "<td>0.2787805</td></tr>\n",
       "<tr><td>err_count</td>\n",
       "<td>38416.6</td>\n",
       "<td>260.3513</td>\n",
       "<td>38284.0</td>\n",
       "<td>38189.0</td>\n",
       "<td>38224.0</td>\n",
       "<td>38616.0</td>\n",
       "<td>38770.0</td></tr>\n",
       "<tr><td>f0point5</td>\n",
       "<td>0.7060528</td>\n",
       "<td>0.0024726</td>\n",
       "<td>0.7068282</td>\n",
       "<td>0.7090363</td>\n",
       "<td>0.7074054</td>\n",
       "<td>0.7033247</td>\n",
       "<td>0.7036697</td></tr>\n",
       "<tr><td>f1</td>\n",
       "<td>0.7553660</td>\n",
       "<td>0.0007962</td>\n",
       "<td>0.7546338</td>\n",
       "<td>0.7559387</td>\n",
       "<td>0.7564296</td>\n",
       "<td>0.7551703</td>\n",
       "<td>0.7546575</td></tr>\n",
       "<tr><td>f2</td>\n",
       "<td>0.8120990</td>\n",
       "<td>0.0025985</td>\n",
       "<td>0.8093750</td>\n",
       "<td>0.8094857</td>\n",
       "<td>0.8127545</td>\n",
       "<td>0.815268</td>\n",
       "<td>0.8136117</td></tr>\n",
       "<tr><td>lift_top_group</td>\n",
       "<td>1.9738523</td>\n",
       "<td>0.0066798</td>\n",
       "<td>1.983887</td>\n",
       "<td>1.9668639</td>\n",
       "<td>1.975595</td>\n",
       "<td>1.9741449</td>\n",
       "<td>1.9687706</td></tr>\n",
       "<tr><td>logloss</td>\n",
       "<td>0.5152484</td>\n",
       "<td>0.0015012</td>\n",
       "<td>0.5159825</td>\n",
       "<td>0.5139705</td>\n",
       "<td>0.5135448</td>\n",
       "<td>0.5155311</td>\n",
       "<td>0.5172132</td></tr>\n",
       "<tr><td>max_per_class_error</td>\n",
       "<td>0.4084078</td>\n",
       "<td>0.0088510</td>\n",
       "<td>0.4030936</td>\n",
       "<td>0.3988317</td>\n",
       "<td>0.4047667</td>\n",
       "<td>0.4197293</td>\n",
       "<td>0.4156178</td></tr>\n",
       "<tr><td>mcc</td>\n",
       "<td>0.4628561</td>\n",
       "<td>0.0029887</td>\n",
       "<td>0.4624984</td>\n",
       "<td>0.4654087</td>\n",
       "<td>0.4663943</td>\n",
       "<td>0.4597335</td>\n",
       "<td>0.4602454</td></tr>\n",
       "<tr><td>mean_per_class_accuracy</td>\n",
       "<td>0.7232540</td>\n",
       "<td>0.0021944</td>\n",
       "<td>0.723706</td>\n",
       "<td>0.7253877</td>\n",
       "<td>0.7252206</td>\n",
       "<td>0.7206078</td>\n",
       "<td>0.7213476</td></tr>\n",
       "<tr><td>mean_per_class_error</td>\n",
       "<td>0.2767461</td>\n",
       "<td>0.0021944</td>\n",
       "<td>0.276294</td>\n",
       "<td>0.2746123</td>\n",
       "<td>0.2747793</td>\n",
       "<td>0.2793922</td>\n",
       "<td>0.2786524</td></tr>\n",
       "<tr><td>mse</td>\n",
       "<td>0.1724338</td>\n",
       "<td>0.0005862</td>\n",
       "<td>0.1727788</td>\n",
       "<td>0.1719781</td>\n",
       "<td>0.1716948</td>\n",
       "<td>0.1725932</td>\n",
       "<td>0.1731239</td></tr>\n",
       "<tr><td>pr_auc</td>\n",
       "<td>0.8222535</td>\n",
       "<td>0.0013994</td>\n",
       "<td>0.8220800</td>\n",
       "<td>0.8235418</td>\n",
       "<td>0.8234022</td>\n",
       "<td>0.8221856</td>\n",
       "<td>0.8200580</td></tr>\n",
       "<tr><td>precision</td>\n",
       "<td>0.6766099</td>\n",
       "<td>0.0035402</td>\n",
       "<td>0.6781864</td>\n",
       "<td>0.6808731</td>\n",
       "<td>0.6781067</td>\n",
       "<td>0.6725427</td>\n",
       "<td>0.6733406</td></tr>\n",
       "<tr><td>r2</td>\n",
       "<td>0.3102644</td>\n",
       "<td>0.0023444</td>\n",
       "<td>0.3088845</td>\n",
       "<td>0.3120873</td>\n",
       "<td>0.3132191</td>\n",
       "<td>0.3096269</td>\n",
       "<td>0.3075039</td></tr>\n",
       "<tr><td>recall</td>\n",
       "<td>0.8549157</td>\n",
       "<td>0.0048890</td>\n",
       "<td>0.8505057</td>\n",
       "<td>0.8496071</td>\n",
       "<td>0.855208</td>\n",
       "<td>0.8609449</td>\n",
       "<td>0.8583130</td></tr>\n",
       "<tr><td>rmse</td>\n",
       "<td>0.4152510</td>\n",
       "<td>0.0007059</td>\n",
       "<td>0.4156667</td>\n",
       "<td>0.4147024</td>\n",
       "<td>0.4143608</td>\n",
       "<td>0.4154434</td>\n",
       "<td>0.4160816</td></tr>\n",
       "<tr><td>specificity</td>\n",
       "<td>0.5915922</td>\n",
       "<td>0.0088510</td>\n",
       "<td>0.5969064</td>\n",
       "<td>0.6011683</td>\n",
       "<td>0.5952333</td>\n",
       "<td>0.5802707</td>\n",
       "<td>0.5843822</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-22.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-22 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-22 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-22 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-22 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-22 .h2o-table th,\n",
       "#h2o-table-22 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-22 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-22\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Scoring History: </caption>\n",
       "    <thead><tr><th></th>\n",
       "<th>timestamp</th>\n",
       "<th>duration</th>\n",
       "<th>number_of_trees</th>\n",
       "<th>training_rmse</th>\n",
       "<th>training_logloss</th>\n",
       "<th>training_auc</th>\n",
       "<th>training_pr_auc</th>\n",
       "<th>training_lift</th>\n",
       "<th>training_classification_error</th></tr></thead>\n",
       "    <tbody><tr><td></td>\n",
       "<td>2023-11-09 21:03:16</td>\n",
       "<td> 3 min 29.064 sec</td>\n",
       "<td>0.0</td>\n",
       "<td>0.5000000</td>\n",
       "<td>0.6931471</td>\n",
       "<td>0.5</td>\n",
       "<td>0.4998401</td>\n",
       "<td>1.0</td>\n",
       "<td>0.5001599</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 21:03:17</td>\n",
       "<td> 3 min 29.718 sec</td>\n",
       "<td>1.0</td>\n",
       "<td>0.4868523</td>\n",
       "<td>0.6671539</td>\n",
       "<td>0.8012611</td>\n",
       "<td>0.7904550</td>\n",
       "<td>1.8853230</td>\n",
       "<td>0.2940338</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 21:03:17</td>\n",
       "<td> 3 min 30.247 sec</td>\n",
       "<td>2.0</td>\n",
       "<td>0.4759415</td>\n",
       "<td>0.6459107</td>\n",
       "<td>0.8032011</td>\n",
       "<td>0.7941527</td>\n",
       "<td>1.9148960</td>\n",
       "<td>0.2967227</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 21:03:18</td>\n",
       "<td> 3 min 30.744 sec</td>\n",
       "<td>3.0</td>\n",
       "<td>0.4668622</td>\n",
       "<td>0.6283144</td>\n",
       "<td>0.8045394</td>\n",
       "<td>0.7956231</td>\n",
       "<td>1.9148960</td>\n",
       "<td>0.2930395</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 21:03:18</td>\n",
       "<td> 3 min 31.269 sec</td>\n",
       "<td>4.0</td>\n",
       "<td>0.4592369</td>\n",
       "<td>0.6134883</td>\n",
       "<td>0.8058911</td>\n",
       "<td>0.7970095</td>\n",
       "<td>1.9140762</td>\n",
       "<td>0.2862670</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 21:03:19</td>\n",
       "<td> 3 min 31.826 sec</td>\n",
       "<td>5.0</td>\n",
       "<td>0.4529236</td>\n",
       "<td>0.6010879</td>\n",
       "<td>0.8066021</td>\n",
       "<td>0.7979727</td>\n",
       "<td>1.9154512</td>\n",
       "<td>0.2889717</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 21:03:19</td>\n",
       "<td> 3 min 32.374 sec</td>\n",
       "<td>6.0</td>\n",
       "<td>0.4476318</td>\n",
       "<td>0.5905441</td>\n",
       "<td>0.8073334</td>\n",
       "<td>0.7994223</td>\n",
       "<td>1.9252765</td>\n",
       "<td>0.2838274</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 21:03:20</td>\n",
       "<td> 3 min 32.902 sec</td>\n",
       "<td>7.0</td>\n",
       "<td>0.4432372</td>\n",
       "<td>0.5816248</td>\n",
       "<td>0.8079129</td>\n",
       "<td>0.8002026</td>\n",
       "<td>1.9244673</td>\n",
       "<td>0.2835248</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 21:03:24</td>\n",
       "<td> 3 min 37.017 sec</td>\n",
       "<td>16.0</td>\n",
       "<td>0.4242118</td>\n",
       "<td>0.5394483</td>\n",
       "<td>0.8141335</td>\n",
       "<td>0.8114251</td>\n",
       "<td>1.9631802</td>\n",
       "<td>0.2826098</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 21:03:28</td>\n",
       "<td> 3 min 41.329 sec</td>\n",
       "<td>24.0</td>\n",
       "<td>0.4193011</td>\n",
       "<td>0.5268246</td>\n",
       "<td>0.8174917</td>\n",
       "<td>0.8159203</td>\n",
       "<td>1.9703709</td>\n",
       "<td>0.2803677</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 21:03:32</td>\n",
       "<td> 3 min 45.368 sec</td>\n",
       "<td>33.0</td>\n",
       "<td>0.4166989</td>\n",
       "<td>0.5200798</td>\n",
       "<td>0.8203950</td>\n",
       "<td>0.8198945</td>\n",
       "<td>1.9729654</td>\n",
       "<td>0.2782206</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 21:03:37</td>\n",
       "<td> 3 min 49.707 sec</td>\n",
       "<td>43.0</td>\n",
       "<td>0.4149690</td>\n",
       "<td>0.5158119</td>\n",
       "<td>0.8228030</td>\n",
       "<td>0.8229091</td>\n",
       "<td>1.9775779</td>\n",
       "<td>0.2769511</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 21:03:41</td>\n",
       "<td> 3 min 54.058 sec</td>\n",
       "<td>53.0</td>\n",
       "<td>0.4137734</td>\n",
       "<td>0.5129038</td>\n",
       "<td>0.8245924</td>\n",
       "<td>0.8251101</td>\n",
       "<td>1.9778661</td>\n",
       "<td>0.2755145</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 21:03:45</td>\n",
       "<td> 3 min 58.443 sec</td>\n",
       "<td>63.0</td>\n",
       "<td>0.4128660</td>\n",
       "<td>0.5108103</td>\n",
       "<td>0.8260253</td>\n",
       "<td>0.8268226</td>\n",
       "<td>1.9764248</td>\n",
       "<td>0.2750433</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 21:03:49</td>\n",
       "<td> 4 min  2.458 sec</td>\n",
       "<td>72.0</td>\n",
       "<td>0.4121690</td>\n",
       "<td>0.5092063</td>\n",
       "<td>0.8271317</td>\n",
       "<td>0.8282184</td>\n",
       "<td>1.9775779</td>\n",
       "<td>0.2710187</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 21:03:54</td>\n",
       "<td> 4 min  6.815 sec</td>\n",
       "<td>82.0</td>\n",
       "<td>0.4115678</td>\n",
       "<td>0.5078123</td>\n",
       "<td>0.8280866</td>\n",
       "<td>0.8293121</td>\n",
       "<td>1.9798841</td>\n",
       "<td>0.2710273</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 21:03:58</td>\n",
       "<td> 4 min 11.149 sec</td>\n",
       "<td>92.0</td>\n",
       "<td>0.4110134</td>\n",
       "<td>0.5065836</td>\n",
       "<td>0.8289860</td>\n",
       "<td>0.8303398</td>\n",
       "<td>1.9793075</td>\n",
       "<td>0.2695201</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-11-09 21:04:01</td>\n",
       "<td> 4 min 14.603 sec</td>\n",
       "<td>100.0</td>\n",
       "<td>0.4106479</td>\n",
       "<td>0.5057681</td>\n",
       "<td>0.8295815</td>\n",
       "<td>0.8310413</td>\n",
       "<td>1.9795958</td>\n",
       "<td>0.2689278</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-23.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-23 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-23 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-23 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-23 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-23 .h2o-table th,\n",
       "#h2o-table-23 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-23 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-23\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Variable Importances: </caption>\n",
       "    <thead><tr><th>variable</th>\n",
       "<th>relative_importance</th>\n",
       "<th>scaled_importance</th>\n",
       "<th>percentage</th></tr></thead>\n",
       "    <tbody><tr><td>sex</td>\n",
       "<td>109815.5</td>\n",
       "<td>1.0</td>\n",
       "<td>0.3642448</td></tr>\n",
       "<tr><td>age</td>\n",
       "<td>69633.8203125</td>\n",
       "<td>0.6340983</td>\n",
       "<td>0.2309670</td></tr>\n",
       "<tr><td>gamma_GTP</td>\n",
       "<td>37638.6289062</td>\n",
       "<td>0.3427442</td>\n",
       "<td>0.1248428</td></tr>\n",
       "<tr><td>SMK_stat_type_cd</td>\n",
       "<td>28268.8457031</td>\n",
       "<td>0.2574213</td>\n",
       "<td>0.0937644</td></tr>\n",
       "<tr><td>HDL_chole</td>\n",
       "<td>18703.9707031</td>\n",
       "<td>0.1703218</td>\n",
       "<td>0.0620388</td></tr>\n",
       "<tr><td>SGOT_ALT</td>\n",
       "<td>9447.3222656</td>\n",
       "<td>0.0860290</td>\n",
       "<td>0.0313356</td></tr>\n",
       "<tr><td>height</td>\n",
       "<td>8054.6665039</td>\n",
       "<td>0.0733473</td>\n",
       "<td>0.0267164</td></tr>\n",
       "<tr><td>SGOT_AST</td>\n",
       "<td>2914.7712402</td>\n",
       "<td>0.0265424</td>\n",
       "<td>0.0096679</td></tr>\n",
       "<tr><td>LDL_chole</td>\n",
       "<td>2739.8293457</td>\n",
       "<td>0.0249494</td>\n",
       "<td>0.0090877</td></tr>\n",
       "<tr><td>weight</td>\n",
       "<td>2295.0285645</td>\n",
       "<td>0.0208989</td>\n",
       "<td>0.0076123</td></tr>\n",
       "<tr><td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td></tr>\n",
       "<tr><td>BLDS</td>\n",
       "<td>1382.4124756</td>\n",
       "<td>0.0125885</td>\n",
       "<td>0.0045853</td></tr>\n",
       "<tr><td>waistline</td>\n",
       "<td>1253.3624268</td>\n",
       "<td>0.0114133</td>\n",
       "<td>0.0041573</td></tr>\n",
       "<tr><td>serum_creatinine</td>\n",
       "<td>1147.5129395</td>\n",
       "<td>0.0104495</td>\n",
       "<td>0.0038062</td></tr>\n",
       "<tr><td>SBP</td>\n",
       "<td>958.2173462</td>\n",
       "<td>0.0087257</td>\n",
       "<td>0.0031783</td></tr>\n",
       "<tr><td>tot_chole</td>\n",
       "<td>595.4566040</td>\n",
       "<td>0.0054223</td>\n",
       "<td>0.0019751</td></tr>\n",
       "<tr><td>sight_right</td>\n",
       "<td>284.5676880</td>\n",
       "<td>0.0025913</td>\n",
       "<td>0.0009439</td></tr>\n",
       "<tr><td>sight_left</td>\n",
       "<td>274.8047791</td>\n",
       "<td>0.0025024</td>\n",
       "<td>0.0009115</td></tr>\n",
       "<tr><td>urine_protein</td>\n",
       "<td>253.4477081</td>\n",
       "<td>0.0023079</td>\n",
       "<td>0.0008407</td></tr>\n",
       "<tr><td>hear_left</td>\n",
       "<td>49.4229813</td>\n",
       "<td>0.0004501</td>\n",
       "<td>0.0001639</td></tr>\n",
       "<tr><td>hear_right</td>\n",
       "<td>40.1841736</td>\n",
       "<td>0.0003659</td>\n",
       "<td>0.0001333</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "<pre style='font-size: smaller; margin-bottom: 1em;'>[23 rows x 4 columns]</pre></div><pre style=\"font-size: smaller; margin: 1em 0 0 0;\">\n",
       "\n",
       "[tips]\n",
       "Use `model.explain()` to inspect the model.\n",
       "--\n",
       "Use `h2o.display.toggle_user_tips()` to switch on/off this section.</pre>"
      ],
      "text/plain": [
       "Model Details\n",
       "=============\n",
       "H2OGradientBoostingEstimator : Gradient Boosting Machine\n",
       "Model Key: GBM_model_python_1699563465891_28\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",
       "    100                100                         154275                 7            7            7             81            128           118.27\n",
       "\n",
       "ModelMetricsBinomial: gbm\n",
       "** Reported on train data. **\n",
       "\n",
       "MSE: 0.16863170701986008\n",
       "RMSE: 0.41064791125714994\n",
       "LogLoss: 0.5057681196160003\n",
       "Mean Per-Class Error: 0.2688883006064671\n",
       "AUC: 0.8295815337792323\n",
       "AUCPR: 0.831041283791346\n",
       "Gini: 0.6591630675584645\n",
       "\n",
       "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.38537513318477773\n",
       "       N       Y       Error    Rate\n",
       "-----  ------  ------  -------  -------------------\n",
       "N      210874  136226  0.3925   (136226.0/347100.0)\n",
       "Y      50404   296474  0.1453   (50404.0/346878.0)\n",
       "Total  261278  432700  0.2689   (186630.0/693978.0)\n",
       "\n",
       "Maximum Metrics: Maximum metrics at their respective thresholds\n",
       "metric                       threshold    value     idx\n",
       "---------------------------  -----------  --------  -----\n",
       "max f1                       0.385375     0.760601  250\n",
       "max f2                       0.170955     0.856118  338\n",
       "max f0point5                 0.612956     0.758347  155\n",
       "max accuracy                 0.497316     0.746008  203\n",
       "max precision                0.980907     0.990724  0\n",
       "max recall                   0.0211488    1         399\n",
       "max specificity              0.980907     0.999986  0\n",
       "max absolute_mcc             0.497316     0.492116  203\n",
       "max min_per_class_accuracy   0.507409     0.745493  199\n",
       "max mean_per_class_accuracy  0.497316     0.746011  203\n",
       "max tns                      0.980907     347095    0\n",
       "max fns                      0.980907     346344    0\n",
       "max fps                      0.0211488    347100    399\n",
       "max tps                      0.0211488    346878    399\n",
       "max tnr                      0.980907     0.999986  0\n",
       "max fnr                      0.980907     0.998461  0\n",
       "max fpr                      0.0211488    1         399\n",
       "max tpr                      0.0211488    1         399\n",
       "\n",
       "Gains/Lift Table: Avg response rate: 49.98 %, avg score: 49.98 %\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        0.0100003                   0.963732           1.9796    1.9796             0.989481         0.970931   0.989481                    0.970931            0.0197966       0.0197966                  97.9596   97.9596            0.0195863\n",
       "2        0.0200006                   0.953817           1.97124   1.97542            0.985303         0.958632   0.987392                    0.964781            0.019713        0.0395096                  97.1236   97.5416            0.0390054\n",
       "3        0.030001                    0.944818           1.95163   1.96749            0.975504         0.949222   0.983429                    0.959595            0.0195169       0.0590265                  95.1633   96.7488            0.0580326\n",
       "4        0.0400013                   0.936229           1.93549   1.95949            0.967435         0.940527   0.979431                    0.954828            0.0193555       0.078382                   93.5489   95.9489            0.076737\n",
       "5        0.0500001                   0.927517           1.92625   1.95284            0.962819         0.93192    0.976109                    0.950247            0.0192604       0.0976424                  92.6254   95.2842            0.095254\n",
       "6        0.1                         0.881961           1.86411   1.90848            0.931756         0.905012   0.953932                    0.927629            0.0932057       0.190848                   86.4108   90.8475            0.181637\n",
       "7        0.15                        0.833916           1.7507    1.85588            0.875068         0.858045   0.927644                    0.904434            0.0875351       0.278383                   75.0697   85.5883            0.256683\n",
       "8        0.200001                    0.78476            1.64605   1.80342            0.822761         0.809388   0.901424                    0.880673            0.0823027       0.360686                   64.6049   80.3424            0.321268\n",
       "9        0.300001                    0.689822           1.4766    1.69448            0.738062         0.73692    0.84697                     0.832755            0.14766         0.508346                   47.6596   69.4481            0.416557\n",
       "10       0.4                         0.59882            1.28467   1.59203            0.642132         0.644252   0.795761                    0.78563             0.128466        0.636812                   28.4674   59.2031            0.473473\n",
       "11       0.5                         0.505337           1.09185   1.49199            0.545751         0.552316   0.745759                    0.738967            0.109185        0.745997                   9.18505   49.1994            0.491837\n",
       "12       0.6                         0.407643           0.901352  1.39355            0.450532         0.457032   0.696554                    0.691978            0.0901354       0.836133                   -9.86482  39.3554            0.472114\n",
       "13       0.699999                    0.308511           0.707375  1.29553            0.353574         0.357814   0.647557                    0.644241            0.0707367       0.906869                   -29.2625  29.5529            0.413608\n",
       "14       0.799999                    0.210702           0.508765  1.19718            0.254301         0.259886   0.5984                      0.596196            0.0508767       0.957746                   -49.1235  19.7183            0.315392\n",
       "15       0.9                         0.111958           0.308638  1.09846            0.15427          0.160409   0.549052                    0.547775            0.0308639       0.98861                    -69.1362  9.84557            0.177164\n",
       "16       1                           0.00601637         0.113901  1                  0.0569325        0.0683813  0.49984                     0.499836            0.0113902       1                          -88.6099  0                  0\n",
       "\n",
       "ModelMetricsBinomial: gbm\n",
       "** Reported on cross-validation data. **\n",
       "\n",
       "MSE: 0.1724333366790474\n",
       "RMSE: 0.4152509321832371\n",
       "LogLoss: 0.5152475686346667\n",
       "Mean Per-Class Error: 0.27717523901296864\n",
       "AUC: 0.8216203468926443\n",
       "AUCPR: 0.8222467083702414\n",
       "Gini: 0.6432406937852886\n",
       "\n",
       "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.37602342252866316\n",
       "       N       Y       Error    Rate\n",
       "-----  ------  ------  -------  -------------------\n",
       "N      204797  142303  0.41     (142303.0/347100.0)\n",
       "Y      50080   296798  0.1444   (50080.0/346878.0)\n",
       "Total  254877  439101  0.2772   (192383.0/693978.0)\n",
       "\n",
       "Maximum Metrics: Maximum metrics at their respective thresholds\n",
       "metric                       threshold    value     idx\n",
       "---------------------------  -----------  --------  -----\n",
       "max f1                       0.376023     0.755231  252\n",
       "max f2                       0.171154     0.854308  336\n",
       "max f0point5                 0.606687     0.749725  157\n",
       "max accuracy                 0.50193      0.739346  200\n",
       "max precision                0.97023      0.988131  3\n",
       "max recall                   0.0202079    1         399\n",
       "max specificity              0.980254     0.999971  0\n",
       "max absolute_mcc             0.50193      0.478717  200\n",
       "max min_per_class_accuracy   0.50809      0.737755  198\n",
       "max mean_per_class_accuracy  0.50193      0.739348  200\n",
       "max tns                      0.980254     347090    0\n",
       "max fns                      0.980254     346296    0\n",
       "max fps                      0.0202079    347100    399\n",
       "max tps                      0.0202079    346878    399\n",
       "max tnr                      0.980254     0.999971  0\n",
       "max fnr                      0.980254     0.998322  0\n",
       "max fpr                      0.0202079    1         399\n",
       "max tpr                      0.0202079    1         399\n",
       "\n",
       "Gains/Lift Table: Avg response rate: 49.98 %, avg score: 49.99 %\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        0.0100003                   0.963252           1.97412   1.97412            0.986744         0.970476   0.986744                    0.970476            0.0197418       0.0197418                  97.4119   97.4119            0.0194768\n",
       "2        0.0200006                   0.953344           1.96143   1.96778            0.980403         0.958206   0.983573                    0.964341            0.019615        0.0393568                  96.1434   96.7776            0.0386999\n",
       "3        0.030001                    0.944482           1.94587   1.96047            0.972622         0.948887   0.979923                    0.95919             0.0194593       0.0588161                  94.5867   96.0473            0.0576118\n",
       "4        0.0400013                   0.935782           1.9205    1.95048            0.959942         0.940157   0.974928                    0.954432            0.0192056       0.0780217                  92.0499   95.048             0.0760165\n",
       "5        0.0500001                   0.927157           1.90636   1.94166            0.952875         0.931456   0.970518                    0.949837            0.0190615       0.0970831                  90.636    94.1657            0.0941358\n",
       "6        0.1                         0.882086           1.84681   1.89423            0.92311          0.904961   0.946814                    0.927399            0.0923408       0.189424                   84.6811   89.4234            0.17879\n",
       "7        0.15                        0.834251           1.73375   1.84074            0.866596         0.858412   0.920075                    0.904403            0.0866875       0.276111                   73.3746   84.0738            0.252141\n",
       "8        0.200001                    0.785684           1.61549   1.78443            0.807487         0.809963   0.891928                    0.880793            0.0807748       0.356886                   61.5491   78.4426            0.313671\n",
       "9        0.300001                    0.690478           1.45558   1.67481            0.727557         0.737804   0.837137                    0.83313             0.145558        0.502445                   45.558    67.4811            0.404758\n",
       "10       0.4                         0.598544           1.27404   1.57462            0.636814         0.64455    0.787057                    0.785985            0.127402        0.629847                   27.4036   57.4618            0.459547\n",
       "11       0.5                         0.505098           1.09496   1.47869            0.547307         0.552126   0.739107                    0.739213            0.109497        0.739344                   9.4964    47.8687            0.478534\n",
       "12       0.6                         0.407095           0.905013  1.38307            0.452362         0.456487   0.691316                    0.692092            0.0905016       0.829845                   -9.4987   38.3074            0.459543\n",
       "13       0.699999                    0.308968           0.722222  1.28867            0.360995         0.357811   0.644128                    0.644338            0.0722214       0.902066                   -27.7778  28.8668            0.404005\n",
       "14       0.799999                    0.210938           0.529291  1.19375            0.264561         0.260242   0.596682                    0.596326            0.0529293       0.954996                   -47.0709  19.3745            0.309893\n",
       "15       0.9                         0.111343           0.326512  1.09739            0.163204         0.160626   0.548517                    0.547915            0.0326512       0.987647                   -67.3488  9.73859            0.175238\n",
       "16       1                           0.00789608         0.12353   1                  0.0617453        0.0681147  0.49984                     0.499935            0.012353        1                          -87.647   0                  0\n",
       "\n",
       "Cross-Validation Metrics Summary: \n",
       "                         mean      sd           cv_1_valid    cv_2_valid    cv_3_valid    cv_4_valid    cv_5_valid\n",
       "-----------------------  --------  -----------  ------------  ------------  ------------  ------------  ------------\n",
       "accuracy                 0.723212  0.00217529   0.723629      0.725484      0.725029      0.720698      0.72122\n",
       "auc                      0.821624  0.00122623   0.820883      0.822561      0.823179      0.821314      0.820184\n",
       "err                      0.276788  0.00217529   0.276371      0.274516      0.274971      0.279302      0.27878\n",
       "err_count                38416.6   260.351      38284         38189         38224         38616         38770\n",
       "f0point5                 0.706053  0.00247256   0.706828      0.709036      0.707405      0.703325      0.70367\n",
       "f1                       0.755366  0.00079616   0.754634      0.755939      0.75643       0.75517       0.754657\n",
       "f2                       0.812099  0.00259853   0.809375      0.809486      0.812755      0.815268      0.813612\n",
       "lift_top_group           1.97385   0.00667981   1.98389       1.96686       1.97559       1.97414       1.96877\n",
       "logloss                  0.515248  0.00150121   0.515983      0.51397       0.513545      0.515531      0.517213\n",
       "max_per_class_error      0.408408  0.008851     0.403094      0.398832      0.404767      0.419729      0.415618\n",
       "mcc                      0.462856  0.00298869   0.462498      0.465409      0.466394      0.459734      0.460245\n",
       "mean_per_class_accuracy  0.723254  0.00219441   0.723706      0.725388      0.725221      0.720608      0.721348\n",
       "mean_per_class_error     0.276746  0.00219441   0.276294      0.274612      0.274779      0.279392      0.278652\n",
       "mse                      0.172434  0.000586172  0.172779      0.171978      0.171695      0.172593      0.173124\n",
       "pr_auc                   0.822253  0.00139945   0.82208       0.823542      0.823402      0.822186      0.820058\n",
       "precision                0.67661   0.00354017   0.678186      0.680873      0.678107      0.672543      0.673341\n",
       "r2                       0.310264  0.00234437   0.308884      0.312087      0.313219      0.309627      0.307504\n",
       "recall                   0.854916  0.00488895   0.850506      0.849607      0.855208      0.860945      0.858313\n",
       "rmse                     0.415251  0.000705897  0.415667      0.414702      0.414361      0.415443      0.416082\n",
       "specificity              0.591592  0.008851     0.596906      0.601168      0.595233      0.580271      0.584382\n",
       "\n",
       "Scoring History: \n",
       "    timestamp            duration          number_of_trees    training_rmse    training_logloss    training_auc    training_pr_auc    training_lift    training_classification_error\n",
       "--  -------------------  ----------------  -----------------  ---------------  ------------------  --------------  -----------------  ---------------  -------------------------------\n",
       "    2023-11-09 21:03:16  3 min 29.064 sec  0                  0.5              0.693147            0.5             0.49984            1                0.50016\n",
       "    2023-11-09 21:03:17  3 min 29.718 sec  1                  0.486852         0.667154            0.801261        0.790455           1.88532          0.294034\n",
       "    2023-11-09 21:03:17  3 min 30.247 sec  2                  0.475942         0.645911            0.803201        0.794153           1.9149           0.296723\n",
       "    2023-11-09 21:03:18  3 min 30.744 sec  3                  0.466862         0.628314            0.804539        0.795623           1.9149           0.29304\n",
       "    2023-11-09 21:03:18  3 min 31.269 sec  4                  0.459237         0.613488            0.805891        0.79701            1.91408          0.286267\n",
       "    2023-11-09 21:03:19  3 min 31.826 sec  5                  0.452924         0.601088            0.806602        0.797973           1.91545          0.288972\n",
       "    2023-11-09 21:03:19  3 min 32.374 sec  6                  0.447632         0.590544            0.807333        0.799422           1.92528          0.283827\n",
       "    2023-11-09 21:03:20  3 min 32.902 sec  7                  0.443237         0.581625            0.807913        0.800203           1.92447          0.283525\n",
       "    2023-11-09 21:03:24  3 min 37.017 sec  16                 0.424212         0.539448            0.814133        0.811425           1.96318          0.28261\n",
       "    2023-11-09 21:03:28  3 min 41.329 sec  24                 0.419301         0.526825            0.817492        0.81592            1.97037          0.280368\n",
       "    2023-11-09 21:03:32  3 min 45.368 sec  33                 0.416699         0.52008             0.820395        0.819895           1.97297          0.278221\n",
       "    2023-11-09 21:03:37  3 min 49.707 sec  43                 0.414969         0.515812            0.822803        0.822909           1.97758          0.276951\n",
       "    2023-11-09 21:03:41  3 min 54.058 sec  53                 0.413773         0.512904            0.824592        0.82511            1.97787          0.275514\n",
       "    2023-11-09 21:03:45  3 min 58.443 sec  63                 0.412866         0.51081             0.826025        0.826823           1.97642          0.275043\n",
       "    2023-11-09 21:03:49  4 min  2.458 sec  72                 0.412169         0.509206            0.827132        0.828218           1.97758          0.271019\n",
       "    2023-11-09 21:03:54  4 min  6.815 sec  82                 0.411568         0.507812            0.828087        0.829312           1.97988          0.271027\n",
       "    2023-11-09 21:03:58  4 min 11.149 sec  92                 0.411013         0.506584            0.828986        0.83034            1.97931          0.26952\n",
       "    2023-11-09 21:04:01  4 min 14.603 sec  100                0.410648         0.505768            0.829582        0.831041           1.9796           0.268928\n",
       "\n",
       "Variable Importances: \n",
       "variable          relative_importance    scaled_importance       percentage\n",
       "----------------  ---------------------  ----------------------  ----------------------\n",
       "sex               109815.5               1.0                     0.36424482740099745\n",
       "age               69633.8203125          0.6340982858749448      0.2309670206937876\n",
       "gamma_GTP         37638.62890625         0.34274422924131837     0.12484281262269191\n",
       "SMK_stat_type_cd  28268.845703125        0.2574212720711102      0.09376436681488673\n",
       "HDL_chole         18703.970703125        0.17032177336646467     0.06203882494249972\n",
       "SGOT_ALT          9447.322265625         0.08602904203527735     0.03133563356761275\n",
       "height            8054.66650390625       0.0733472643106506      0.026716361629168264\n",
       "SGOT_AST          2914.771240234375      0.026542439275278763    0.009667946212625369\n",
       "LDL_chole         2739.829345703125      0.02494938643181632     0.009087684954617722\n",
       "weight            2295.028564453125      0.020898949278135827    0.0076123341726767844\n",
       "---               ---                    ---                     ---\n",
       "BLDS              1382.4124755859375     0.012588500490239879    0.004585296188304796\n",
       "waistline         1253.3624267578125     0.011413347175560942    0.004157252672029856\n",
       "serum_creatinine  1147.512939453125      0.010449462411527744    0.0038061626325201336\n",
       "SBP               958.2173461914062      0.008725702165827286    0.003178291879344269\n",
       "tot_chole         595.4566040039062      0.005422336591864594    0.0019750580560138316\n",
       "sight_right       284.56768798828125     0.0025913253410336542   0.0009438768515846341\n",
       "sight_left        274.8047790527344      0.002502422509142465    0.0009114944549269681\n",
       "urine_protein     253.4477081298828      0.002307941120605769    0.0008406556151267129\n",
       "hear_left         49.42298126220703      0.0004500546941206572   0.00016393009438098747\n",
       "hear_right        40.184173583984375     0.00036592442400193393  0.00013328607866239383\n",
       "[23 rows x 4 columns]\n",
       "\n",
       "\n",
       "[tips]\n",
       "Use `model.explain()` to inspect the model.\n",
       "--\n",
       "Use `h2o.display.toggle_user_tips()` to switch on/off this section."
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_gbm = H2OGradientBoostingEstimator(nfolds = 5, ntrees = 100, max_depth = 7, stopping_rounds = 5, stopping_metric = \"AUC\")\n",
    "model_gbm.train(x = x, y = y, training_frame = train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "94c502da-c850-4260-9bec-43f97594fbef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.82239\n",
      "Gini: 0.64477\n"
     ]
    }
   ],
   "source": [
    "# Eval performance:\n",
    "valid_perf4 = model_gbm.model_performance(valid)\n",
    "\n",
    "# Retreive test set AUC and Gini\n",
    "print(\"AUC: \" + str(round(valid_perf4.auc(), 5)))\n",
    "print(\"Gini: \" + str(round(valid_perf4.gini(), 5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e2ef8681-92b1-453d-9f26-6af0e4e50d05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n",
      "Prediction      N       Y\n",
      "Original                 \n",
      "N           89253   59505\n",
      "Y           22390  126220\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Prediction', ylabel='Original'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = 'DRK_YN'\n",
    "y_df = valid[y].as_data_frame()\n",
    "y_orig = y_df[y]\n",
    "\n",
    "valid_pred = model_gbm.predict(valid).as_data_frame()\n",
    "y_pred = valid_pred['predict']\n",
    "\n",
    "ct_target = pd.crosstab(y_orig, y_pred, rownames = [\"Original\"], colnames = [\"Prediction\"])\n",
    "print(ct_target)\n",
    "ct_normalize = pd.crosstab(y_orig, y_pred, rownames = [\"Original\"], colnames = [\"Prediction\"], normalize = True)\n",
    "sns.heatmap(ct_normalize, cmap='coolwarm', annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f8dd68ce-768b-4f4e-85a5-b719df2c1425",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H2O session _sid_9352 closed.\n"
     ]
    }
   ],
   "source": [
    "# Shutdown cluster\n",
    "h2o.cluster().shutdown()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}