4 H2O-3: Distributed ML.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# H2O-3 for Distributed ML\n",
    "\n",
    "This notebook is intended to help you get started with distributed machine learning in the H2O AI Cloud using python.\n",
    "\n",
    "* **Product Documentation:** https://docs.h2o.ai/h2o/latest-stable/h2o-docs/index.html\n",
    "* **Python Documentation:** https://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/index.html\n",
    "* **Additional Tutorials:** https://github.com/h2oai/h2o-tutorials"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequistes\n",
    "\n",
    "Update the `h2o_ai_cloud.py` file with the connection parameters for your H2O AI Cloud environemnt:\n",
    "1. Login to your H2O AI Cloud environment\n",
    "1. Click your username or avatar in the H2O AI Cloud navigation bar\n",
    "1. Navigate to `CLI & API Access`\n",
    "1. Use the variables from the `Accessing H2O AI Cloud APIs` section to populate the parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from getpass import getpass\n",
    "\n",
    "from h2o_ai_cloud import steam_client\n",
    "from h2osteam.clients import H2oKubernetesClient\n",
    "\n",
    "from h2o.estimators.glm import H2OGeneralizedLinearEstimator\n",
    "import h2o\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Securely connect to the platform\n",
    "We first connect to the H2O AI Cloud using our platform token to create a token provider object. We can then use this object to log into Steam and other APIs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visit https://cloud.h2o.ai/auth/get-platform-token to get your platform token\n",
      "Enter your platform token: ········\n"
     ]
    }
   ],
   "source": [
    "steam = steam_client()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connect to H2O-3\n",
    "We will connect to a specific instance of H2O-3. This step will connect our imported `h2o` library which we can then use to interact with the cluster. \n",
    "\n",
    "We will also check if our local python package and the backend server have the same version numbers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "for instance in steam.get_h2o_kubernetes_clusters():\n",
    "    print(instance[\"id\"], \"\\t\", instance[\"profile_name\"], \"\\t\", instance[\"status\"], \"\\t\", instance[\"name\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['visitor-h2o-kubernetes']\n"
     ]
    }
   ],
   "source": [
    "h2o_profiles = [profile[\"name\"] for profile in steam.get_profiles() if profile[\"profile_type\"] == \"h2o_kubernetes\"]\n",
    "\n",
    "print(h2o_profiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connecting to H2O server at https://steam.cloud.h2o.ai:443/proxy/h2o-k8s/946 ..... successful.\n",
      "Warning: Version mismatch. H2O is version 3.36.0.4, but the h2o-python package is version 3.36.0.3. Install the matching h2o-Python version from - http://h2o-release.s3.amazonaws.com/h2o/rel-zorn/4/index.html.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O_cluster_uptime:</td>\n",
       "<td>12 secs</td></tr>\n",
       "<tr><td>H2O_cluster_timezone:</td>\n",
       "<td>Etc/GMT</td></tr>\n",
       "<tr><td>H2O_data_parsing_timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O_cluster_version:</td>\n",
       "<td>3.36.0.4</td></tr>\n",
       "<tr><td>H2O_cluster_version_age:</td>\n",
       "<td>3 months and 5 days </td></tr>\n",
       "<tr><td>H2O_cluster_name:</td>\n",
       "<td>my-test-instance</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>3.229 Gb</td></tr>\n",
       "<tr><td>H2O_cluster_total_cores:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O_cluster_allowed_cores:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O_cluster_status:</td>\n",
       "<td>locked, healthy</td></tr>\n",
       "<tr><td>H2O_connection_url:</td>\n",
       "<td>https://steam.cloud.h2o.ai:443/proxy/h2o-k8s/946</td></tr>\n",
       "<tr><td>H2O_connection_proxy:</td>\n",
       "<td>null</td></tr>\n",
       "<tr><td>H2O_internal_security:</td>\n",
       "<td>True</td></tr>\n",
       "<tr><td>Python_version:</td>\n",
       "<td>3.8.12 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ------------------------------------------------\n",
       "H2O_cluster_uptime:         12 secs\n",
       "H2O_cluster_timezone:       Etc/GMT\n",
       "H2O_data_parsing_timezone:  UTC\n",
       "H2O_cluster_version:        3.36.0.4\n",
       "H2O_cluster_version_age:    3 months and 5 days\n",
       "H2O_cluster_name:           my-test-instance\n",
       "H2O_cluster_total_nodes:    1\n",
       "H2O_cluster_free_memory:    3.229 Gb\n",
       "H2O_cluster_total_cores:    1\n",
       "H2O_cluster_allowed_cores:  1\n",
       "H2O_cluster_status:         locked, healthy\n",
       "H2O_connection_url:         https://steam.cloud.h2o.ai:443/proxy/h2o-k8s/946\n",
       "H2O_connection_proxy:       null\n",
       "H2O_internal_security:      True\n",
       "Python_version:             3.8.12 final\n",
       "--------------------------  ------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<h2osteam.clients.h2ok8s.h2ok8s.H2oKubernetesCluster at 0x106c79c70>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster = h2o_machine = H2oKubernetesClient(steam).launch_cluster(\n",
    "    name=\"my-test-instance\",\n",
    "    version=steam.get_h2o_kubernetes_engines()[-1][\"version\"],  # newest version of the AutoML software,\n",
    "    profile_name=h2o_profiles[0],\n",
    "    max_uptime_h=1\n",
    ")\n",
    "while not h2o_machine.is_running():\n",
    "    pass\n",
    "cluster.connect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "We can create an H2O Dataframe object with data from our local machine or a URL. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n"
     ]
    }
   ],
   "source": [
    "data = h2o.import_file(\"https://h2o-internal-release.s3-us-west-2.amazonaws.com/data/Splunk/churn.csv\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3333, 21)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>State  </th><th style=\"text-align: right;\">  Account Length</th><th style=\"text-align: right;\">  Area Code</th><th>Phone   </th><th>Int&#x27;l Plan  </th><th>VMail Plan  </th><th style=\"text-align: right;\">  VMail Message</th><th style=\"text-align: right;\">  Day Mins</th><th style=\"text-align: right;\">  Day Calls</th><th style=\"text-align: right;\">  Day Charge</th><th style=\"text-align: right;\">  Eve Mins</th><th style=\"text-align: right;\">  Eve Calls</th><th style=\"text-align: right;\">  Eve Charge</th><th style=\"text-align: right;\">  Night Mins</th><th style=\"text-align: right;\">  Night Calls</th><th style=\"text-align: right;\">  Night Charge</th><th style=\"text-align: right;\">  Intl Mins</th><th style=\"text-align: right;\">  Intl Calls</th><th style=\"text-align: right;\">  Intl Charge</th><th style=\"text-align: right;\">  CustServ Calls</th><th>Churn?  </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>KS     </td><td style=\"text-align: right;\">             128</td><td style=\"text-align: right;\">        415</td><td>382-4657</td><td>no          </td><td>yes         </td><td style=\"text-align: right;\">             25</td><td style=\"text-align: right;\">     265.1</td><td style=\"text-align: right;\">        110</td><td style=\"text-align: right;\">       45.07</td><td style=\"text-align: right;\">     197.4</td><td style=\"text-align: right;\">         99</td><td style=\"text-align: right;\">       16.78</td><td style=\"text-align: right;\">       244.7</td><td style=\"text-align: right;\">           91</td><td style=\"text-align: right;\">         11.01</td><td style=\"text-align: right;\">       10  </td><td style=\"text-align: right;\">           3</td><td style=\"text-align: right;\">         2.7 </td><td style=\"text-align: right;\">               1</td><td>False.  </td></tr>\n",
       "<tr><td>OH     </td><td style=\"text-align: right;\">             107</td><td style=\"text-align: right;\">        415</td><td>371-7191</td><td>no          </td><td>yes         </td><td style=\"text-align: right;\">             26</td><td style=\"text-align: right;\">     161.6</td><td style=\"text-align: right;\">        123</td><td style=\"text-align: right;\">       27.47</td><td style=\"text-align: right;\">     195.5</td><td style=\"text-align: right;\">        103</td><td style=\"text-align: right;\">       16.62</td><td style=\"text-align: right;\">       254.4</td><td style=\"text-align: right;\">          103</td><td style=\"text-align: right;\">         11.45</td><td style=\"text-align: right;\">       13.7</td><td style=\"text-align: right;\">           3</td><td style=\"text-align: right;\">         3.7 </td><td style=\"text-align: right;\">               1</td><td>False.  </td></tr>\n",
       "<tr><td>NJ     </td><td style=\"text-align: right;\">             137</td><td style=\"text-align: right;\">        415</td><td>358-1921</td><td>no          </td><td>no          </td><td style=\"text-align: right;\">              0</td><td style=\"text-align: right;\">     243.4</td><td style=\"text-align: right;\">        114</td><td style=\"text-align: right;\">       41.38</td><td style=\"text-align: right;\">     121.2</td><td style=\"text-align: right;\">        110</td><td style=\"text-align: right;\">       10.3 </td><td style=\"text-align: right;\">       162.6</td><td style=\"text-align: right;\">          104</td><td style=\"text-align: right;\">          7.32</td><td style=\"text-align: right;\">       12.2</td><td style=\"text-align: right;\">           5</td><td style=\"text-align: right;\">         3.29</td><td style=\"text-align: right;\">               0</td><td>False.  </td></tr>\n",
       "<tr><td>OH     </td><td style=\"text-align: right;\">              84</td><td style=\"text-align: right;\">        408</td><td>375-9999</td><td>yes         </td><td>no          </td><td style=\"text-align: right;\">              0</td><td style=\"text-align: right;\">     299.4</td><td style=\"text-align: right;\">         71</td><td style=\"text-align: right;\">       50.9 </td><td style=\"text-align: right;\">      61.9</td><td style=\"text-align: right;\">         88</td><td style=\"text-align: right;\">        5.26</td><td style=\"text-align: right;\">       196.9</td><td style=\"text-align: right;\">           89</td><td style=\"text-align: right;\">          8.86</td><td style=\"text-align: right;\">        6.6</td><td style=\"text-align: right;\">           7</td><td style=\"text-align: right;\">         1.78</td><td style=\"text-align: right;\">               2</td><td>False.  </td></tr>\n",
       "<tr><td>OK     </td><td style=\"text-align: right;\">              75</td><td style=\"text-align: right;\">        415</td><td>330-6626</td><td>yes         </td><td>no          </td><td style=\"text-align: right;\">              0</td><td style=\"text-align: right;\">     166.7</td><td style=\"text-align: right;\">        113</td><td style=\"text-align: right;\">       28.34</td><td style=\"text-align: right;\">     148.3</td><td style=\"text-align: right;\">        122</td><td style=\"text-align: right;\">       12.61</td><td style=\"text-align: right;\">       186.9</td><td style=\"text-align: right;\">          121</td><td style=\"text-align: right;\">          8.41</td><td style=\"text-align: right;\">       10.1</td><td style=\"text-align: right;\">           3</td><td style=\"text-align: right;\">         2.73</td><td style=\"text-align: right;\">               3</td><td>False.  </td></tr>\n",
       "<tr><td>AL     </td><td style=\"text-align: right;\">             118</td><td style=\"text-align: right;\">        510</td><td>391-8027</td><td>yes         </td><td>no          </td><td style=\"text-align: right;\">              0</td><td style=\"text-align: right;\">     223.4</td><td style=\"text-align: right;\">         98</td><td style=\"text-align: right;\">       37.98</td><td style=\"text-align: right;\">     220.6</td><td style=\"text-align: right;\">        101</td><td style=\"text-align: right;\">       18.75</td><td style=\"text-align: right;\">       203.9</td><td style=\"text-align: right;\">          118</td><td style=\"text-align: right;\">          9.18</td><td style=\"text-align: right;\">        6.3</td><td style=\"text-align: right;\">           6</td><td style=\"text-align: right;\">         1.7 </td><td style=\"text-align: right;\">               0</td><td>False.  </td></tr>\n",
       "<tr><td>MA     </td><td style=\"text-align: right;\">             121</td><td style=\"text-align: right;\">        510</td><td>355-9993</td><td>no          </td><td>yes         </td><td style=\"text-align: right;\">             24</td><td style=\"text-align: right;\">     218.2</td><td style=\"text-align: right;\">         88</td><td style=\"text-align: right;\">       37.09</td><td style=\"text-align: right;\">     348.5</td><td style=\"text-align: right;\">        108</td><td style=\"text-align: right;\">       29.62</td><td style=\"text-align: right;\">       212.6</td><td style=\"text-align: right;\">          118</td><td style=\"text-align: right;\">          9.57</td><td style=\"text-align: right;\">        7.5</td><td style=\"text-align: right;\">           7</td><td style=\"text-align: right;\">         2.03</td><td style=\"text-align: right;\">               3</td><td>False.  </td></tr>\n",
       "<tr><td>MO     </td><td style=\"text-align: right;\">             147</td><td style=\"text-align: right;\">        415</td><td>329-9001</td><td>yes         </td><td>no          </td><td style=\"text-align: right;\">              0</td><td style=\"text-align: right;\">     157  </td><td style=\"text-align: right;\">         79</td><td style=\"text-align: right;\">       26.69</td><td style=\"text-align: right;\">     103.1</td><td style=\"text-align: right;\">         94</td><td style=\"text-align: right;\">        8.76</td><td style=\"text-align: right;\">       211.8</td><td style=\"text-align: right;\">           96</td><td style=\"text-align: right;\">          9.53</td><td style=\"text-align: right;\">        7.1</td><td style=\"text-align: right;\">           6</td><td style=\"text-align: right;\">         1.92</td><td style=\"text-align: right;\">               0</td><td>False.  </td></tr>\n",
       "<tr><td>LA     </td><td style=\"text-align: right;\">             117</td><td style=\"text-align: right;\">        408</td><td>335-4719</td><td>no          </td><td>no          </td><td style=\"text-align: right;\">              0</td><td style=\"text-align: right;\">     184.5</td><td style=\"text-align: right;\">         97</td><td style=\"text-align: right;\">       31.37</td><td style=\"text-align: right;\">     351.6</td><td style=\"text-align: right;\">         80</td><td style=\"text-align: right;\">       29.89</td><td style=\"text-align: right;\">       215.8</td><td style=\"text-align: right;\">           90</td><td style=\"text-align: right;\">          9.71</td><td style=\"text-align: right;\">        8.7</td><td style=\"text-align: right;\">           4</td><td style=\"text-align: right;\">         2.35</td><td style=\"text-align: right;\">               1</td><td>False.  </td></tr>\n",
       "<tr><td>WV     </td><td style=\"text-align: right;\">             141</td><td style=\"text-align: right;\">        415</td><td>330-8173</td><td>yes         </td><td>yes         </td><td style=\"text-align: right;\">             37</td><td style=\"text-align: right;\">     258.6</td><td style=\"text-align: right;\">         84</td><td style=\"text-align: right;\">       43.96</td><td style=\"text-align: right;\">     222  </td><td style=\"text-align: right;\">        111</td><td style=\"text-align: right;\">       18.87</td><td style=\"text-align: right;\">       326.4</td><td style=\"text-align: right;\">           97</td><td style=\"text-align: right;\">         14.69</td><td style=\"text-align: right;\">       11.2</td><td style=\"text-align: right;\">           5</td><td style=\"text-align: right;\">         3.02</td><td style=\"text-align: right;\">               0</td><td>False.  </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'State': 'enum',\n",
       " 'Account Length': 'int',\n",
       " 'Area Code': 'int',\n",
       " 'Phone': 'string',\n",
       " \"Int'l Plan\": 'enum',\n",
       " 'VMail Plan': 'enum',\n",
       " 'VMail Message': 'int',\n",
       " 'Day Mins': 'real',\n",
       " 'Day Calls': 'int',\n",
       " 'Day Charge': 'real',\n",
       " 'Eve Mins': 'real',\n",
       " 'Eve Calls': 'int',\n",
       " 'Eve Charge': 'real',\n",
       " 'Night Mins': 'real',\n",
       " 'Night Calls': 'int',\n",
       " 'Night Charge': 'real',\n",
       " 'Intl Mins': 'real',\n",
       " 'Intl Calls': 'int',\n",
       " 'Intl Charge': 'real',\n",
       " 'CustServ Calls': 'int',\n",
       " 'Churn?': 'enum'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use H2O-3 to explore our dataset.  We can find correlations, build decision trees, and visualize our dataset.  In this demo, we will view the correlations and plot distributions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will first use H2O-3 to find the numeric columns that are correlated to Churn. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  Account Length</th><th style=\"text-align: right;\">  Area Code</th><th style=\"text-align: right;\">  VMail Message</th><th style=\"text-align: right;\">  Day Mins</th><th style=\"text-align: right;\">  Day Calls</th><th style=\"text-align: right;\">  Day Charge</th><th style=\"text-align: right;\">  Eve Mins</th><th style=\"text-align: right;\">  Eve Calls</th><th style=\"text-align: right;\">  Eve Charge</th><th style=\"text-align: right;\">  Night Mins</th><th style=\"text-align: right;\">  Night Calls</th><th style=\"text-align: right;\">  Night Charge</th><th style=\"text-align: right;\">  Intl Mins</th><th style=\"text-align: right;\">  Intl Calls</th><th style=\"text-align: right;\">  Intl Charge</th><th style=\"text-align: right;\">  CustServ Calls</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">       0.0165407</td><td style=\"text-align: right;\"> 0.00617423</td><td style=\"text-align: right;\">      -0.089728</td><td style=\"text-align: right;\">  0.205151</td><td style=\"text-align: right;\">  0.0184593</td><td style=\"text-align: right;\">    0.205151</td><td style=\"text-align: right;\"> 0.0927958</td><td style=\"text-align: right;\"> 0.00923313</td><td style=\"text-align: right;\">    0.092786</td><td style=\"text-align: right;\">   0.0354929</td><td style=\"text-align: right;\">    0.0061412</td><td style=\"text-align: right;\">     0.0354956</td><td style=\"text-align: right;\">  0.0682388</td><td style=\"text-align: right;\">  -0.0528443</td><td style=\"text-align: right;\">    0.0682586</td><td style=\"text-align: right;\">         0.20875</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_cols = [k for k, v in data.types.items() if v in ['real', 'int']]\n",
    "churn_cor_hf = data['Churn?'].cor(data[numeric_cols])\n",
    "churn_cor_hf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result of the `cor` function is an H2O Frame with one row, showing the correlation of each variable to `Churn?`. Since this data is very small, we can convert it to a Pandas dataframe and order it based on absolute correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Feature</th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CustServ Calls</td>\n",
       "      <td>0.208750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Day Mins</td>\n",
       "      <td>0.205151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Day Charge</td>\n",
       "      <td>0.205151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Eve Mins</td>\n",
       "      <td>0.092796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Eve Charge</td>\n",
       "      <td>0.092786</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Feature  Correlation\n",
       "15  CustServ Calls     0.208750\n",
       "3         Day Mins     0.205151\n",
       "5       Day Charge     0.205151\n",
       "6         Eve Mins     0.092796\n",
       "8       Eve Charge     0.092786"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_cor = churn_cor_hf.as_data_frame().transpose().reset_index()\n",
    "churn_cor.columns = ['Feature', 'Correlation']\n",
    "churn_cor = churn_cor.iloc[(-churn_cor['Correlation'].abs()).argsort()]\n",
    "churn_cor.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The greatest indicators of churn seem to be more calls to customer service as well as more calling minutes/charges.\n",
    "\n",
    "We can use the histogram function to see the distribution of these top features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['CustServ Calls'].hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Day Mins'].hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split a Dataset\n",
    "\n",
    "We will next split a dataset for training. When building models, we want to separate a section of data for validating how the model does.  This can be a good indicator of how well the model is at generalizing and predicting on unseen data.\n",
    "\n",
    "We will use the split function to create a random split on the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "splits = data.split_frame(ratios=[0.7, 0.15], seed=1)  \n",
    "\n",
    "train = splits[0]\n",
    "valid = splits[1]\n",
    "test = splits[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare columns for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = 'Churn?'\n",
    "x = [i for i in data.columns if i not in [y, 'Phone']] # remove columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "\n",
    "In this section, we will create models predicting Churn using H2O-3's algorithms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline Model\n",
    "\n",
    "We will start our modeling by building a baseline model.  This is a simple model that we will use as a control. In this example, we will build a Linear model to predict churn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first create an object of class, \"H2OGeneralizedLinearEstimator\". This does not actually do any training, it just sets the model up for training by specifying model parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "glm_fit1 = H2OGeneralizedLinearEstimator(model_id='glm_fit1',\n",
    "                                         ## fix a random number generator seed for reproducibility\n",
    "                                         seed=1234,\n",
    "                                         \n",
    "                                         ## predict a yes/no column\n",
    "                                         family='binomial',\n",
    "                                         \n",
    "                                         ## cross validation\n",
    "                                         nfolds=3, \n",
    "                                         \n",
    "                                         ## use cross validation to find the best regularization\n",
    "                                         lambda_search=True \n",
    "                                        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that `glm_fit1` object is initialized, we can train the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "glm Model Build progress: |██████████████████████████████████████████████████████| (done) 100%\n"
     ]
    }
   ],
   "source": [
    "glm_fit1.train(x = x, y = y, training_frame = train, validation_frame = valid);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot below shows the objective loss function as each iteration of the model is trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "glm_fit1.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explore predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the performance of the GLM that were just trained. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "glm_perf1 = glm_fit1.model_performance(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "glm_perf1.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the AUC on the training dataset compared with the validation dataset.  The linear model is slighly better at predicting on the training dataset.  This is to be expected since the training data was seen by the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC on Training Data: 0.834\n",
      "AUC on Validation Data: 0.817\n"
     ]
    }
   ],
   "source": [
    "print (\"AUC on Training Data: {0:.3f}\".format(glm_fit1.auc(train = True)))\n",
    "print (\"AUC on Validation Data: {0:.3f}\".format(glm_fit1.auc(valid = True)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the predictions on the validation dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "glm prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>predict  </th><th style=\"text-align: right;\">  False.</th><th style=\"text-align: right;\">    True.</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>False.   </td><td style=\"text-align: right;\">0.87653 </td><td style=\"text-align: right;\">0.12347  </td></tr>\n",
       "<tr><td>True.    </td><td style=\"text-align: right;\">0.486393</td><td style=\"text-align: right;\">0.513607 </td></tr>\n",
       "<tr><td>False.   </td><td style=\"text-align: right;\">0.71364 </td><td style=\"text-align: right;\">0.28636  </td></tr>\n",
       "<tr><td>False.   </td><td style=\"text-align: right;\">0.858283</td><td style=\"text-align: right;\">0.141717 </td></tr>\n",
       "<tr><td>False.   </td><td style=\"text-align: right;\">0.970588</td><td style=\"text-align: right;\">0.0294123</td></tr>\n",
       "<tr><td>False.   </td><td style=\"text-align: right;\">0.944882</td><td style=\"text-align: right;\">0.0551175</td></tr>\n",
       "<tr><td>False.   </td><td style=\"text-align: right;\">0.862908</td><td style=\"text-align: right;\">0.137092 </td></tr>\n",
       "<tr><td>False.   </td><td style=\"text-align: right;\">0.961935</td><td style=\"text-align: right;\">0.0380645</td></tr>\n",
       "<tr><td>False.   </td><td style=\"text-align: right;\">0.922746</td><td style=\"text-align: right;\">0.0772542</td></tr>\n",
       "<tr><td>False.   </td><td style=\"text-align: right;\">0.968175</td><td style=\"text-align: right;\">0.0318246</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glm_preds = glm_fit1.predict(valid)\n",
    "glm_preds.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since our model is a GLM model, we can also see the coefficients of each variable. The plot below shows that `Int'l Plan=Yes` increases the likelihood of churn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "glm_fit1.std_coef_plot(num_of_features=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PartialDependencePlot progress: |████████████████████████████████████████████████| (done) 100%\n"
     ]
    },
    {
     "data": {
      "image/png": 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gWs7jqeGyrBrgJGCZmQEcByw1syvdfUWEdYlICdGIkZ6LMriXA3PMbBZBYF8HvCP7pLsfBcZnH5vZMuCTCm2RwSGdcRr60MoezCI7OOnuKeBm4CHgReBed19jZreZ2ZVRva+IlL6WtjQHmxIK7V6KtI/b3R8EHmy37HOdrLskylpEpPjUyu4fOnNSRApiIF77sVgU3CISqYF2hfVSoOAWkcg0JVI0JdTK7m8KbhHpd8l0hvqWJKmMIjsKCm4R6TfuTmMiRXOb5heJkoJbRPpFWypDfWuStFrZkVNwi0ifuDsNiRQtamUXjIJbRHqtNZmmoTVFRuerF5SCW0R6TJcRKy4Ft4j0iCaFKj4Ft4jkJZ1x6luStOlEmqJTcItIt3QiTWlRcItIp3QiTWlScItIh+pbkxriV6KivFiwiMRQazJNKuMK7RKmFreIAJorO04U3CKiubJjRsEtMohprux4UnCLDELZWfxa2tJqZceQgltkkEmkgvlFNItffCm4RQYJzS8ycCi4RQaB5rYUjYmU5hcZILodx21mc83sT2b2Qvh4oZl9NvrSRKSvkukMBxsTNLQqtAeSfE7A+S7waSAJ4O6rgOuiLEpE+iaTcepbkxxqatPp6gNQPl0lw939aTPLXZaKqB4R6SNNuzrw5RPcB8zseAhGDZnZNcDuSKsSkR5LpTM0tKY07eogkE9wfwS4A5hnZjuBzcC7Iq1KRPKmMdmDT7fB7e6bgIvMbARQ5u4N0ZclIvnQNR8Hp3xGlXzMzEYCzcDXzWylmV0SfWki0pl0xjnS3MbRlqRCexDKZ1TJ+9y9HrgEGAe8G/hypFWJSIfcnaZEioONCc3iN4jl08edHU5yOXCnu6+xdkNMRCR6OlVdsvIJ7mfM7PfALODTZlYD6E+9SIFonmxpL5/gfj+wCNjk7s1mNg54b6RViUjQLdKWplkX6ZV28hlVkjGzvcB8M9PcJiIFoNEi0pVug9jMvgJcC6wFstOKOfBIhHWJDEo6iUbykU8L+mqgzt0TEdciMmjpJBrpiXyCexNQCSi4RSKgbhHpqXyCuxl4zsz+RE54u/tHI6tKZBBQt4j0Vj7BvTS8iUg/yHaLNLfpSjTSO/mMKvmxmQ0B5oaL1rt7MtqyRAamlrY0DQlNuSp9k8+okiXAj4EtBGdRTjOzG9xdo0pE8tSWypAOL24g0lf5zFXyH8Al7v4Gdz8fuBT4ej4bN7PLzGy9mW00s1s6eP5DZrbazJ4zs8fMbH7PyhcpbemMc7Q5yeHmNo0WkX6TT3BXuvv67AN3f4lglEmXzKwcuB14EzAfuL6DYP6Zu5/s7ouArwJfy7dwkVLmHpymfrAxoauqS7/L5+DkCjP7HnBX+PidwIo8XncmsDGczxszuxu4iuBEHgDCWQezRoAaJRJ/LW1pGhMa3ifRySe4P0xwFZzs8L9HgW/n8bopwPacxzuAs9qvZGYfAT4ODAHemMd2RUpSWypDQ2tSF+eVyJnn0SoIR5WcSDAr4Hp3b8vjNdcAl7n7B8LH7wbOcvebO1n/HcCl7n5DB8/dBNwEUFtbe/q9997bbc2lorGxkerq6mKXkZc41QqlVW86491+XWxpamTYiNKotztxqhXiV+/FF77xGXdf3NvX5zOq5M3Ad4CXCUaVzDKzD7r7b7t56U5gWs7jqeGyztwN/FdHT7j7HQTXvaSurs6XLFnSXdklY9myZcSl3jjVCsWvt6ez961e8QQnLz4n8rr6Q5xqhfjV21f5dJX8B3CBu28ECK/4/hugu+BeDswxs1kEgX0d8I7cFcxsjrtvCB++GdiASAzoNHUppnyCuyEb2qFNQLcXDHb3lJndDDwElAM/CK+ecxuwwt2XAjeb2UVAEjgMvKabRKSUtKUyNCZSJHWauhRRvqNKHgTuJRj18TfAcjN7G4C739fZC939QeDBdss+l3P/Y70pWqTQUukgsHUVGikF+QT3UGAv8Ibw8X5gGHAFQZB3GtwicZfJOI1tKVo13aqUkHzmKtFlymTQcXea29I0taU0r4iUnG7PnDSzr5rZSDOrNLM/mdl+M3tXIYoTKYbWZJoDjW00JhTaUpryOeX9kvAMx7cQTDR1AvCPURYlUgyJVJqDjQmOtiQ1WkRKWj593Nl13gz8wt2PmlmEJYkUlg48StzkE9wPmNk6oAX4sJnVAq3RliUSveyBxxZd0EBiJp+Dk7eY2VeBo+6eNrNmgsmiRGKpp2c8ivQXd+epzYf6vJ18TnkfDvwdMJ1gvpDJQB3wQJ/fXaTANHOfFMuLu+v51p83smLr4T5vK5+Dkz8E2oBzw8c7gX/r8zuLFFAwUiRBfasOPEph7Tzcwmf/5wVu/OFyNu5r5OMXz+3+Rd3Ip4/7eHe/1syuB3D3ZtPRSYmJRCpNY2tKU61KwR1uauMHf93MfSt3Ul5mvPfcmbzrnBlUV1XQ11PG8wnuNjMbRniRg3CSqUQf31ckUm2pDE2JFG2aU0QKrKUtzd3Lt3HnE1tpTaa54pTJ/O15s6mtqeq398gnuD8P/I7gIsE/BV4H3NhvFYj0Iw3tk2JJZTI88PxuvvvoJg40tnH+3PH83ZITmDV+RL+/V5fBbWZlwBjgbcDZBPNxf8zdD/R7JSJ9kM44jYkUrUkN7ZPCcnceeekA3162kS0Hm1k4dRRfeuvJnDJtdGTv2WVwu3vGzP7J3e8lmINbpKRkMk5TOBZbvdhSaKt2HOGbf97Iqh1HmTF2OF99+0LOnzueqA8D5tNV8kcz+yRwD9CUXejufR+MKNJL7k7G4UBjQoEtBbflQBPfXvYyf3lpP+NGDOGWN83jilMmUVGWz0C9vssnuK8N//1IzjIHZvd/OSJdc3dakq+MxVZoSyHtrW/l+49t5oHnd1NVWcYHz5/N9WdOZ9iQ8oLWkc+Zk7O6et7MLnb3P/RfSSKv5e60JjM6eUaK4mBjgh8/sZX7Vu4A4O2nT+F9r5vFmBFDilJPPi3u7nwFUHBLZHS2oxRLfUuSu57ayj3Lt5NMOW9eOIn3v34Wx40aWtS6+iO4dTKORKI17BJJ6+QZKbDmthT3LN/OXU9uozGR4uL5E7npvNlMHze82KUB/RPc+r9K+pUCW4olkUpz38qd/PjxLRxuTnLenPHcdP5s5k6sKXZpr9IfwS3SLxTYUiypdIYHVu3m+49tZl9DgsUzxvChJcdz8pRRxS6tQ/0R3Fv6YRsyiLUm0zQlNJ+IFF7GnT+s3csdj2xix+EWFkweyefeMp8zZo0tdmldyiu4zexcYGbu+u5+Z/jv2yKpTAY8BbYUi7vzyIYD3PGXTWzc38gJtdX8+98s5PUnRH/yTH/IZz7unwDHA88B2fOJHbgzurJkIEuk0jQl0iQ1AZQUWPZCBnc8sok1u+qZNnYY/3rVAi6aP5GyGAR2Vj4t7sXAfHeNxZK+UWBLsWQD+3uPbmb1zqNMHFnFZy4/kcsXHlewsx37Uz7B/QJwHLA74lpkgFKXiBRL+8CeUFPFP11axxWnTGZIRfwCOyuf4B4PrDWzp8mZh9vdr4ysKhkQFNhSLB21sD91WR1vWRjvwM7KJ7hvjboIGVg0rE+KJRvY3310Ey/srB9wgZ2Vz1wlfylEIRJv2blEmtoU2FJ47s6Tmw4O+MDOymdUydnAN4ETgSFAOdDk7iMjrk1iIDtbX1MirblEpOCyLexvPJ1g09HnBnxgZ+XTVfIt4DrgFwQjTN4D9P0yxRJrCmwppvZdImOHGre8aR5vWTiJyvKBG9hZeZ2A4+4bzazc3dPAD83sWeDT0ZYmpcjdaW5L09SWQnkthebuPLHpIN9/bDMv7KznuJFDueVN85iZ3Mqpp04pdnkFk09wN5vZEOA5M/sqwbDAgf8nTV4lk3Gak2maFdhSBOmMs2z9Pn70+BZe2tt4LLCzLezVK7YVu8SCyie4300Q1DcD/xuYBrw9yqKkdKTDazq26pqOUgSpdIbfvrCHO5/YyrZDzUwfO5x/ecuJXLrguEHRJdKZfEaVbDWzYcAkd/9CAWqSEpBMZ2hOpGlN6arpUnityTS/fn4Xdz25jT31rdRNrOFLbz2JJXUTKC+Lz6npUclnVMkVwL8TjCiZZWaLgNt0As7AlEilaU6kadNp6VIEja0p/nvlDn7+9DYONydZOHUUn3pTHefMHheLyZ8KJd8TcM4ElgG4+3Nm1uV1KCV+WpNp0hnnSHOy2KXIIHS4qY17lm/nF8/soDGR4pzZ47jh3BmcOn1MsUsrSfkEd9Ldj7b7a6fuzgEgO6SvuS0Ibf1SpdD21rfy06e28T/P7qQtleGCeRO44dwZzDtOp4l0JZ/gXmNm7wDKzWwO8FHg8WjLkihlMuEYbI0QkSLZdqiZu57cym9W7cYdLjvpON5zzgxmjh9R7NJiIZ/g/nvgMwQTTP0MeAj41yiLkmikM05zW4oWjRCRInlxdz13PbmVP6/bR0VZGVefOoV3njWdyaOHFbu0WMknuOeHt4rwdhVwJbAwwrqkH7WlMrS0aYSIFEfGncdfPshPn9zKym1HGFFVzjvPmsH1Z05jXHVVscuLpXyC+6fAJwnm5dZQgxhpDfuvdeECKYa2VIbfrdnDz57axuYDTUwcWcXHLpzDlYsmU12l65T3RT57b7+7/7o3Gzezy4BvEExM9T13/3K75z8OfABIAfuB97n71t68lwSy/dfNbZpDRIqjviXJfc/u5N7l2znY1MacCdV84coFXHTiBCoG8Ukz/Smf4P68mX0P+BOvvpDCfV29yMzKgduBi4EdwHIzW+rua3NWexZY7O7NZvZh4KvAtT38GQSd4SjFt+tIC3cv387S53bRkkxz9uyx3HrWDM6YOUZjsPtZPsH9XmAeUMkrXSUOdBncBGO/N7r7JgAzu5ugf/xYcLv7wznrPwm8K7+yJastlaG5LUUipe4QKY7cA45mxqULJvKOs6YzZ0JNsUsbsKy7awCb2Xp3r+vxhs2uAS5z9w+Ej98NnOXuN3ey/reAPe7+bx08dxNwE0Btbe3p9957b0/LKZrGxkaqq6v7fbvuwUGf/mxdtzQ1MmxE/9caFdUbne5qzbjzwoEMD21Jsv5whmEVcP7UCi6cXsHYoYXvDonTvgW4+MI3PuPui3v7+nxa3I+b2fx2XRz9yszeRTDX9xs6et7d7wDuAKirq/MlS5ZEVUq/W7ZsGf1Vb9T916tXPMHJi8/p9+1GRfVGp7NaE6k0D63ZGx5wbGFCTRUfvXAaVy2aUtQDjnHat/0hnz19NsGUrpsJ+rgNcHfvbjjgToKZBLOmhstexcwuIhgn/gZ3T7R/Xl4ZzpdIqf9aimNfQyv3PbOT+5/dyZGWJHMmVHPrlfO5+MSJOuBYBPkE92W93PZyYE44r8lOgqvovCN3BTM7Ffh/BF0q+3r5PgNS9hqOzW26SroUz+qdR7ln+Xb+vG4fmYxz3tzxXLt4GqfP0AHHYsprWtfebNjdU2Z2M8GZluXAD9x9jZndBqxw96XA/wdUA78IPwTbBvusg8fObkymdTq6FEUyneHJ3Sm+/qPlrNlVT3VVBdcunsY1p09lyhid4VgKIu2UcvcHgQfbLftczv2Lonz/OEmk0mF3iEaHSHEcbExw/7M7uW/lTg42tTF9bAWfvGQub144ieFDdMJMKdFvo4gyGac19crsfCLFsG5PPfcu38Hv1+4hmXbOmT2Os0Y3cu2lZ1Om7pCSpOAugmQ6Q3NbmkRSBxulOFKZDH9Zv597lm/n+R1HGVZZzlWLpvC/Fk9lxrgRrF7xhEK7hCm4C8TdSaQymjtEiupwUxu/XrWLXz6zg731CSaPHsrHLpzDFadMomZoZbHLkzwpuAugvjVJqw42SpG4O8/vOMr9K3fyp3V7Saad02eM4ROX1PH6E8brGo4xpOCOQHYoX0syTSrjtLRpOlUpvMZEit+9sIf7V+5k4/5GRlSVc/WiKbzttCnMro3PWYbyWgrufpRMB2Gt1rUU00t7G7hv5U4eWrOH5rY0dRNr+PSb5nHpguMYNqS82OVJP1Bw91Fu61p911IsiVSaP724j/tW7mT1zqNUVZRx0fyJvP20KcyfNFInywwwCu5eOta61jSqUkTbDzVz/7M7eWDVbo62JJk+djj/cNEcLj95EqOG6WDjQKXg7gGdhi6lIJXJ8NiGA/z3yp08vfkQ5WacP3c8bz9tKos19/WgoODOQyKVprUto0mepKh2HWlh6fO7eGDVbvY3JKitqeJvz5vFVYumUFujazcOJgruTqSOHWjM6BJgUjSJVJpl6/fz6+d3sXzLYQw4e/Y4/vGSOl43ZxwVZZqZbzBScOfQgUYpFS/tbeDXz+/idy/sob41xaRRQ7np/Nm8ZeEkJo4cWuzypMgU3KgrREpDY2uK36/dw6+e28W6PQ1UlhtL6iZw5SmTWTxzjE5Bl2MGbXCrK0RKgbvz7LYjLH1+F39et49EKsMJE6r5xMVzufSk4zQyRDo0qII7kwnmC1FXiBTbgcYEv1m1m1+v2sX2Qy2MqCrnzSdP4spFk5l3XI1GhkiXBnxwZyd3ak2maUtl1BUiRZPMOMvW7+OBVbt5fONB0u6cOm0073vdLN44bwJDK3VWo+RnwAZ3IhV0gyRSOv1cisfdeWFXPb9dvZvfrW6hKbmacSOG8M6zp3PFwslMHze82CVKDA2o4M6ezZhQv7UU2a4jLfz2hT389oXdbD/UQlVFGYvGl/OOJSezeOYYDeOTPol9cKfSGVrDrhBdRUaKqbE1xZ/W7eW3q/fw7PYjAJw+Yww3nDOTC+ZNYPPq5Zw8e1xxi5QBIZbBnb3kV0tbWqeeS1GlMhme2nSIB1fv5tENB0ikMswYO5wPv+F4Lj1pIpNG6eK60v9iGdz7GxPFLkEGMXfnpb2NPLh6Nw+t2cPh5iSjhlVy5SmTufzkSZw4SaNCJFqxC261r6VYdh1p4Q9r9/LQmj28vL+JynLj9SeM5/KTJ3HO8eOoLFe/tRRG7IJbpJD2NyT444t7+cPavazZVQ/Awqmj+NRldVx44kSdICNFoeAWaedIcxt/XrePP6zdy7PbjuBA3cQabr7gBC6aP0H91lJ0Cm4RguszPvLSfn6/di9Pbz5EOuPMGDucD5w3i4vnT2TGuBHFLlHkGAW3DFqtyTR/3XiA36/Zy+MvH6QtnWHSqKG886zpXDx/InMmVOsgo5QkBbcMKsl0MHzv92v38MhLB2hJphk3YghvPW0KF8+fyEmTdX1GKX0KbhnwWpNpntx0kIfX7efRjftpSqQZOayCSxdM5OL5Ezl1+hjKyxTWEh8KbhmQmhIp/rrxAA+v38/jLx+gNZlh5LAKLqibwBvnTeDMWWM1fE9iS8EtA0Z9S5JHNxzg4fX7eGrTIdrSGcaOGMLlJ03ignkTOG36aCoU1jIAKLgl1g41tfGXl/bz8Lp9rNh6mHTGmTiyireeNoUL6mpZOHW0ukFkwFFwS+zsa2hl2br9/HpFKxv/8CgZh6ljhvHOs6ZzQd0EnXIuA56CW0qeu/Py/iYe23CARzbsP3YG4+QRxntfN4sL5tVyQq2G7sngoeCWkpRMZ3hu2xEe2bCfRzccYPfRVgAWTB7Jh94wmwvqJtCwZRUnL55d5EpFCk/BLSWjviXJE5sO8shL+3li00GaEmmqKso4Y+ZYbjx3Jq+fM57x1VXH1l+9pXi1ihSTgluKasfhZh7dcIBHNxzguW1HSLszZnglF86byHlzxnPmrLG6FqNIOwpuKah0xlm7q/5YF8jmA00AHF87gnedM53z5tSyYPJIytRfLdIpBbdE7nBTG09tPsQTLx/kyU0HOdKSpNyMU6eP5upFkzlvTi1TxmjGPZF8Kbil36Uzztrd9Tzx8kGeePkgL+6ux4HRwyo5e/Y4zj1hHOfMHsdIzWUt0isKbukXh5raeHJTENRPbT7E0ZYkBiyYMpIPnDeLc48fz7xJNeoCEekHCm7plXTGWbPrKI+H3R8v7m4AYMzwSl53wjjOnj2Os2eNY9RwtapF+lukwW1mlwHfAMqB77n7l9s9fz7wn8BC4Dp3/2WU9Ujf7K1vZfmWoK/66c2HqG9NUWZw0pRRfPD82Zxz/DjqjlOrWiRqkQW3mZUDtwMXAzuA5Wa21N3X5qy2DbgR+GRUdUjvNbameGbbYZZvPsTyLYfYcrAZgHEjhnDe3FrOnT2OM2aN1XUXRQosyhb3mcBGd98EYGZ3A1cBx4Lb3beEz2UirEPylMw4K7ce5uktQVCv3VVPxmFoZRmnTh/DVYumcMasMRxfW61WtUgRRRncU4DtOY93AGdF+H7SQxl3Nu5rZPmWQyzffJhntrTQlllJuRnzJ4/kxnNncuassZw0ZZTmrhYpIbE4OGlmNwE3AdTW1rJ6xRNFrih/LU2NJVXvwZYMaw9mePFQmhcPpmlIBssnjTDOmeicPLGKuWPKGV6ZBPbBvn2s21fUkjtVavu2O3GqN061Qvzq7asog3snMC3n8dRwWY+5+x3AHQBz6+r85MXn9L26Alm94gmKVa+7s/toK89sPcyz246wctvhY5M1ja8ewuvrajlz1lgWzxzDhJqhRa21N1RvdOJUK8Sv3r6KMriXA3PMbBZBYF8HvCPC9xv03J2dR1pYuTUI6ZXbDrO3PgEEJ78smj6a68+czhkzxzBr/AhNgyoSU5EFt7unzOxm4CGC4YA/cPc1ZnYbsMLdl5rZGcD9wBjgCjP7grsviKqmgcbd2X6o5VhIr9x2hP0NQVCPGV7JadPH8O6zR3P6jDHMHD9CBxRFBohI+7jd/UHgwXbLPpdzfzlBF4rkwd3ZerD5WEg/u+0wBxrbgGCI3qnTR3Pa9DGcNmMMM8cNV4taZICKxcHJwSqRSvPi7gZW7TjCqh1HWbXjKEdbgqOJtdVVx0L6tOmjmT5WQS0yWCi4S8jhpjZW7TjK82FQr9tTTzLtAEwfO5zz5oznlKmjOXX6aKaOGaagFhmkFNxF4u5sOdjMqh1HeH7HUVbtOML2Qy0AVJYbJ04aybVnTGPh1NEsnDKKMSOGFLliESkVCu4CaUqkWLengRd2Bl0eq3Yeob4lBcCoYZUsnDqKq06ZwsKpo5g3qYaqCl31RUQ6puCOQDrjbDrQyJqd9Ty6JsHuZ59k84EmMkGvBzPGDucNc2tZOHU0p0wdpf5pEekRBXc/2Fvfyppd9azZdZQ1O+tZt6eBlmQagBGVsHDaUC6om8CCKSOZP2kko4er20NEek/B3UNNiRQv7q4Pg7qetbvq2d8YjJ2uLDfmTqzhilMmsWDyKOZPHsmRl59j4RmLilu0iAwoCu4uNCVSvLS3gXV7wtvuerYebCbs8WDqmGGcPmMMCyaPZMGUkcyZUMOQildPxnR0k7pARKR/KbhDja0p1u9tYN2eetbvaWDd7ga2HXolpGurq5g3qYaLTpzIgikjWTBplK7uIiI9UmbGsCF9H3gwKIO7oTXJ+j0NvBi2otfvbTg2FA9gQk0Q0pcsmMi8SSM58bgaxlVXFbFiEYkrA6oqyhk6pKzfRosN6OB2d/bUt7JhbyMb9jWyYW8DL+1tZOeRV0L6uJFDmXdcDZefNIl5k2qYd9xIxmrMtIj0UUVZ0LoeWlFOWVn/dpkOmOBuTabZfKApDOkGNuxtZOP+RhpaU8fWmTpmGHMmVnPlKZOZN6mGuok1OrFFRPqNGQytLGdYZXmkFx+JZXDvb0gcC+dsS3r7oRbSHvRID6ss5/gJI7joxInMmVDNnInVHF9bzYiqWP64IlLihpSXMWxIOVUVZQU5JyN2Sba9IcNbvvnYscfHjRzKnInVXDBvQhjSNUwdM0xTmIpIpLIHGodVllPez10h3YldcA+vMD5+8VzmTKjmhAnVjNQVxkWkQKI40NgbsQvuccOMa8+Y1v2KIiL9pKqijKGVhesK6U7sgltEpBAqy8sYWlkWyaiQvlJwi4iEysuMYZXlDC1Cv3VPKLhFZFArMwta1hEP4etPCm4RGXSy462HVpS/Zn6hOFBwi8igkA3rqooyhpSXxkHG3lJwi8iAVWZGVXiAMY4t684ouEVkQCkvs2PD9+LSZ91TCm4RiT0DRlRVMLSijIoBGta5FNwiEkuV5WVUVQS38jKjehDNRTR4flIRiTUDhlSUMaQiON28lMdZR03BLSIlq7zMwqCO/0iQ/qTgFpGSYQRdINmwHgz91b2h4BaRoiqzV1rVpTKJU6lTcItIweW2qgfqkL0oKbhFJHLZvuoh5cGt1GbbixsFt4j0u9zuj8ryskE9AiQKCm4R6TMzqCovp7LCGFKug4pRU3CLSI9lx1Rn+6rVT11YCm4R6ZZZcCXzymM30+iPIlJwi0iHhlaWh2Ft6vooMQpuEaGizKjMGfVRUWaMGlZZ7LKkEwpukUEme3ZiZUXQmtap5PGj4BYZwIxgDHVlRRmVZer2GCgU3CIDSHmZHTt4WBl2eag1PfAouEViqrzMqCwro6LcNNJjkFFwi5S4bHdHRRjSFeVBYOu08cEr0uA2s8uAbwDlwPfc/cvtnq8C7gROBw4C17r7lihrEillZhxrRR8LanV3SDuRBbeZlQO3AxcDO4DlZrbU3dfmrPZ+4LC7n2Bm1wFfAa6NqiaRUqFWtPRFlC3uM4GN7r4JwMzuBq4CcoP7KuDW8P4vgW+Zmbm7R1iXSMHkBnSZBWOjg8dqRUvvRRncU4DtOY93AGd1to67p8zsKDAOOJC7kpndBNwEUFtby+oVT0RVc79raWqMTb1xqhVKr14L/2MYRtDtkau5qZEn//poESrrucbGRpYtW1bsMvIWt3r7KhYHJ939DuAOgLl1dX7y4nOKXFH+Vq94grjUG6daoTj1llnQWi4P+56zIzvy6eJYtmwZS5Ysib7IfhCnWiF+9fZVlMG9E5iW83hquKyjdXaYWQUwiuAgpUhRGFBW9kooH7tZ8K+6N6QURBncy4E5ZjaLIKCvA97Rbp2lwA3AE8A1wJ/Vvy1RM4NyC/qdy8tfCeXsTaTURRbcYZ/1zcBDBMMBf+Dua8zsNmCFuy8Fvg/8xMw2AocIwl2k14K+5VdayWVlqNUsA06kfdzu/iDwYLtln8u53wr8TZQ1yMCSbS2XlxllBtVVFeF9tZhl8IjFwUkZ+MosCOJsizgbzGXH7r82lMvMGFGlj7AMPvrUSySyXRbZ8C0zw8rCIDYLWs7HujPUShbpCQW35MVyA5hg5MWrQtle22oWkWgouAeJ7Mkhx4I3pxWcfVwentmXG9JlhkJYpMQouEvUq87Cs1fC1cJlQQgHofqq53Jek9s6zus9LbjOoIiUNgV3F6zdnWwgZhdlQ9NynudYeAbKzKiuqnjVOu0Dtv221MIVka7ELrgNqBlaEc4GES7rJOdyl2fXzw3e4LEde3zsuX4MzjJDIx9EpF/FMlGGD4ll2SIi/UJXDRURiRmL29QgZtYArC92HT0wnnbT1JawONUKqjdKcaoV4ldvnbvX9PbFcexzWO/ui4tdRL7MbEVc6o1TraB6oxSnWiGe9fbl9eoqERGJGQW3iEjMxDG47yh2AT0Up3rjVCuo3ijFqVYYZPXG7uCkiMhgF8cWt4jIoBar4Dazy8xsvZltNLNbil1Pe2a2xcxWm9lz2aPGZjbWzP5gZhvCf8cUsb4fmNk+M3shZ1mH9Vng/4b7epWZnVYi9d5qZjvDffycmV2e89ynw3rXm9mlBa51mpk9bGZrzWyNmX0sXF6S+7eLektu/5rZUDN72syeD2v9Qrh8lpk9FdZ0j5kNCZdXhY83hs/PLFSt3dT7IzPbnLNvF4XLe/5ZcPdY3Aguf/YyMBsYAjwPzC92Xe1q3AKMb7fsq8At4f1bgK8Usb7zgdOAF7qrD7gc+C3BbABnA0+VSL23Ap/sYN354WeiCpgVflbKC1jrJOC08H4N8FJYU0nu3y7qLbn9G+6j6vB+JfBUuM/uBa4Ll38H+HB4/++A74T3rwPuKfC+7azeHwHXdLB+jz8LcWpxnwlsdPdN7t4G3A1cVeSa8nEV8OPw/o+Bq4tViLs/QnBtz1yd1XcVcKcHngRGm9mkghQa6qTezlwF3O3uCXffDGwk+MwUhLvvdveV4f0G4EVgCiW6f7uotzNF27/hPmoMH1aGNwfeCPwyXN5+32b3+S+BC62AM7d1UW9nevxZiFNwTwG25zzeQdcftGJw4Pdm9oyZ3RQum+juu8P7e4CJxSmtU53VV8r7++bwK+UPcrqeSqbe8Kv5qQQtrZLfv+3qhRLcv2ZWbmbPAfuAPxC0+I+4e6qDeo7VGj5/FBhXqFo7qtfds/v2i+G+/bqZVbWvN9Ttvo1TcMfB6939NOBNwEfM7PzcJz34XlSyw3hKvb7QfwHHA4uA3cB/FLWadsysGvhv4B/cvT73uVLcvx3UW5L7193T7r4ImErQ0p9X3Iq61r5eMzsJ+DRB3WcAY4FP9Xb7cQruncC0nMdTw2Ulw913hv/uA+4n+IDtzX7tCf/dV7wKO9RZfSW5v919b/g/RQb4Lq98XS96vWZWSRCCP3X3+8LFJbt/O6q3lPdvWN8R4GHgHIIuhey0Hbn1HKs1fH4UcLCwlQZy6r0s7J5yd08AP6QP+zZOwb0cmBMeSR5CcNBhaZFrOsbMRphZTfY+cAnwAkGNN4Sr3QD8qjgVdqqz+pYC7wmPeJ8NHM35yl807fr+3kqwjyGo97pwRMEsYA7wdAHrMuD7wIvu/rWcp0py/3ZWbynuXzOrNbPR4f1hwMUEffIPA9eEq7Xft9l9fg3w5/DbTkF0Uu+6nD/gRtAfn7tve/ZZKOTR1r7eCI6+vkTQv/WZYtfTrrbZBEfdnwfWZOsj6Fv7E7AB+CMwtog1/pzg62+SoB/t/Z3VR3CE+/ZwX68GFpdIvT8J61kVfuAn5az/mbDe9cCbClzr6wm6QVYBz4W3y0t1/3ZRb8ntX2Ah8GxY0wvA58Llswn+eGwEfgFUhcuHho83hs/PLvC+7azeP4f79gXgLl4ZedLjz4LOnBQRiZk4dZWIiAgKbhGR2FFwi4jEjIJbRCRmFNwiIjGj4BYRiRkFtwwI4XSknyzwez5eyPcTyVJwi/SSu59b7BpkcFJwS2yZ2WfM7CUzewyoC5f9rZktDyex/28zG25mNeEE9pXhOiNzH3ew3WXh7G0rzOxFMzvDzO6z4GII/5azXmP475LwNb80s3Vm9tPsNKJm9mULLlawysz+PfKdIoOCgltiycxOJ5ivZhHBqdpnhE/d5+5nuPspBPNZvN+D+aaXAW8O17kuXC/ZxVu0uftiggn6fwV8BDgJuNHMOpoi9FTgHwguODAbeF243luBBe6+EPi3Dl4n0mMKbomr84D73b3Zg+lIsxOOnWRmj5rZauCdwIJw+feA94b330swO1tXsttbDazxYGa3BLCJV8/klvW0u+/wYFa954CZBPNAtwLfN7O3Ac09/BlFOqTgloHmR8DN7n4y8AWCCYdw978CM81sCcElt17obAOhRPhvJud+9nHFa1d/1TppoMKDSfzPJLgKy1uA3/XkBxHpjIJb4uoR4GozGxZOp3tFuLwG2B32X7+z3WvuBH5G963tfhFepGCUuz8I/G/glEK8rwx8HbUcREqeu680s3sIptHdRzBfO8C/EFyCa3/4b03Oy35K0M/88wKVWQP8ysyGEkzd+fECva8McJrWVQYNM7sGuMrd313sWkT6Qi1uGRTM7JsE1wK9vNi1iPSVWtwyaJnZ7cDr2i3+hrsXpA9cpLcU3CIiMaNRJSIiMaPgFhGJGQW3iEjMKLhFRGJGwS0iEjP/P+oK44LXfclnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "glm_fit1.partial_plot(data=train, cols=['Day Mins'], figsize=(5, 5));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AutoML\n",
    "\n",
    "We will now automate the machine learning process using AutoML and the `explain` function.  AutoML will automatically tune models and try out different algorithms for the user and provide a leaderboard. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AutoML progress: |\n",
      "00:39:30.975: AutoML: XGBoost is not available; skipping it.\n",
      "00:39:31.55: Step 'best_of_family_xgboost' not defined in provider 'StackedEnsemble': skipping it.\n",
      "00:39:31.55: Step 'all_xgboost' not defined in provider 'StackedEnsemble': skipping it.\n",
      "\n",
      "███████████████████████████████████████████████████████████████| (done) 100%\n"
     ]
    }
   ],
   "source": [
    "from h2o.automl import H2OAutoML\n",
    "\n",
    "aml = H2OAutoML(## train up to 10 models\n",
    "                max_models=10, \n",
    "    \n",
    "                ## set seed for reproducibility\n",
    "                seed=1234,\n",
    "                \n",
    "                ## option to exclude specific algorithms\n",
    "                exclude_algos=['StackedEnsemble', 'DeepLearning']\n",
    "                )\n",
    "aml.train(x=x, y=y, training_frame=train, leaderboard_frame=valid);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The leaderboard belows shows us the AUC of each model on our `valid` data.  The best model is a Random Forest model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>model_id                                  </th><th style=\"text-align: right;\">     auc</th><th style=\"text-align: right;\">  logloss</th><th style=\"text-align: right;\">   aucpr</th><th style=\"text-align: right;\">  mean_per_class_error</th><th style=\"text-align: right;\">    rmse</th><th style=\"text-align: right;\">      mse</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>GBM_2_AutoML_1_20220706_03930             </td><td style=\"text-align: right;\">0.899849</td><td style=\"text-align: right;\"> 0.183781</td><td style=\"text-align: right;\">0.818355</td><td style=\"text-align: right;\">              0.143308</td><td style=\"text-align: right;\">0.211693</td><td style=\"text-align: right;\">0.044814 </td></tr>\n",
       "<tr><td>GBM_grid_1_AutoML_1_20220706_03930_model_2</td><td style=\"text-align: right;\">0.895149</td><td style=\"text-align: right;\"> 0.199008</td><td style=\"text-align: right;\">0.80031 </td><td style=\"text-align: right;\">              0.13447 </td><td style=\"text-align: right;\">0.22478 </td><td style=\"text-align: right;\">0.0505258</td></tr>\n",
       "<tr><td>GBM_5_AutoML_1_20220706_03930             </td><td style=\"text-align: right;\">0.894465</td><td style=\"text-align: right;\"> 0.185459</td><td style=\"text-align: right;\">0.800027</td><td style=\"text-align: right;\">              0.148569</td><td style=\"text-align: right;\">0.211678</td><td style=\"text-align: right;\">0.0448074</td></tr>\n",
       "<tr><td>DRF_1_AutoML_1_20220706_03930             </td><td style=\"text-align: right;\">0.887819</td><td style=\"text-align: right;\"> 0.44122 </td><td style=\"text-align: right;\">0.837511</td><td style=\"text-align: right;\">              0.09375 </td><td style=\"text-align: right;\">0.217224</td><td style=\"text-align: right;\">0.0471862</td></tr>\n",
       "<tr><td>GBM_3_AutoML_1_20220706_03930             </td><td style=\"text-align: right;\">0.887276</td><td style=\"text-align: right;\"> 0.18667 </td><td style=\"text-align: right;\">0.812002</td><td style=\"text-align: right;\">              0.135732</td><td style=\"text-align: right;\">0.208377</td><td style=\"text-align: right;\">0.0434209</td></tr>\n",
       "<tr><td>GBM_grid_1_AutoML_1_20220706_03930_model_1</td><td style=\"text-align: right;\">0.883838</td><td style=\"text-align: right;\"> 0.178955</td><td style=\"text-align: right;\">0.828886</td><td style=\"text-align: right;\">              0.118266</td><td style=\"text-align: right;\">0.1988  </td><td style=\"text-align: right;\">0.0395214</td></tr>\n",
       "<tr><td>XRT_1_AutoML_1_20220706_03930             </td><td style=\"text-align: right;\">0.879787</td><td style=\"text-align: right;\"> 0.290344</td><td style=\"text-align: right;\">0.807797</td><td style=\"text-align: right;\">              0.123422</td><td style=\"text-align: right;\">0.237825</td><td style=\"text-align: right;\">0.0565606</td></tr>\n",
       "<tr><td>GBM_4_AutoML_1_20220706_03930             </td><td style=\"text-align: right;\">0.879577</td><td style=\"text-align: right;\"> 0.19549 </td><td style=\"text-align: right;\">0.813502</td><td style=\"text-align: right;\">              0.131629</td><td style=\"text-align: right;\">0.212793</td><td style=\"text-align: right;\">0.0452811</td></tr>\n",
       "<tr><td>GBM_1_AutoML_1_20220706_03930             </td><td style=\"text-align: right;\">0.873159</td><td style=\"text-align: right;\"> 0.245236</td><td style=\"text-align: right;\">0.713209</td><td style=\"text-align: right;\">              0.137311</td><td style=\"text-align: right;\">0.257353</td><td style=\"text-align: right;\">0.0662308</td></tr>\n",
       "<tr><td>GLM_1_AutoML_1_20220706_03930             </td><td style=\"text-align: right;\">0.817796</td><td style=\"text-align: right;\"> 0.309194</td><td style=\"text-align: right;\">0.459268</td><td style=\"text-align: right;\">              0.261258</td><td style=\"text-align: right;\">0.302586</td><td style=\"text-align: right;\">0.0915581</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aml.leaderboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Details\n",
      "=============\n",
      "H2OGradientBoostingEstimator :  Gradient Boosting Machine\n",
      "Model Key:  GBM_2_AutoML_1_20220706_03930\n",
      "\n",
      "\n",
      "Model Summary: \n"
     ]
    },
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>number_of_trees</th>\n",
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       "      <th>model_size_in_bytes</th>\n",
       "      <th>min_depth</th>\n",
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       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>46.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>33715.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>59.0</td>\n",
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       "     number_of_trees  number_of_internal_trees  model_size_in_bytes  \\\n",
       "0               46.0                      46.0              33715.0   \n",
       "\n",
       "   min_depth  max_depth  mean_depth  min_leaves  max_leaves  mean_leaves  \n",
       "0        7.0        7.0         7.0        27.0        59.0    42.565216  "
      ]
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     "text": [
      "\n",
      "\n",
      "ModelMetricsBinomial: gbm\n",
      "** Reported on train data. **\n",
      "\n",
      "MSE: 0.00867598318329953\n",
      "RMSE: 0.09314495790594104\n",
      "LogLoss: 0.04922249797381245\n",
      "Mean Per-Class Error: 0.004763876199177707\n",
      "AUC: 0.9999393273184102\n",
      "AUCPR: 0.9996675130515511\n",
      "Gini: 0.9998786546368204\n",
      "\n",
      "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.22615901479764813: \n"
     ]
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>False.</th>\n",
       "      <th>True.</th>\n",
       "      <th>Error</th>\n",
       "      <th>Rate</th>\n",
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       "      <th>0</th>\n",
       "      <td>False.</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.001</td>\n",
       "      <td>(2.0/1990.0)</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True.</td>\n",
       "      <td>3.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>(3.0/352.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Total</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>0.0021</td>\n",
       "      <td>(5.0/2342.0)</td>\n",
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       "           False.  True.   Error           Rate\n",
       "0  False.  1988.0    2.0   0.001   (2.0/1990.0)\n",
       "1   True.     3.0  349.0  0.0085    (3.0/352.0)\n",
       "2   Total  1991.0  351.0  0.0021   (5.0/2342.0)"
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     "text": [
      "\n",
      "Maximum Metrics: Maximum metrics at their respective thresholds\n"
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       "      <th>metric</th>\n",
       "      <th>threshold</th>\n",
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       "      <th>idx</th>\n",
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       "      <th>0</th>\n",
       "      <td>max f1</td>\n",
       "      <td>0.226159</td>\n",
       "      <td>0.992888</td>\n",
       "      <td>180.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>max f2</td>\n",
       "      <td>0.190623</td>\n",
       "      <td>0.992647</td>\n",
       "      <td>188.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>max f0point5</td>\n",
       "      <td>0.245942</td>\n",
       "      <td>0.995423</td>\n",
       "      <td>178.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>max accuracy</td>\n",
       "      <td>0.245942</td>\n",
       "      <td>0.997865</td>\n",
       "      <td>178.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>max precision</td>\n",
       "      <td>0.989105</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>max recall</td>\n",
       "      <td>0.143025</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>max specificity</td>\n",
       "      <td>0.989105</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>max absolute_mcc</td>\n",
       "      <td>0.226159</td>\n",
       "      <td>0.991633</td>\n",
       "      <td>180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>max min_per_class_accuracy</td>\n",
       "      <td>0.190623</td>\n",
       "      <td>0.995477</td>\n",
       "      <td>188.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>max mean_per_class_accuracy</td>\n",
       "      <td>0.190623</td>\n",
       "      <td>0.996318</td>\n",
       "      <td>188.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>max tns</td>\n",
       "      <td>0.989105</td>\n",
       "      <td>1990.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>max fns</td>\n",
       "      <td>0.989105</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>max fps</td>\n",
       "      <td>0.003916</td>\n",
       "      <td>1990.000000</td>\n",
       "      <td>399.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>max tps</td>\n",
       "      <td>0.143025</td>\n",
       "      <td>352.000000</td>\n",
       "      <td>198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>max tnr</td>\n",
       "      <td>0.989105</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>max fnr</td>\n",
       "      <td>0.989105</td>\n",
       "      <td>0.994318</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>max fpr</td>\n",
       "      <td>0.003916</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>399.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>max tpr</td>\n",
       "      <td>0.143025</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>198.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         metric  threshold        value    idx\n",
       "0                        max f1   0.226159     0.992888  180.0\n",
       "1                        max f2   0.190623     0.992647  188.0\n",
       "2                  max f0point5   0.245942     0.995423  178.0\n",
       "3                  max accuracy   0.245942     0.997865  178.0\n",
       "4                 max precision   0.989105     1.000000    0.0\n",
       "5                    max recall   0.143025     1.000000  198.0\n",
       "6               max specificity   0.989105     1.000000    0.0\n",
       "7              max absolute_mcc   0.226159     0.991633  180.0\n",
       "8    max min_per_class_accuracy   0.190623     0.995477  188.0\n",
       "9   max mean_per_class_accuracy   0.190623     0.996318  188.0\n",
       "10                      max tns   0.989105  1990.000000    0.0\n",
       "11                      max fns   0.989105   350.000000    0.0\n",
       "12                      max fps   0.003916  1990.000000  399.0\n",
       "13                      max tps   0.143025   352.000000  198.0\n",
       "14                      max tnr   0.989105     1.000000    0.0\n",
       "15                      max fnr   0.989105     0.994318    0.0\n",
       "16                      max fpr   0.003916     1.000000  399.0\n",
       "17                      max tpr   0.143025     1.000000  198.0"
      ]
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     "output_type": "stream",
     "text": [
      "\n",
      "Gains/Lift Table: Avg response rate: 15.03 %, avg score: 14.95 %\n"
     ]
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       "      <td>1.000000</td>\n",
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       "      <td>565.340909</td>\n",
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       "      <td>6.653409</td>\n",
       "      <td>6.653409</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.970444</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.975759</td>\n",
       "      <td>0.065341</td>\n",
       "      <td>0.133523</td>\n",
       "      <td>565.340909</td>\n",
       "      <td>565.340909</td>\n",
       "      <td>0.133523</td>\n",
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       "      <td>0.961086</td>\n",
       "      <td>6.653409</td>\n",
       "      <td>6.653409</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.963300</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.971547</td>\n",
       "      <td>0.068182</td>\n",
       "      <td>0.201705</td>\n",
       "      <td>565.340909</td>\n",
       "      <td>565.340909</td>\n",
       "      <td>0.201705</td>\n",
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       "      <td>0.955616</td>\n",
       "      <td>6.653409</td>\n",
       "      <td>6.653409</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.958420</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.968335</td>\n",
       "      <td>0.065341</td>\n",
       "      <td>0.267045</td>\n",
       "      <td>565.340909</td>\n",
       "      <td>565.340909</td>\n",
       "      <td>0.267045</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.050384</td>\n",
       "      <td>0.949942</td>\n",
       "      <td>6.653409</td>\n",
       "      <td>6.653409</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.953596</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.965338</td>\n",
       "      <td>0.068182</td>\n",
       "      <td>0.335227</td>\n",
       "      <td>565.340909</td>\n",
       "      <td>565.340909</td>\n",
       "      <td>0.335227</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.100342</td>\n",
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       "      <td>6.653409</td>\n",
       "      <td>6.653409</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.924856</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.945183</td>\n",
       "      <td>0.332386</td>\n",
       "      <td>0.667614</td>\n",
       "      <td>565.340909</td>\n",
       "      <td>565.340909</td>\n",
       "      <td>0.667614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.150299</td>\n",
       "      <td>0.216799</td>\n",
       "      <td>6.482809</td>\n",
       "      <td>6.596704</td>\n",
       "      <td>0.974359</td>\n",
       "      <td>0.684568</td>\n",
       "      <td>0.991477</td>\n",
       "      <td>0.858558</td>\n",
       "      <td>0.323864</td>\n",
       "      <td>0.991477</td>\n",
       "      <td>548.280886</td>\n",
       "      <td>559.670390</td>\n",
       "      <td>0.989970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.200256</td>\n",
       "      <td>0.065734</td>\n",
       "      <td>0.170600</td>\n",
       "      <td>4.993603</td>\n",
       "      <td>0.025641</td>\n",
       "      <td>0.109269</td>\n",
       "      <td>0.750533</td>\n",
       "      <td>0.671635</td>\n",
       "      <td>0.008523</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-82.939977</td>\n",
       "      <td>399.360341</td>\n",
       "      <td>0.941206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.300171</td>\n",
       "      <td>0.035032</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.331437</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.046804</td>\n",
       "      <td>0.500711</td>\n",
       "      <td>0.463654</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>233.143670</td>\n",
       "      <td>0.823618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0.400085</td>\n",
       "      <td>0.024018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.499466</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028931</td>\n",
       "      <td>0.375667</td>\n",
       "      <td>0.355090</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>149.946638</td>\n",
       "      <td>0.706030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.018385</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021206</td>\n",
       "      <td>0.300598</td>\n",
       "      <td>0.288370</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.588442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>0.599915</td>\n",
       "      <td>0.014239</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.666904</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016219</td>\n",
       "      <td>0.250534</td>\n",
       "      <td>0.243044</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>66.690391</td>\n",
       "      <td>0.470854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0.699829</td>\n",
       "      <td>0.011523</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.428920</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012792</td>\n",
       "      <td>0.214765</td>\n",
       "      <td>0.210171</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>42.892007</td>\n",
       "      <td>0.353266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0.799744</td>\n",
       "      <td>0.009226</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.250400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010412</td>\n",
       "      <td>0.187934</td>\n",
       "      <td>0.185214</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>25.040043</td>\n",
       "      <td>0.235678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0.899658</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.111533</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008203</td>\n",
       "      <td>0.167062</td>\n",
       "      <td>0.165556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>11.153299</td>\n",
       "      <td>0.118090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003743</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005984</td>\n",
       "      <td>0.150299</td>\n",
       "      <td>0.149544</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    group  cumulative_data_fraction  lower_threshold      lift  \\\n",
       "0       1                  0.010248         0.975916  6.653409   \n",
       "1       2                  0.020068         0.966366  6.653409   \n",
       "2       3                  0.030316         0.961086  6.653409   \n",
       "3       4                  0.040137         0.955616  6.653409   \n",
       "4       5                  0.050384         0.949942  6.653409   \n",
       "5       6                  0.100342         0.889367  6.653409   \n",
       "6       7                  0.150299         0.216799  6.482809   \n",
       "7       8                  0.200256         0.065734  0.170600   \n",
       "8       9                  0.300171         0.035032  0.000000   \n",
       "9      10                  0.400085         0.024018  0.000000   \n",
       "10     11                  0.500000         0.018385  0.000000   \n",
       "11     12                  0.599915         0.014239  0.000000   \n",
       "12     13                  0.699829         0.011523  0.000000   \n",
       "13     14                  0.799744         0.009226  0.000000   \n",
       "14     15                  0.899658         0.007143  0.000000   \n",
       "15     16                  1.000000         0.003743  0.000000   \n",
       "\n",
       "    cumulative_lift  response_rate     score  cumulative_response_rate  \\\n",
       "0          6.653409       1.000000  0.980852                  1.000000   \n",
       "1          6.653409       1.000000  0.970444                  1.000000   \n",
       "2          6.653409       1.000000  0.963300                  1.000000   \n",
       "3          6.653409       1.000000  0.958420                  1.000000   \n",
       "4          6.653409       1.000000  0.953596                  1.000000   \n",
       "5          6.653409       1.000000  0.924856                  1.000000   \n",
       "6          6.596704       0.974359  0.684568                  0.991477   \n",
       "7          4.993603       0.025641  0.109269                  0.750533   \n",
       "8          3.331437       0.000000  0.046804                  0.500711   \n",
       "9          2.499466       0.000000  0.028931                  0.375667   \n",
       "10         2.000000       0.000000  0.021206                  0.300598   \n",
       "11         1.666904       0.000000  0.016219                  0.250534   \n",
       "12         1.428920       0.000000  0.012792                  0.214765   \n",
       "13         1.250400       0.000000  0.010412                  0.187934   \n",
       "14         1.111533       0.000000  0.008203                  0.167062   \n",
       "15         1.000000       0.000000  0.005984                  0.150299   \n",
       "\n",
       "    cumulative_score  capture_rate  cumulative_capture_rate        gain  \\\n",
       "0           0.980852      0.068182                 0.068182  565.340909   \n",
       "1           0.975759      0.065341                 0.133523  565.340909   \n",
       "2           0.971547      0.068182                 0.201705  565.340909   \n",
       "3           0.968335      0.065341                 0.267045  565.340909   \n",
       "4           0.965338      0.068182                 0.335227  565.340909   \n",
       "5           0.945183      0.332386                 0.667614  565.340909   \n",
       "6           0.858558      0.323864                 0.991477  548.280886   \n",
       "7           0.671635      0.008523                 1.000000  -82.939977   \n",
       "8           0.463654      0.000000                 1.000000 -100.000000   \n",
       "9           0.355090      0.000000                 1.000000 -100.000000   \n",
       "10          0.288370      0.000000                 1.000000 -100.000000   \n",
       "11          0.243044      0.000000                 1.000000 -100.000000   \n",
       "12          0.210171      0.000000                 1.000000 -100.000000   \n",
       "13          0.185214      0.000000                 1.000000 -100.000000   \n",
       "14          0.165556      0.000000                 1.000000 -100.000000   \n",
       "15          0.149544      0.000000                 1.000000 -100.000000   \n",
       "\n",
       "    cumulative_gain  kolmogorov_smirnov  \n",
       "0        565.340909            0.068182  \n",
       "1        565.340909            0.133523  \n",
       "2        565.340909            0.201705  \n",
       "3        565.340909            0.267045  \n",
       "4        565.340909            0.335227  \n",
       "5        565.340909            0.667614  \n",
       "6        559.670390            0.989970  \n",
       "7        399.360341            0.941206  \n",
       "8        233.143670            0.823618  \n",
       "9        149.946638            0.706030  \n",
       "10       100.000000            0.588442  \n",
       "11        66.690391            0.470854  \n",
       "12        42.892007            0.353266  \n",
       "13        25.040043            0.235678  \n",
       "14        11.153299            0.118090  \n",
       "15         0.000000            0.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "ModelMetricsBinomial: gbm\n",
      "** Reported on cross-validation data. **\n",
      "\n",
      "MSE: 0.05539791243792369\n",
      "RMSE: 0.23536761127632597\n",
      "LogLoss: 0.21209361157716017\n",
      "Mean Per-Class Error: 0.14041371630881683\n",
      "AUC: 0.9063256624029238\n",
      "AUCPR: 0.8111767352655737\n",
      "Gini: 0.8126513248058476\n",
      "\n",
      "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.273778010748177: \n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>False.</th>\n",
       "      <th>True.</th>\n",
       "      <th>Error</th>\n",
       "      <th>Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False.</td>\n",
       "      <td>1923.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.0337</td>\n",
       "      <td>(67.0/1990.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True.</td>\n",
       "      <td>87.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>0.2472</td>\n",
       "      <td>(87.0/352.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Total</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>332.0</td>\n",
       "      <td>0.0658</td>\n",
       "      <td>(154.0/2342.0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           False.  True.   Error             Rate\n",
       "0  False.  1923.0   67.0  0.0337    (67.0/1990.0)\n",
       "1   True.    87.0  265.0  0.2472     (87.0/352.0)\n",
       "2   Total  2010.0  332.0  0.0658   (154.0/2342.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Maximum Metrics: Maximum metrics at their respective thresholds\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>metric</th>\n",
       "      <th>threshold</th>\n",
       "      <th>value</th>\n",
       "      <th>idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>max f1</td>\n",
       "      <td>0.273778</td>\n",
       "      <td>0.774854</td>\n",
       "      <td>195.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>max f2</td>\n",
       "      <td>0.171170</td>\n",
       "      <td>0.777840</td>\n",
       "      <td>222.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>max f0point5</td>\n",
       "      <td>0.541599</td>\n",
       "      <td>0.813070</td>\n",
       "      <td>133.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>max accuracy</td>\n",
       "      <td>0.293202</td>\n",
       "      <td>0.934244</td>\n",
       "      <td>188.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>max precision</td>\n",
       "      <td>0.991429</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>max recall</td>\n",
       "      <td>0.003266</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>397.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>max specificity</td>\n",
       "      <td>0.991429</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>max absolute_mcc</td>\n",
       "      <td>0.273778</td>\n",
       "      <td>0.736824</td>\n",
       "      <td>195.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>max min_per_class_accuracy</td>\n",
       "      <td>0.062005</td>\n",
       "      <td>0.843750</td>\n",
       "      <td>289.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>max mean_per_class_accuracy</td>\n",
       "      <td>0.171170</td>\n",
       "      <td>0.869509</td>\n",
       "      <td>222.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>max tns</td>\n",
       "      <td>0.991429</td>\n",
       "      <td>1990.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>max fns</td>\n",
       "      <td>0.991429</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>max fps</td>\n",
       "      <td>0.001907</td>\n",
       "      <td>1990.000000</td>\n",
       "      <td>399.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>max tps</td>\n",
       "      <td>0.003266</td>\n",
       "      <td>352.000000</td>\n",
       "      <td>397.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>max tnr</td>\n",
       "      <td>0.991429</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>max fnr</td>\n",
       "      <td>0.991429</td>\n",
       "      <td>0.994318</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>max fpr</td>\n",
       "      <td>0.001907</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>399.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>max tpr</td>\n",
       "      <td>0.003266</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>397.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         metric  threshold        value    idx\n",
       "0                        max f1   0.273778     0.774854  195.0\n",
       "1                        max f2   0.171170     0.777840  222.0\n",
       "2                  max f0point5   0.541599     0.813070  133.0\n",
       "3                  max accuracy   0.293202     0.934244  188.0\n",
       "4                 max precision   0.991429     1.000000    0.0\n",
       "5                    max recall   0.003266     1.000000  397.0\n",
       "6               max specificity   0.991429     1.000000    0.0\n",
       "7              max absolute_mcc   0.273778     0.736824  195.0\n",
       "8    max min_per_class_accuracy   0.062005     0.843750  289.0\n",
       "9   max mean_per_class_accuracy   0.171170     0.869509  222.0\n",
       "10                      max tns   0.991429  1990.000000    0.0\n",
       "11                      max fns   0.991429   350.000000    0.0\n",
       "12                      max fps   0.001907  1990.000000  399.0\n",
       "13                      max tps   0.003266   352.000000  397.0\n",
       "14                      max tnr   0.991429     1.000000    0.0\n",
       "15                      max fnr   0.991429     0.994318    0.0\n",
       "16                      max fpr   0.001907     1.000000  399.0\n",
       "17                      max tpr   0.003266     1.000000  397.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gains/Lift Table: Avg response rate: 15.03 %, avg score: 12.72 %\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <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>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.010248</td>\n",
       "      <td>0.955422</td>\n",
       "      <td>6.653409</td>\n",
       "      <td>6.653409</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.970636</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.970636</td>\n",
       "      <td>0.068182</td>\n",
       "      <td>0.068182</td>\n",
       "      <td>565.340909</td>\n",
       "      <td>565.340909</td>\n",
       "      <td>0.068182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.020068</td>\n",
       "      <td>0.940095</td>\n",
       "      <td>6.364130</td>\n",
       "      <td>6.511847</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>0.948753</td>\n",
       "      <td>0.978723</td>\n",
       "      <td>0.959927</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.130682</td>\n",
       "      <td>536.413043</td>\n",
       "      <td>551.184720</td>\n",
       "      <td>0.130179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.030316</td>\n",
       "      <td>0.925780</td>\n",
       "      <td>6.653409</td>\n",
       "      <td>6.559699</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.933319</td>\n",
       "      <td>0.985915</td>\n",
       "      <td>0.950933</td>\n",
       "      <td>0.068182</td>\n",
       "      <td>0.198864</td>\n",
       "      <td>565.340909</td>\n",
       "      <td>555.969910</td>\n",
       "      <td>0.198361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.040137</td>\n",
       "      <td>0.896228</td>\n",
       "      <td>6.653409</td>\n",
       "      <td>6.582628</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.912015</td>\n",
       "      <td>0.989362</td>\n",
       "      <td>0.941410</td>\n",
       "      <td>0.065341</td>\n",
       "      <td>0.264205</td>\n",
       "      <td>565.340909</td>\n",
       "      <td>558.262814</td>\n",
       "      <td>0.263702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.050384</td>\n",
       "      <td>0.868115</td>\n",
       "      <td>6.376184</td>\n",
       "      <td>6.540639</td>\n",
       "      <td>0.958333</td>\n",
       "      <td>0.882041</td>\n",
       "      <td>0.983051</td>\n",
       "      <td>0.929335</td>\n",
       "      <td>0.065341</td>\n",
       "      <td>0.329545</td>\n",
       "      <td>537.618371</td>\n",
       "      <td>554.063945</td>\n",
       "      <td>0.328540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.100342</td>\n",
       "      <td>0.556494</td>\n",
       "      <td>5.345474</td>\n",
       "      <td>5.945600</td>\n",
       "      <td>0.803419</td>\n",
       "      <td>0.746239</td>\n",
       "      <td>0.893617</td>\n",
       "      <td>0.838177</td>\n",
       "      <td>0.267045</td>\n",
       "      <td>0.596591</td>\n",
       "      <td>434.547397</td>\n",
       "      <td>494.559961</td>\n",
       "      <td>0.584028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.150299</td>\n",
       "      <td>0.233148</td>\n",
       "      <td>3.355138</td>\n",
       "      <td>5.084565</td>\n",
       "      <td>0.504274</td>\n",
       "      <td>0.368301</td>\n",
       "      <td>0.764205</td>\n",
       "      <td>0.681996</td>\n",
       "      <td>0.167614</td>\n",
       "      <td>0.764205</td>\n",
       "      <td>235.513792</td>\n",
       "      <td>408.456547</td>\n",
       "      <td>0.722496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.200256</td>\n",
       "      <td>0.100612</td>\n",
       "      <td>1.137335</td>\n",
       "      <td>4.099862</td>\n",
       "      <td>0.170940</td>\n",
       "      <td>0.145543</td>\n",
       "      <td>0.616205</td>\n",
       "      <td>0.548169</td>\n",
       "      <td>0.056818</td>\n",
       "      <td>0.821023</td>\n",
       "      <td>13.733489</td>\n",
       "      <td>309.986189</td>\n",
       "      <td>0.730570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.300171</td>\n",
       "      <td>0.044007</td>\n",
       "      <td>0.398067</td>\n",
       "      <td>2.867686</td>\n",
       "      <td>0.059829</td>\n",
       "      <td>0.064607</td>\n",
       "      <td>0.431010</td>\n",
       "      <td>0.387211</td>\n",
       "      <td>0.039773</td>\n",
       "      <td>0.860795</td>\n",
       "      <td>-60.193279</td>\n",
       "      <td>186.768557</td>\n",
       "      <td>0.659790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0.400085</td>\n",
       "      <td>0.027353</td>\n",
       "      <td>0.255900</td>\n",
       "      <td>2.215436</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.034070</td>\n",
       "      <td>0.332978</td>\n",
       "      <td>0.299020</td>\n",
       "      <td>0.025568</td>\n",
       "      <td>0.886364</td>\n",
       "      <td>-74.409965</td>\n",
       "      <td>121.543611</td>\n",
       "      <td>0.572293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.019450</td>\n",
       "      <td>0.255900</td>\n",
       "      <td>1.823864</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.023036</td>\n",
       "      <td>0.274125</td>\n",
       "      <td>0.243870</td>\n",
       "      <td>0.025568</td>\n",
       "      <td>0.911932</td>\n",
       "      <td>-74.409965</td>\n",
       "      <td>82.386364</td>\n",
       "      <td>0.484796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>0.599915</td>\n",
       "      <td>0.014352</td>\n",
       "      <td>0.199034</td>\n",
       "      <td>1.553251</td>\n",
       "      <td>0.029915</td>\n",
       "      <td>0.016788</td>\n",
       "      <td>0.233452</td>\n",
       "      <td>0.206050</td>\n",
       "      <td>0.019886</td>\n",
       "      <td>0.931818</td>\n",
       "      <td>-80.096639</td>\n",
       "      <td>55.325137</td>\n",
       "      <td>0.390612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0.699829</td>\n",
       "      <td>0.011285</td>\n",
       "      <td>0.170600</td>\n",
       "      <td>1.355850</td>\n",
       "      <td>0.025641</td>\n",
       "      <td>0.012737</td>\n",
       "      <td>0.203783</td>\n",
       "      <td>0.178451</td>\n",
       "      <td>0.017045</td>\n",
       "      <td>0.948864</td>\n",
       "      <td>-82.939977</td>\n",
       "      <td>35.585030</td>\n",
       "      <td>0.293085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0.799744</td>\n",
       "      <td>0.009052</td>\n",
       "      <td>0.142167</td>\n",
       "      <td>1.204221</td>\n",
       "      <td>0.021368</td>\n",
       "      <td>0.010186</td>\n",
       "      <td>0.180993</td>\n",
       "      <td>0.157429</td>\n",
       "      <td>0.014205</td>\n",
       "      <td>0.963068</td>\n",
       "      <td>-85.783314</td>\n",
       "      <td>20.422087</td>\n",
       "      <td>0.192214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0.899658</td>\n",
       "      <td>0.006486</td>\n",
       "      <td>0.199034</td>\n",
       "      <td>1.092586</td>\n",
       "      <td>0.029915</td>\n",
       "      <td>0.007830</td>\n",
       "      <td>0.164215</td>\n",
       "      <td>0.140815</td>\n",
       "      <td>0.019886</td>\n",
       "      <td>0.982955</td>\n",
       "      <td>-80.096639</td>\n",
       "      <td>9.258640</td>\n",
       "      <td>0.098030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001907</td>\n",
       "      <td>0.169874</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025532</td>\n",
       "      <td>0.004900</td>\n",
       "      <td>0.150299</td>\n",
       "      <td>0.127177</td>\n",
       "      <td>0.017045</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-83.012573</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    group  cumulative_data_fraction  lower_threshold      lift  \\\n",
       "0       1                  0.010248         0.955422  6.653409   \n",
       "1       2                  0.020068         0.940095  6.364130   \n",
       "2       3                  0.030316         0.925780  6.653409   \n",
       "3       4                  0.040137         0.896228  6.653409   \n",
       "4       5                  0.050384         0.868115  6.376184   \n",
       "5       6                  0.100342         0.556494  5.345474   \n",
       "6       7                  0.150299         0.233148  3.355138   \n",
       "7       8                  0.200256         0.100612  1.137335   \n",
       "8       9                  0.300171         0.044007  0.398067   \n",
       "9      10                  0.400085         0.027353  0.255900   \n",
       "10     11                  0.500000         0.019450  0.255900   \n",
       "11     12                  0.599915         0.014352  0.199034   \n",
       "12     13                  0.699829         0.011285  0.170600   \n",
       "13     14                  0.799744         0.009052  0.142167   \n",
       "14     15                  0.899658         0.006486  0.199034   \n",
       "15     16                  1.000000         0.001907  0.169874   \n",
       "\n",
       "    cumulative_lift  response_rate     score  cumulative_response_rate  \\\n",
       "0          6.653409       1.000000  0.970636                  1.000000   \n",
       "1          6.511847       0.956522  0.948753                  0.978723   \n",
       "2          6.559699       1.000000  0.933319                  0.985915   \n",
       "3          6.582628       1.000000  0.912015                  0.989362   \n",
       "4          6.540639       0.958333  0.882041                  0.983051   \n",
       "5          5.945600       0.803419  0.746239                  0.893617   \n",
       "6          5.084565       0.504274  0.368301                  0.764205   \n",
       "7          4.099862       0.170940  0.145543                  0.616205   \n",
       "8          2.867686       0.059829  0.064607                  0.431010   \n",
       "9          2.215436       0.038462  0.034070                  0.332978   \n",
       "10         1.823864       0.038462  0.023036                  0.274125   \n",
       "11         1.553251       0.029915  0.016788                  0.233452   \n",
       "12         1.355850       0.025641  0.012737                  0.203783   \n",
       "13         1.204221       0.021368  0.010186                  0.180993   \n",
       "14         1.092586       0.029915  0.007830                  0.164215   \n",
       "15         1.000000       0.025532  0.004900                  0.150299   \n",
       "\n",
       "    cumulative_score  capture_rate  cumulative_capture_rate        gain  \\\n",
       "0           0.970636      0.068182                 0.068182  565.340909   \n",
       "1           0.959927      0.062500                 0.130682  536.413043   \n",
       "2           0.950933      0.068182                 0.198864  565.340909   \n",
       "3           0.941410      0.065341                 0.264205  565.340909   \n",
       "4           0.929335      0.065341                 0.329545  537.618371   \n",
       "5           0.838177      0.267045                 0.596591  434.547397   \n",
       "6           0.681996      0.167614                 0.764205  235.513792   \n",
       "7           0.548169      0.056818                 0.821023   13.733489   \n",
       "8           0.387211      0.039773                 0.860795  -60.193279   \n",
       "9           0.299020      0.025568                 0.886364  -74.409965   \n",
       "10          0.243870      0.025568                 0.911932  -74.409965   \n",
       "11          0.206050      0.019886                 0.931818  -80.096639   \n",
       "12          0.178451      0.017045                 0.948864  -82.939977   \n",
       "13          0.157429      0.014205                 0.963068  -85.783314   \n",
       "14          0.140815      0.019886                 0.982955  -80.096639   \n",
       "15          0.127177      0.017045                 1.000000  -83.012573   \n",
       "\n",
       "    cumulative_gain  kolmogorov_smirnov  \n",
       "0        565.340909            0.068182  \n",
       "1        551.184720            0.130179  \n",
       "2        555.969910            0.198361  \n",
       "3        558.262814            0.263702  \n",
       "4        554.063945            0.328540  \n",
       "5        494.559961            0.584028  \n",
       "6        408.456547            0.722496  \n",
       "7        309.986189            0.730570  \n",
       "8        186.768557            0.659790  \n",
       "9        121.543611            0.572293  \n",
       "10        82.386364            0.484796  \n",
       "11        55.325137            0.390612  \n",
       "12        35.585030            0.293085  \n",
       "13        20.422087            0.192214  \n",
       "14         9.258640            0.098030  \n",
       "15         0.000000            0.000000  "
      ]
     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Cross-Validation Metrics Summary: \n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.936386</td>\n",
       "      <td>0.008663</td>\n",
       "      <td>0.929638</td>\n",
       "      <td>0.927505</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.933761</td>\n",
       "      <td>0.946581</td>\n",
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       "      <th>1</th>\n",
       "      <td>auc</td>\n",
       "      <td>0.905572</td>\n",
       "      <td>0.012673</td>\n",
       "      <td>0.902022</td>\n",
       "      <td>0.888939</td>\n",
       "      <td>0.912904</td>\n",
       "      <td>0.901584</td>\n",
       "      <td>0.922408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>err</td>\n",
       "      <td>0.063614</td>\n",
       "      <td>0.008663</td>\n",
       "      <td>0.070362</td>\n",
       "      <td>0.072495</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.066239</td>\n",
       "      <td>0.053419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>err_count</td>\n",
       "      <td>29.800000</td>\n",
       "      <td>4.086563</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f0point5</td>\n",
       "      <td>0.799785</td>\n",
       "      <td>0.034051</td>\n",
       "      <td>0.822622</td>\n",
       "      <td>0.758621</td>\n",
       "      <td>0.822785</td>\n",
       "      <td>0.766773</td>\n",
       "      <td>0.828125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>f1</td>\n",
       "      <td>0.776282</td>\n",
       "      <td>0.036851</td>\n",
       "      <td>0.795031</td>\n",
       "      <td>0.721311</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.755906</td>\n",
       "      <td>0.809160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>f2</td>\n",
       "      <td>0.754312</td>\n",
       "      <td>0.040922</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.778443</td>\n",
       "      <td>0.745342</td>\n",
       "      <td>0.791045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>lift_top_group</td>\n",
       "      <td>6.433440</td>\n",
       "      <td>0.772937</td>\n",
       "      <td>5.517647</td>\n",
       "      <td>5.684848</td>\n",
       "      <td>6.882353</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>6.882353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>logloss</td>\n",
       "      <td>0.215290</td>\n",
       "      <td>0.034978</td>\n",
       "      <td>0.257172</td>\n",
       "      <td>0.242893</td>\n",
       "      <td>0.192797</td>\n",
       "      <td>0.211387</td>\n",
       "      <td>0.172201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>max_per_class_error</td>\n",
       "      <td>0.259563</td>\n",
       "      <td>0.043904</td>\n",
       "      <td>0.247059</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.261538</td>\n",
       "      <td>0.220588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mcc</td>\n",
       "      <td>0.740633</td>\n",
       "      <td>0.039676</td>\n",
       "      <td>0.754438</td>\n",
       "      <td>0.682999</td>\n",
       "      <td>0.768926</td>\n",
       "      <td>0.717880</td>\n",
       "      <td>0.778924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>mean_per_class_accuracy</td>\n",
       "      <td>0.855642</td>\n",
       "      <td>0.022869</td>\n",
       "      <td>0.860846</td>\n",
       "      <td>0.818445</td>\n",
       "      <td>0.869853</td>\n",
       "      <td>0.851861</td>\n",
       "      <td>0.877206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>mean_per_class_error</td>\n",
       "      <td>0.144358</td>\n",
       "      <td>0.022869</td>\n",
       "      <td>0.139154</td>\n",
       "      <td>0.181555</td>\n",
       "      <td>0.130147</td>\n",
       "      <td>0.148139</td>\n",
       "      <td>0.122794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>mse</td>\n",
       "      <td>0.055669</td>\n",
       "      <td>0.010611</td>\n",
       "      <td>0.068063</td>\n",
       "      <td>0.063995</td>\n",
       "      <td>0.047877</td>\n",
       "      <td>0.055651</td>\n",
       "      <td>0.042757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>pr_auc</td>\n",
       "      <td>0.805132</td>\n",
       "      <td>0.062005</td>\n",
       "      <td>0.821680</td>\n",
       "      <td>0.706971</td>\n",
       "      <td>0.841642</td>\n",
       "      <td>0.788193</td>\n",
       "      <td>0.867174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>precision</td>\n",
       "      <td>0.816399</td>\n",
       "      <td>0.033541</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.838710</td>\n",
       "      <td>0.774193</td>\n",
       "      <td>0.841270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>r2</td>\n",
       "      <td>0.563393</td>\n",
       "      <td>0.072494</td>\n",
       "      <td>0.541323</td>\n",
       "      <td>0.470770</td>\n",
       "      <td>0.614476</td>\n",
       "      <td>0.534688</td>\n",
       "      <td>0.655708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>recall</td>\n",
       "      <td>0.740437</td>\n",
       "      <td>0.043904</td>\n",
       "      <td>0.752941</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.738462</td>\n",
       "      <td>0.779412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>rmse</td>\n",
       "      <td>0.235070</td>\n",
       "      <td>0.022654</td>\n",
       "      <td>0.260889</td>\n",
       "      <td>0.252973</td>\n",
       "      <td>0.218809</td>\n",
       "      <td>0.235904</td>\n",
       "      <td>0.206777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>specificity</td>\n",
       "      <td>0.970847</td>\n",
       "      <td>0.004198</td>\n",
       "      <td>0.968750</td>\n",
       "      <td>0.970223</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.965261</td>\n",
       "      <td>0.975000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  mean        sd  cv_1_valid  cv_2_valid  \\\n",
       "0                  accuracy   0.936386  0.008663    0.929638    0.927505   \n",
       "1                       auc   0.905572  0.012673    0.902022    0.888939   \n",
       "2                       err   0.063614  0.008663    0.070362    0.072495   \n",
       "3                 err_count  29.800000  4.086563   33.000000   34.000000   \n",
       "4                  f0point5   0.799785  0.034051    0.822622    0.758621   \n",
       "5                        f1   0.776282  0.036851    0.795031    0.721311   \n",
       "6                        f2   0.754312  0.040922    0.769231    0.687500   \n",
       "7            lift_top_group   6.433440  0.772937    5.517647    5.684848   \n",
       "8                   logloss   0.215290  0.034978    0.257172    0.242893   \n",
       "9       max_per_class_error   0.259563  0.043904    0.247059    0.333333   \n",
       "10                      mcc   0.740633  0.039676    0.754438    0.682999   \n",
       "11  mean_per_class_accuracy   0.855642  0.022869    0.860846    0.818445   \n",
       "12     mean_per_class_error   0.144358  0.022869    0.139154    0.181555   \n",
       "13                      mse   0.055669  0.010611    0.068063    0.063995   \n",
       "14                   pr_auc   0.805132  0.062005    0.821680    0.706971   \n",
       "15                precision   0.816399  0.033541    0.842105    0.785714   \n",
       "16                       r2   0.563393  0.072494    0.541323    0.470770   \n",
       "17                   recall   0.740437  0.043904    0.752941    0.666667   \n",
       "18                     rmse   0.235070  0.022654    0.260889    0.252973   \n",
       "19              specificity   0.970847  0.004198    0.968750    0.970223   \n",
       "\n",
       "    cv_3_valid  cv_4_valid  cv_5_valid  \n",
       "0     0.944444    0.933761    0.946581  \n",
       "1     0.912904    0.901584    0.922408  \n",
       "2     0.055556    0.066239    0.053419  \n",
       "3    26.000000   31.000000   25.000000  \n",
       "4     0.822785    0.766773    0.828125  \n",
       "5     0.800000    0.755906    0.809160  \n",
       "6     0.778443    0.745342    0.791045  \n",
       "7     6.882353    7.200000    6.882353  \n",
       "8     0.192797    0.211387    0.172201  \n",
       "9     0.235294    0.261538    0.220588  \n",
       "10    0.768926    0.717880    0.778924  \n",
       "11    0.869853    0.851861    0.877206  \n",
       "12    0.130147    0.148139    0.122794  \n",
       "13    0.047877    0.055651    0.042757  \n",
       "14    0.841642    0.788193    0.867174  \n",
       "15    0.838710    0.774193    0.841270  \n",
       "16    0.614476    0.534688    0.655708  \n",
       "17    0.764706    0.738462    0.779412  \n",
       "18    0.218809    0.235904    0.206777  \n",
       "19    0.975000    0.965261    0.975000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Scoring History: \n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <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>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>2022-07-06 00:40:12</td>\n",
       "      <td>7.601 sec</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.357364</td>\n",
       "      <td>0.423227</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.150299</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.849701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>2022-07-06 00:40:12</td>\n",
       "      <td>7.709 sec</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.262344</td>\n",
       "      <td>0.252827</td>\n",
       "      <td>0.977169</td>\n",
       "      <td>0.937010</td>\n",
       "      <td>6.653409</td>\n",
       "      <td>0.029035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td>2022-07-06 00:40:13</td>\n",
       "      <td>7.800 sec</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.213610</td>\n",
       "      <td>0.188216</td>\n",
       "      <td>0.986771</td>\n",
       "      <td>0.957943</td>\n",
       "      <td>6.653409</td>\n",
       "      <td>0.025619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td>2022-07-06 00:40:13</td>\n",
       "      <td>7.913 sec</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.183660</td>\n",
       "      <td>0.149481</td>\n",
       "      <td>0.992215</td>\n",
       "      <td>0.969919</td>\n",
       "      <td>6.653409</td>\n",
       "      <td>0.022630</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td></td>\n",
       "      <td>2022-07-06 00:40:13</td>\n",
       "      <td>8.023 sec</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.160428</td>\n",
       "      <td>0.120239</td>\n",
       "      <td>0.994861</td>\n",
       "      <td>0.979520</td>\n",
       "      <td>6.653409</td>\n",
       "      <td>0.020068</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td></td>\n",
       "      <td>2022-07-06 00:40:13</td>\n",
       "      <td>8.130 sec</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.143984</td>\n",
       "      <td>0.099934</td>\n",
       "      <td>0.997068</td>\n",
       "      <td>0.987085</td>\n",
       "      <td>6.653409</td>\n",
       "      <td>0.014944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td></td>\n",
       "      <td>2022-07-06 00:40:13</td>\n",
       "      <td>8.300 sec</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.128760</td>\n",
       "      <td>0.081767</td>\n",
       "      <td>0.998196</td>\n",
       "      <td>0.991923</td>\n",
       "      <td>6.653409</td>\n",
       "      <td>0.010675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td></td>\n",
       "      <td>2022-07-06 00:40:13</td>\n",
       "      <td>8.409 sec</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.117805</td>\n",
       "      <td>0.069675</td>\n",
       "      <td>0.998991</td>\n",
       "      <td>0.995165</td>\n",
       "      <td>6.653409</td>\n",
       "      <td>0.007686</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td></td>\n",
       "      <td>2022-07-06 00:40:13</td>\n",
       "      <td>8.519 sec</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.105686</td>\n",
       "      <td>0.058905</td>\n",
       "      <td>0.999643</td>\n",
       "      <td>0.998147</td>\n",
       "      <td>6.653409</td>\n",
       "      <td>0.005551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td></td>\n",
       "      <td>2022-07-06 00:40:13</td>\n",
       "      <td>8.625 sec</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.096039</td>\n",
       "      <td>0.051241</td>\n",
       "      <td>0.999920</td>\n",
       "      <td>0.999557</td>\n",
       "      <td>6.653409</td>\n",
       "      <td>0.003416</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td></td>\n",
       "      <td>2022-07-06 00:40:13</td>\n",
       "      <td>8.704 sec</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.093145</td>\n",
       "      <td>0.049222</td>\n",
       "      <td>0.999939</td>\n",
       "      <td>0.999668</td>\n",
       "      <td>6.653409</td>\n",
       "      <td>0.002135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                timestamp    duration  number_of_trees  training_rmse  \\\n",
       "0     2022-07-06 00:40:12   7.601 sec              0.0       0.357364   \n",
       "1     2022-07-06 00:40:12   7.709 sec              5.0       0.262344   \n",
       "2     2022-07-06 00:40:13   7.800 sec             10.0       0.213610   \n",
       "3     2022-07-06 00:40:13   7.913 sec             15.0       0.183660   \n",
       "4     2022-07-06 00:40:13   8.023 sec             20.0       0.160428   \n",
       "5     2022-07-06 00:40:13   8.130 sec             25.0       0.143984   \n",
       "6     2022-07-06 00:40:13   8.300 sec             30.0       0.128760   \n",
       "7     2022-07-06 00:40:13   8.409 sec             35.0       0.117805   \n",
       "8     2022-07-06 00:40:13   8.519 sec             40.0       0.105686   \n",
       "9     2022-07-06 00:40:13   8.625 sec             45.0       0.096039   \n",
       "10    2022-07-06 00:40:13   8.704 sec             46.0       0.093145   \n",
       "\n",
       "    training_logloss  training_auc  training_pr_auc  training_lift  \\\n",
       "0           0.423227      0.500000         0.150299       1.000000   \n",
       "1           0.252827      0.977169         0.937010       6.653409   \n",
       "2           0.188216      0.986771         0.957943       6.653409   \n",
       "3           0.149481      0.992215         0.969919       6.653409   \n",
       "4           0.120239      0.994861         0.979520       6.653409   \n",
       "5           0.099934      0.997068         0.987085       6.653409   \n",
       "6           0.081767      0.998196         0.991923       6.653409   \n",
       "7           0.069675      0.998991         0.995165       6.653409   \n",
       "8           0.058905      0.999643         0.998147       6.653409   \n",
       "9           0.051241      0.999920         0.999557       6.653409   \n",
       "10          0.049222      0.999939         0.999668       6.653409   \n",
       "\n",
       "    training_classification_error  \n",
       "0                        0.849701  \n",
       "1                        0.029035  \n",
       "2                        0.025619  \n",
       "3                        0.022630  \n",
       "4                        0.020068  \n",
       "5                        0.014944  \n",
       "6                        0.010675  \n",
       "7                        0.007686  \n",
       "8                        0.005551  \n",
       "9                        0.003416  \n",
       "10                       0.002135  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Variable Importances: \n"
     ]
    },
    {
     "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>variable</th>\n",
       "      <th>relative_importance</th>\n",
       "      <th>scaled_importance</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>State</td>\n",
       "      <td>261.457062</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Day Mins</td>\n",
       "      <td>196.744736</td>\n",
       "      <td>0.752493</td>\n",
       "      <td>0.163178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CustServ Calls</td>\n",
       "      <td>154.550491</td>\n",
       "      <td>0.591112</td>\n",
       "      <td>0.128183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Day Charge</td>\n",
       "      <td>110.697166</td>\n",
       "      <td>0.423386</td>\n",
       "      <td>0.091811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Int'l Plan</td>\n",
       "      <td>86.897903</td>\n",
       "      <td>0.332360</td>\n",
       "      <td>0.072072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Intl Calls</td>\n",
       "      <td>79.236420</td>\n",
       "      <td>0.303057</td>\n",
       "      <td>0.065718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Eve Charge</td>\n",
       "      <td>61.366600</td>\n",
       "      <td>0.234710</td>\n",
       "      <td>0.050897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Intl Mins</td>\n",
       "      <td>53.108543</td>\n",
       "      <td>0.203125</td>\n",
       "      <td>0.044048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Eve Mins</td>\n",
       "      <td>50.958187</td>\n",
       "      <td>0.194901</td>\n",
       "      <td>0.042264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Intl Charge</td>\n",
       "      <td>38.061172</td>\n",
       "      <td>0.145573</td>\n",
       "      <td>0.031568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>VMail Message</td>\n",
       "      <td>25.536459</td>\n",
       "      <td>0.097670</td>\n",
       "      <td>0.021180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>VMail Plan</td>\n",
       "      <td>24.010521</td>\n",
       "      <td>0.091834</td>\n",
       "      <td>0.019914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Night Mins</td>\n",
       "      <td>13.251447</td>\n",
       "      <td>0.050683</td>\n",
       "      <td>0.010991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Day Calls</td>\n",
       "      <td>10.961466</td>\n",
       "      <td>0.041925</td>\n",
       "      <td>0.009091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Night Calls</td>\n",
       "      <td>10.943468</td>\n",
       "      <td>0.041856</td>\n",
       "      <td>0.009076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Night Charge</td>\n",
       "      <td>10.605638</td>\n",
       "      <td>0.040564</td>\n",
       "      <td>0.008796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Account Length</td>\n",
       "      <td>8.637052</td>\n",
       "      <td>0.033034</td>\n",
       "      <td>0.007163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Eve Calls</td>\n",
       "      <td>7.112403</td>\n",
       "      <td>0.027203</td>\n",
       "      <td>0.005899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Area Code</td>\n",
       "      <td>1.568077</td>\n",
       "      <td>0.005997</td>\n",
       "      <td>0.001301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          variable  relative_importance  scaled_importance  percentage\n",
       "0            State           261.457062           1.000000    0.216850\n",
       "1         Day Mins           196.744736           0.752493    0.163178\n",
       "2   CustServ Calls           154.550491           0.591112    0.128183\n",
       "3       Day Charge           110.697166           0.423386    0.091811\n",
       "4       Int'l Plan            86.897903           0.332360    0.072072\n",
       "5       Intl Calls            79.236420           0.303057    0.065718\n",
       "6       Eve Charge            61.366600           0.234710    0.050897\n",
       "7        Intl Mins            53.108543           0.203125    0.044048\n",
       "8         Eve Mins            50.958187           0.194901    0.042264\n",
       "9      Intl Charge            38.061172           0.145573    0.031568\n",
       "10   VMail Message            25.536459           0.097670    0.021180\n",
       "11      VMail Plan            24.010521           0.091834    0.019914\n",
       "12      Night Mins            13.251447           0.050683    0.010991\n",
       "13       Day Calls            10.961466           0.041925    0.009091\n",
       "14     Night Calls            10.943468           0.041856    0.009076\n",
       "15    Night Charge            10.605638           0.040564    0.008796\n",
       "16  Account Length             8.637052           0.033034    0.007163\n",
       "17       Eve Calls             7.112403           0.027203    0.005899\n",
       "18       Area Code             1.568077           0.005997    0.001301"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aml.leader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can examine the model automatically by calling the explain function.  The explain function returns performance metrics, comparison between the models, and explains the best model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h1>Leaderboard</h1>"
      ],
      "text/markdown": [
       "\n",
       "\n",
       "# Leaderboard"
      ],
      "text/plain": [
       "\n",
       "\n",
       "# Leaderboard"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<blockquote>Leaderboard shows models with their metrics. When provided with H2OAutoML object, the leaderboard shows 5-fold cross-validated metrics by default (depending on the H2OAutoML settings), otherwise it shows metrics computed on the frame. At most 20 models are shown by default.</blockquote>"
      ],
      "text/markdown": [
       "\n",
       "> Leaderboard shows models with their metrics. When provided with H2OAutoML object, the leaderboard shows 5-fold cross-validated metrics by default (depending on the H2OAutoML settings), otherwise it shows metrics computed on the frame. At most 20 models are shown by default."
      ],
      "text/plain": [
       "\n",
       "> Leaderboard shows models with their metrics. When provided with H2OAutoML object, the leaderboard shows 5-fold cross-validated metrics by default (depending on the H2OAutoML settings), otherwise it shows metrics computed on the frame. At most 20 models are shown by default."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>model_id                                  </th><th style=\"text-align: right;\">     auc</th><th style=\"text-align: right;\">  logloss</th><th style=\"text-align: right;\">   aucpr</th><th style=\"text-align: right;\">  mean_per_class_error</th><th style=\"text-align: right;\">    rmse</th><th style=\"text-align: right;\">      mse</th><th style=\"text-align: right;\">  training_time_ms</th><th style=\"text-align: right;\">  predict_time_per_row_ms</th><th>algo  </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>GBM_2_AutoML_1_20220706_03930             </td><td style=\"text-align: right;\">0.899849</td><td style=\"text-align: right;\"> 0.183781</td><td style=\"text-align: right;\">0.818355</td><td style=\"text-align: right;\">              0.143308</td><td style=\"text-align: right;\">0.211693</td><td style=\"text-align: right;\">0.044814 </td><td style=\"text-align: right;\">              1118</td><td style=\"text-align: right;\">                 0.054919</td><td>GBM   </td></tr>\n",
       "<tr><td>GBM_grid_1_AutoML_1_20220706_03930_model_2</td><td style=\"text-align: right;\">0.895149</td><td style=\"text-align: right;\"> 0.199008</td><td style=\"text-align: right;\">0.80031 </td><td style=\"text-align: right;\">              0.13447 </td><td style=\"text-align: right;\">0.22478 </td><td style=\"text-align: right;\">0.0505258</td><td style=\"text-align: right;\">               840</td><td style=\"text-align: right;\">                 0.059752</td><td>GBM   </td></tr>\n",
       "<tr><td>GBM_5_AutoML_1_20220706_03930             </td><td style=\"text-align: right;\">0.894465</td><td style=\"text-align: right;\"> 0.185459</td><td style=\"text-align: right;\">0.800027</td><td style=\"text-align: right;\">              0.148569</td><td style=\"text-align: right;\">0.211678</td><td style=\"text-align: right;\">0.0448074</td><td style=\"text-align: right;\">               572</td><td style=\"text-align: right;\">                 0.043864</td><td>GBM   </td></tr>\n",
       "<tr><td>DRF_1_AutoML_1_20220706_03930             </td><td style=\"text-align: right;\">0.887819</td><td style=\"text-align: right;\"> 0.44122 </td><td style=\"text-align: right;\">0.837511</td><td style=\"text-align: right;\">              0.09375 </td><td style=\"text-align: right;\">0.217224</td><td style=\"text-align: right;\">0.0471862</td><td style=\"text-align: right;\">              1698</td><td style=\"text-align: right;\">                 0.056871</td><td>DRF   </td></tr>\n",
       "<tr><td>GBM_3_AutoML_1_20220706_03930             </td><td style=\"text-align: right;\">0.887276</td><td style=\"text-align: right;\"> 0.18667 </td><td style=\"text-align: right;\">0.812002</td><td style=\"text-align: right;\">              0.135732</td><td style=\"text-align: right;\">0.208377</td><td style=\"text-align: right;\">0.0434209</td><td style=\"text-align: right;\">              1298</td><td style=\"text-align: right;\">                 0.091041</td><td>GBM   </td></tr>\n",
       "<tr><td>GBM_grid_1_AutoML_1_20220706_03930_model_1</td><td style=\"text-align: right;\">0.883838</td><td style=\"text-align: right;\"> 0.178955</td><td style=\"text-align: right;\">0.828886</td><td style=\"text-align: right;\">              0.118266</td><td style=\"text-align: right;\">0.1988  </td><td style=\"text-align: right;\">0.0395214</td><td style=\"text-align: right;\">               747</td><td style=\"text-align: right;\">                 0.049364</td><td>GBM   </td></tr>\n",
       "<tr><td>XRT_1_AutoML_1_20220706_03930             </td><td style=\"text-align: right;\">0.879787</td><td style=\"text-align: right;\"> 0.290344</td><td style=\"text-align: right;\">0.807797</td><td style=\"text-align: right;\">              0.123422</td><td style=\"text-align: right;\">0.237825</td><td style=\"text-align: right;\">0.0565606</td><td style=\"text-align: right;\">              1140</td><td style=\"text-align: right;\">                 0.056676</td><td>DRF   </td></tr>\n",
       "<tr><td>GBM_4_AutoML_1_20220706_03930             </td><td style=\"text-align: right;\">0.879577</td><td style=\"text-align: right;\"> 0.19549 </td><td style=\"text-align: right;\">0.813502</td><td style=\"text-align: right;\">              0.131629</td><td style=\"text-align: right;\">0.212793</td><td style=\"text-align: right;\">0.0452811</td><td style=\"text-align: right;\">              1092</td><td style=\"text-align: right;\">                 0.081414</td><td>GBM   </td></tr>\n",
       "<tr><td>GBM_1_AutoML_1_20220706_03930             </td><td style=\"text-align: right;\">0.873159</td><td style=\"text-align: right;\"> 0.245236</td><td style=\"text-align: right;\">0.713209</td><td style=\"text-align: right;\">              0.137311</td><td style=\"text-align: right;\">0.257353</td><td style=\"text-align: right;\">0.0662308</td><td style=\"text-align: right;\">              3525</td><td style=\"text-align: right;\">                 0.065486</td><td>GBM   </td></tr>\n",
       "<tr><td>GLM_1_AutoML_1_20220706_03930             </td><td style=\"text-align: right;\">0.817796</td><td style=\"text-align: right;\"> 0.309194</td><td style=\"text-align: right;\">0.459268</td><td style=\"text-align: right;\">              0.261258</td><td style=\"text-align: right;\">0.302586</td><td style=\"text-align: right;\">0.0915581</td><td style=\"text-align: right;\">              1235</td><td style=\"text-align: right;\">                 0.00937 </td><td>GLM   </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h1>Confusion Matrix</h1>"
      ],
      "text/markdown": [
       "\n",
       "\n",
       "# Confusion Matrix"
      ],
      "text/plain": [
       "\n",
       "\n",
       "# Confusion Matrix"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<blockquote>Confusion matrix shows a predicted class vs an actual class.</blockquote>"
      ],
      "text/markdown": [
       "\n",
       "> Confusion matrix shows a predicted class vs an actual class."
      ],
      "text/plain": [
       "\n",
       "> Confusion matrix shows a predicted class vs an actual class."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h2>GBM_2_AutoML_1_20220706_03930</h2>"
      ],
      "text/markdown": [
       "\n",
       "\n",
       "## GBM_2_AutoML_1_20220706_03930"
      ],
      "text/plain": [
       "\n",
       "\n",
       "## GBM_2_AutoML_1_20220706_03930"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.22615900593018554: \n"
     ]
    },
    {
     "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></th>\n",
       "      <th>False.</th>\n",
       "      <th>True.</th>\n",
       "      <th>Error</th>\n",
       "      <th>Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False.</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.001</td>\n",
       "      <td>(2.0/1990.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True.</td>\n",
       "      <td>3.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>(3.0/352.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Total</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>0.0021</td>\n",
       "      <td>(5.0/2342.0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           False.  True.   Error           Rate\n",
       "0  False.  1988.0    2.0   0.001   (2.0/1990.0)\n",
       "1   True.     3.0  349.0  0.0085    (3.0/352.0)\n",
       "2   Total  1991.0  351.0  0.0021   (5.0/2342.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h1>Variable Importance</h1>"
      ],
      "text/markdown": [
       "\n",
       "\n",
       "# Variable Importance"
      ],
      "text/plain": [
       "\n",
       "\n",
       "# Variable Importance"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<blockquote>The variable importance plot shows the relative importance of the most important variables in the model.</blockquote>"
      ],
      "text/markdown": [
       "\n",
       "> The variable importance plot shows the relative importance of the most important variables in the model."
      ],
      "text/plain": [
       "\n",
       "> The variable importance plot shows the relative importance of the most important variables in the model."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<h1>Variable Importance Heatmap</h1>"
      ],
      "text/markdown": [
       "\n",
       "\n",
       "# Variable Importance Heatmap"
      ],
      "text/plain": [
       "\n",
       "\n",
       "# Variable Importance Heatmap"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<blockquote>Variable importance heatmap shows variable importance across multiple models. Some models in H2O return variable importance for one-hot (binary indicator) encoded versions of categorical columns (e.g. Deep Learning, XGBoost). In order for the variable importance of categorical columns to be compared across all model types we compute a summarization of the the variable importance across all one-hot encoded features and return a single variable importance for the original categorical feature. By default, the models and variables are ordered by their similarity.</blockquote>"
      ],
      "text/markdown": [
       "\n",
       "> Variable importance heatmap shows variable importance across multiple models. Some models in H2O return variable importance for one-hot (binary indicator) encoded versions of categorical columns (e.g. Deep Learning, XGBoost). In order for the variable importance of categorical columns to be compared across all model types we compute a summarization of the the variable importance across all one-hot encoded features and return a single variable importance for the original categorical feature. By default, the models and variables are ordered by their similarity."
      ],
      "text/plain": [
       "\n",
       "> Variable importance heatmap shows variable importance across multiple models. Some models in H2O return variable importance for one-hot (binary indicator) encoded versions of categorical columns (e.g. Deep Learning, XGBoost). In order for the variable importance of categorical columns to be compared across all model types we compute a summarization of the the variable importance across all one-hot encoded features and return a single variable importance for the original categorical feature. By default, the models and variables are ordered by their similarity."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<h1>Model Correlation</h1>"
      ],
      "text/markdown": [
       "\n",
       "\n",
       "# Model Correlation"
      ],
      "text/plain": [
       "\n",
       "\n",
       "# Model Correlation"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<blockquote>This plot shows the correlation between the predictions of the models. For classification, frequency of identical predictions is used. By default, models are ordered by their similarity (as computed by hierarchical clustering). Interpretable models, such as GAM, GLM, and RuleFit are highlighted using red colored text.</blockquote>"
      ],
      "text/markdown": [
       "\n",
       "> This plot shows the correlation between the predictions of the models. For classification, frequency of identical predictions is used. By default, models are ordered by their similarity (as computed by hierarchical clustering). Interpretable models, such as GAM, GLM, and RuleFit are highlighted using red colored text."
      ],
      "text/plain": [
       "\n",
       "> This plot shows the correlation between the predictions of the models. For classification, frequency of identical predictions is used. By default, models are ordered by their similarity (as computed by hierarchical clustering). Interpretable models, such as GAM, GLM, and RuleFit are highlighted using red colored text."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<h1>SHAP Summary</h1>"
      ],
      "text/markdown": [
       "\n",
       "\n",
       "# SHAP Summary"
      ],
      "text/plain": [
       "\n",
       "\n",
       "# SHAP Summary"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<blockquote>SHAP summary plot shows the contribution of the features for each instance (row of data). The sum of the feature contributions and the bias term is equal to the raw prediction of the model, i.e., prediction before applying inverse link function.</blockquote>"
      ],
      "text/markdown": [
       "\n",
       "> SHAP summary plot shows the contribution of the features for each instance (row of data). The sum of the feature contributions and the bias term is equal to the raw prediction of the model, i.e., prediction before applying inverse link function."
      ],
      "text/plain": [
       "\n",
       "> SHAP summary plot shows the contribution of the features for each instance (row of data). The sum of the feature contributions and the bias term is equal to the raw prediction of the model, i.e., prediction before applying inverse link function."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<h1>Partial Dependence Plots</h1>"
      ],
      "text/markdown": [
       "\n",
       "\n",
       "# Partial Dependence Plots"
      ],
      "text/plain": [
       "\n",
       "\n",
       "# Partial Dependence Plots"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<blockquote>Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest.</blockquote>"
      ],
      "text/markdown": [
       "\n",
       "> Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest."
      ],
      "text/plain": [
       "\n",
       "> Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<h1>Leaderboard</h1>"
      ],
      "text/markdown": [
       "\n",
       "\n",
       "# Leaderboard"
      ],
      "text/plain": [
       "\n",
       "\n",
       "# Leaderboard"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<blockquote>Leaderboard shows models with their metrics. When provided with H2OAutoML object, the leaderboard shows 5-fold cross-validated metrics by default (depending on the H2OAutoML settings), otherwise it shows metrics computed on the frame. At most 20 models are shown by default.</blockquote>"
      ],
      "text/markdown": [
       "\n",
       "> Leaderboard shows models with their metrics. When provided with H2OAutoML object, the leaderboard shows 5-fold cross-validated metrics by default (depending on the H2OAutoML settings), otherwise it shows metrics computed on the frame. At most 20 models are shown by default."
      ],
      "text/plain": [
       "\n",
       "> Leaderboard shows models with their metrics. When provided with H2OAutoML object, the leaderboard shows 5-fold cross-validated metrics by default (depending on the H2OAutoML settings), otherwise it shows metrics computed on the frame. At most 20 models are shown by default."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>model_id                                  </th><th style=\"text-align: right;\">     auc</th><th style=\"text-align: right;\">  logloss</th><th style=\"text-align: right;\">   aucpr</th><th style=\"text-align: right;\">  mean_per_class_error</th><th style=\"text-align: right;\">    rmse</th><th style=\"text-align: right;\">      mse</th><th style=\"text-align: right;\">  training_time_ms</th><th style=\"text-align: right;\">  predict_time_per_row_ms</th><th>algo  </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>GBM_2_AutoML_1_20220706_03930             </td><td style=\"text-align: right;\">0.899849</td><td style=\"text-align: right;\"> 0.183781</td><td style=\"text-align: right;\">0.818355</td><td style=\"text-align: right;\">              0.143308</td><td style=\"text-align: right;\">0.211693</td><td style=\"text-align: right;\">0.044814 </td><td style=\"text-align: right;\">              1118</td><td style=\"text-align: right;\">                 0.054919</td><td>GBM   </td></tr>\n",
       "<tr><td>GBM_grid_1_AutoML_1_20220706_03930_model_2</td><td style=\"text-align: right;\">0.895149</td><td style=\"text-align: right;\"> 0.199008</td><td style=\"text-align: right;\">0.80031 </td><td style=\"text-align: right;\">              0.13447 </td><td style=\"text-align: right;\">0.22478 </td><td style=\"text-align: right;\">0.0505258</td><td style=\"text-align: right;\">               840</td><td style=\"text-align: right;\">                 0.059752</td><td>GBM   </td></tr>\n",
       "<tr><td>GBM_5_AutoML_1_20220706_03930             </td><td style=\"text-align: right;\">0.894465</td><td style=\"text-align: right;\"> 0.185459</td><td style=\"text-align: right;\">0.800027</td><td style=\"text-align: right;\">              0.148569</td><td style=\"text-align: right;\">0.211678</td><td style=\"text-align: right;\">0.0448074</td><td style=\"text-align: right;\">               572</td><td style=\"text-align: right;\">                 0.043864</td><td>GBM   </td></tr>\n",
       "<tr><td>DRF_1_AutoML_1_20220706_03930             </td><td style=\"text-align: right;\">0.887819</td><td style=\"text-align: right;\"> 0.44122 </td><td style=\"text-align: right;\">0.837511</td><td style=\"text-align: right;\">              0.09375 </td><td style=\"text-align: right;\">0.217224</td><td style=\"text-align: right;\">0.0471862</td><td style=\"text-align: right;\">              1698</td><td style=\"text-align: right;\">                 0.056871</td><td>DRF   </td></tr>\n",
       "<tr><td>GBM_3_AutoML_1_20220706_03930             </td><td style=\"text-align: right;\">0.887276</td><td style=\"text-align: right;\"> 0.18667 </td><td style=\"text-align: right;\">0.812002</td><td style=\"text-align: right;\">              0.135732</td><td style=\"text-align: right;\">0.208377</td><td style=\"text-align: right;\">0.0434209</td><td style=\"text-align: right;\">              1298</td><td style=\"text-align: right;\">                 0.091041</td><td>GBM   </td></tr>\n",
       "<tr><td>GBM_grid_1_AutoML_1_20220706_03930_model_1</td><td style=\"text-align: right;\">0.883838</td><td style=\"text-align: right;\"> 0.178955</td><td style=\"text-align: right;\">0.828886</td><td style=\"text-align: right;\">              0.118266</td><td style=\"text-align: right;\">0.1988  </td><td style=\"text-align: right;\">0.0395214</td><td style=\"text-align: right;\">               747</td><td style=\"text-align: right;\">                 0.049364</td><td>GBM   </td></tr>\n",
       "<tr><td>XRT_1_AutoML_1_20220706_03930             </td><td style=\"text-align: right;\">0.879787</td><td style=\"text-align: right;\"> 0.290344</td><td style=\"text-align: right;\">0.807797</td><td style=\"text-align: right;\">              0.123422</td><td style=\"text-align: right;\">0.237825</td><td style=\"text-align: right;\">0.0565606</td><td style=\"text-align: right;\">              1140</td><td style=\"text-align: right;\">                 0.056676</td><td>DRF   </td></tr>\n",
       "<tr><td>GBM_4_AutoML_1_20220706_03930             </td><td style=\"text-align: right;\">0.879577</td><td style=\"text-align: right;\"> 0.19549 </td><td style=\"text-align: right;\">0.813502</td><td style=\"text-align: right;\">              0.131629</td><td style=\"text-align: right;\">0.212793</td><td style=\"text-align: right;\">0.0452811</td><td style=\"text-align: right;\">              1092</td><td style=\"text-align: right;\">                 0.081414</td><td>GBM   </td></tr>\n",
       "<tr><td>GBM_1_AutoML_1_20220706_03930             </td><td style=\"text-align: right;\">0.873159</td><td style=\"text-align: right;\"> 0.245236</td><td style=\"text-align: right;\">0.713209</td><td style=\"text-align: right;\">              0.137311</td><td style=\"text-align: right;\">0.257353</td><td style=\"text-align: right;\">0.0662308</td><td style=\"text-align: right;\">              3525</td><td style=\"text-align: right;\">                 0.065486</td><td>GBM   </td></tr>\n",
       "<tr><td>GLM_1_AutoML_1_20220706_03930             </td><td style=\"text-align: right;\">0.817796</td><td style=\"text-align: right;\"> 0.309194</td><td style=\"text-align: right;\">0.459268</td><td style=\"text-align: right;\">              0.261258</td><td style=\"text-align: right;\">0.302586</td><td style=\"text-align: right;\">0.0915581</td><td style=\"text-align: right;\">              1235</td><td style=\"text-align: right;\">                 0.00937 </td><td>GLM   </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h1>Confusion Matrix</h1>"
      ],
      "text/markdown": [
       "\n",
       "\n",
       "# Confusion Matrix"
      ],
      "text/plain": [
       "\n",
       "\n",
       "# Confusion Matrix"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<blockquote>Confusion matrix shows a predicted class vs an actual class.</blockquote>"
      ],
      "text/markdown": [
       "\n",
       "> Confusion matrix shows a predicted class vs an actual class."
      ],
      "text/plain": [
       "\n",
       "> Confusion matrix shows a predicted class vs an actual class."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h2>GBM_2_AutoML_1_20220706_03930</h2>"
      ],
      "text/markdown": [
       "\n",
       "\n",
       "## GBM_2_AutoML_1_20220706_03930"
      ],
      "text/plain": [
       "\n",
       "\n",
       "## GBM_2_AutoML_1_20220706_03930"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.22615900593018554: \n"
     ]
    },
    {
     "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></th>\n",
       "      <th>False.</th>\n",
       "      <th>True.</th>\n",
       "      <th>Error</th>\n",
       "      <th>Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False.</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.001</td>\n",
       "      <td>(2.0/1990.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True.</td>\n",
       "      <td>3.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>(3.0/352.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Total</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>0.0021</td>\n",
       "      <td>(5.0/2342.0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           False.  True.   Error           Rate\n",
       "0  False.  1988.0    2.0   0.001   (2.0/1990.0)\n",
       "1   True.     3.0  349.0  0.0085    (3.0/352.0)\n",
       "2   Total  1991.0  351.0  0.0021   (5.0/2342.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h1>Variable Importance</h1>"
      ],
      "text/markdown": [
       "\n",
       "\n",
       "# Variable Importance"
      ],
      "text/plain": [
       "\n",
       "\n",
       "# Variable Importance"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<blockquote>The variable importance plot shows the relative importance of the most important variables in the model.</blockquote>"
      ],
      "text/markdown": [
       "\n",
       "> The variable importance plot shows the relative importance of the most important variables in the model."
      ],
      "text/plain": [
       "\n",
       "> The variable importance plot shows the relative importance of the most important variables in the model."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<h2o.plot._plot_result._MObject at 0x1245c34c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h1>Variable Importance Heatmap</h1>"
      ],
      "text/markdown": [
       "\n",
       "\n",
       "# Variable Importance Heatmap"
      ],
      "text/plain": [
       "\n",
       "\n",
       "# Variable Importance Heatmap"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<blockquote>Variable importance heatmap shows variable importance across multiple models. Some models in H2O return variable importance for one-hot (binary indicator) encoded versions of categorical columns (e.g. Deep Learning, XGBoost). In order for the variable importance of categorical columns to be compared across all model types we compute a summarization of the the variable importance across all one-hot encoded features and return a single variable importance for the original categorical feature. By default, the models and variables are ordered by their similarity.</blockquote>"
      ],
      "text/markdown": [
       "\n",
       "> Variable importance heatmap shows variable importance across multiple models. Some models in H2O return variable importance for one-hot (binary indicator) encoded versions of categorical columns (e.g. Deep Learning, XGBoost). In order for the variable importance of categorical columns to be compared across all model types we compute a summarization of the the variable importance across all one-hot encoded features and return a single variable importance for the original categorical feature. By default, the models and variables are ordered by their similarity."
      ],
      "text/plain": [
       "\n",
       "> Variable importance heatmap shows variable importance across multiple models. Some models in H2O return variable importance for one-hot (binary indicator) encoded versions of categorical columns (e.g. Deep Learning, XGBoost). In order for the variable importance of categorical columns to be compared across all model types we compute a summarization of the the variable importance across all one-hot encoded features and return a single variable importance for the original categorical feature. By default, the models and variables are ordered by their similarity."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<h2o.plot._plot_result._MObject at 0x121f69730>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h1>Model Correlation</h1>"
      ],
      "text/markdown": [
       "\n",
       "\n",
       "# Model Correlation"
      ],
      "text/plain": [
       "\n",
       "\n",
       "# Model Correlation"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<blockquote>This plot shows the correlation between the predictions of the models. For classification, frequency of identical predictions is used. By default, models are ordered by their similarity (as computed by hierarchical clustering). Interpretable models, such as GAM, GLM, and RuleFit are highlighted using red colored text.</blockquote>"
      ],
      "text/markdown": [
       "\n",
       "> This plot shows the correlation between the predictions of the models. For classification, frequency of identical predictions is used. By default, models are ordered by their similarity (as computed by hierarchical clustering). Interpretable models, such as GAM, GLM, and RuleFit are highlighted using red colored text."
      ],
      "text/plain": [
       "\n",
       "> This plot shows the correlation between the predictions of the models. For classification, frequency of identical predictions is used. By default, models are ordered by their similarity (as computed by hierarchical clustering). Interpretable models, such as GAM, GLM, and RuleFit are highlighted using red colored text."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<h2o.plot._plot_result._MObject at 0x12522cd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h1>SHAP Summary</h1>"
      ],
      "text/markdown": [
       "\n",
       "\n",
       "# SHAP Summary"
      ],
      "text/plain": [
       "\n",
       "\n",
       "# SHAP Summary"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<blockquote>SHAP summary plot shows the contribution of the features for each instance (row of data). The sum of the feature contributions and the bias term is equal to the raw prediction of the model, i.e., prediction before applying inverse link function.</blockquote>"
      ],
      "text/markdown": [
       "\n",
       "> SHAP summary plot shows the contribution of the features for each instance (row of data). The sum of the feature contributions and the bias term is equal to the raw prediction of the model, i.e., prediction before applying inverse link function."
      ],
      "text/plain": [
       "\n",
       "> SHAP summary plot shows the contribution of the features for each instance (row of data). The sum of the feature contributions and the bias term is equal to the raw prediction of the model, i.e., prediction before applying inverse link function."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<h2o.plot._plot_result._MObject at 0x124e443d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h1>Partial Dependence Plots</h1>"
      ],
      "text/markdown": [
       "\n",
       "\n",
       "# Partial Dependence Plots"
      ],
      "text/plain": [
       "\n",
       "\n",
       "# Partial Dependence Plots"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<blockquote>Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest.</blockquote>"
      ],
      "text/markdown": [
       "\n",
       "> Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest."
      ],
      "text/plain": [
       "\n",
       "> Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<h2o.plot._plot_result._MObject at 0x1254235b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<h2o.plot._plot_result._MObject at 0x125840dc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<h2o.plot._plot_result._MObject at 0x125bf71c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<h2o.plot._plot_result._MObject at 0x125fcfbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<h2o.plot._plot_result._MObject at 0x123653490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aml.explain(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deployment objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can download the Model Deployment Object (the MOJO) for easy deployment in MLOps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_mojo = aml.leader.download_mojo()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "local_mojo_name = os.path.basename(local_mojo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "!rm $local_mojo_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<h2osteam.clients.h2ok8s.h2ok8s.H2oKubernetesCluster at 0x106c79c70>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h2o_machine.terminate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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