H2O_AutoML_IPYNB/neural_networks-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# imports\n",
    "import h2o \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from h2o.estimators.deeplearning import H2ODeepLearningEstimator\n",
    "from h2o.grid.grid_search import H2OGridSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# display matplotlib graphics in notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321..... not found.\n",
      "Attempting to start a local H2O server...\n",
      "  Java Version: java version \"1.8.0_112\"; Java(TM) SE Runtime Environment (build 1.8.0_112-b16); Java HotSpot(TM) 64-Bit Server VM (build 25.112-b16, mixed mode)\n",
      "  Starting server from /Users/phall/anaconda/lib/python3.5/site-packages/h2o/backend/bin/h2o.jar\n",
      "  Ice root: /var/folders/tc/0ss1l73113j3wdyjsxmy1j2r0000gn/T/tmp_9slgdf5\n",
      "  JVM stdout: /var/folders/tc/0ss1l73113j3wdyjsxmy1j2r0000gn/T/tmp_9slgdf5/h2o_phall_started_from_python.out\n",
      "  JVM stderr: /var/folders/tc/0ss1l73113j3wdyjsxmy1j2r0000gn/T/tmp_9slgdf5/h2o_phall_started_from_python.err\n",
      "  Server is running at http://127.0.0.1:54321\n",
      "Connecting to H2O server at http://127.0.0.1:54321... successful.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime:</td>\n",
       "<td>01 secs</td></tr>\n",
       "<tr><td>H2O cluster version:</td>\n",
       "<td>3.12.0.1</td></tr>\n",
       "<tr><td>H2O cluster version age:</td>\n",
       "<td>29 days </td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_phall_oevqha</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>3.556 Gb</td></tr>\n",
       "<tr><td>H2O cluster total cores:</td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O cluster allowed cores:</td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O cluster status:</td>\n",
       "<td>accepting new members, healthy</td></tr>\n",
       "<tr><td>H2O connection url:</td>\n",
       "<td>http://127.0.0.1:54321</td></tr>\n",
       "<tr><td>H2O connection proxy:</td>\n",
       "<td>None</td></tr>\n",
       "<tr><td>H2O internal security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>Python version:</td>\n",
       "<td>3.5.2 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ------------------------------\n",
       "H2O cluster uptime:         01 secs\n",
       "H2O cluster version:        3.12.0.1\n",
       "H2O cluster version age:    29 days\n",
       "H2O cluster name:           H2O_from_python_phall_oevqha\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    3.556 Gb\n",
       "H2O cluster total cores:    8\n",
       "H2O cluster allowed cores:  8\n",
       "H2O cluster status:         accepting new members, healthy\n",
       "H2O connection url:         http://127.0.0.1:54321\n",
       "H2O connection proxy:\n",
       "H2O internal security:      False\n",
       "Python version:             3.5.2 final\n",
       "--------------------------  ------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# start and connect to h2o server\n",
    "h2o.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load clean data\n",
    "path = 'https://raw.githubusercontent.com/h2oai/app-consumer-loan/master/data/loan.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define input variable measurement levels \n",
    "# strings automatically parsed as enums (nominal)\n",
    "# numbers automatically parsed as numeric\n",
    "col_types = {'bad_loan': 'enum'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "frame = h2o.import_file(path=path, col_types=col_types) # import from url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows:163987\n",
      "Cols:15\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>       </th><th>loan_amnt         </th><th>term     </th><th>int_rate          </th><th>emp_length       </th><th>home_ownership  </th><th>annual_inc       </th><th>purpose           </th><th>addr_state  </th><th>dti               </th><th>delinq_2yrs        </th><th>revol_util        </th><th>total_acc         </th><th>bad_loan  </th><th>longest_credit_length  </th><th>verification_status  </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>type   </td><td>int               </td><td>enum     </td><td>real              </td><td>int              </td><td>enum            </td><td>real             </td><td>enum              </td><td>enum        </td><td>real              </td><td>int                </td><td>real              </td><td>int               </td><td>enum      </td><td>int                    </td><td>enum                 </td></tr>\n",
       "<tr><td>mins   </td><td>500.0             </td><td>         </td><td>5.42              </td><td>0.0              </td><td>                </td><td>1896.0           </td><td>                  </td><td>            </td><td>0.0               </td><td>0.0                </td><td>0.0               </td><td>1.0               </td><td>          </td><td>0.0                    </td><td>                     </td></tr>\n",
       "<tr><td>mean   </td><td>13074.169141456332</td><td>         </td><td>13.715904065566189</td><td>5.684352932995338</td><td>                </td><td>71915.67051974905</td><td>                  </td><td>            </td><td>15.881530121290167</td><td>0.22735700606252723</td><td>54.07917280242262 </td><td>24.579733834274574</td><td>          </td><td>14.854273655448333     </td><td>                     </td></tr>\n",
       "<tr><td>maxs   </td><td>35000.0           </td><td>         </td><td>26.060000000000002</td><td>10.0             </td><td>                </td><td>7141778.0        </td><td>                  </td><td>            </td><td>39.99             </td><td>29.0               </td><td>150.70000000000002</td><td>118.0             </td><td>          </td><td>65.0                   </td><td>                     </td></tr>\n",
       "<tr><td>sigma  </td><td>7993.556188734672 </td><td>         </td><td>4.391939870545809 </td><td>3.610663731100238</td><td>                </td><td>59070.91565491818</td><td>                  </td><td>            </td><td>7.5876682241925355</td><td>0.6941679229284191 </td><td>25.285366766770498</td><td>11.685190365910666</td><td>          </td><td>6.947732922546689      </td><td>                     </td></tr>\n",
       "<tr><td>zeros  </td><td>0                 </td><td>         </td><td>0                 </td><td>14248            </td><td>                </td><td>0                </td><td>                  </td><td>            </td><td>270               </td><td>139459             </td><td>1562              </td><td>0                 </td><td>          </td><td>11                     </td><td>                     </td></tr>\n",
       "<tr><td>missing</td><td>0                 </td><td>0        </td><td>0                 </td><td>5804             </td><td>0               </td><td>4                </td><td>0                 </td><td>0           </td><td>0                 </td><td>29                 </td><td>193               </td><td>29                </td><td>0         </td><td>29                     </td><td>0                    </td></tr>\n",
       "<tr><td>0      </td><td>5000.0            </td><td>36 months</td><td>10.65             </td><td>10.0             </td><td>RENT            </td><td>24000.0          </td><td>credit_card       </td><td>AZ          </td><td>27.650000000000002</td><td>0.0                </td><td>83.7              </td><td>9.0               </td><td>0         </td><td>26.0                   </td><td>verified             </td></tr>\n",
       "<tr><td>1      </td><td>2500.0            </td><td>60 months</td><td>15.27             </td><td>0.0              </td><td>RENT            </td><td>30000.0          </td><td>car               </td><td>GA          </td><td>1.0               </td><td>0.0                </td><td>9.4               </td><td>4.0               </td><td>1         </td><td>12.0                   </td><td>verified             </td></tr>\n",
       "<tr><td>2      </td><td>2400.0            </td><td>36 months</td><td>15.96             </td><td>10.0             </td><td>RENT            </td><td>12252.0          </td><td>small_business    </td><td>IL          </td><td>8.72              </td><td>0.0                </td><td>98.5              </td><td>10.0              </td><td>0         </td><td>10.0                   </td><td>not verified         </td></tr>\n",
       "<tr><td>3      </td><td>10000.0           </td><td>36 months</td><td>13.49             </td><td>10.0             </td><td>RENT            </td><td>49200.0          </td><td>other             </td><td>CA          </td><td>20.0              </td><td>0.0                </td><td>21.0              </td><td>37.0              </td><td>0         </td><td>15.0                   </td><td>verified             </td></tr>\n",
       "<tr><td>4      </td><td>5000.0            </td><td>36 months</td><td>7.9               </td><td>3.0              </td><td>RENT            </td><td>36000.0          </td><td>wedding           </td><td>AZ          </td><td>11.200000000000001</td><td>0.0                </td><td>28.3              </td><td>12.0              </td><td>0         </td><td>7.0                    </td><td>verified             </td></tr>\n",
       "<tr><td>5      </td><td>3000.0            </td><td>36 months</td><td>18.64             </td><td>9.0              </td><td>RENT            </td><td>48000.0          </td><td>car               </td><td>CA          </td><td>5.3500000000000005</td><td>0.0                </td><td>87.5              </td><td>4.0               </td><td>0         </td><td>4.0                    </td><td>verified             </td></tr>\n",
       "<tr><td>6      </td><td>5600.0            </td><td>60 months</td><td>21.28             </td><td>4.0              </td><td>OWN             </td><td>40000.0          </td><td>small_business    </td><td>CA          </td><td>5.55              </td><td>0.0                </td><td>32.6              </td><td>13.0              </td><td>1         </td><td>7.0                    </td><td>verified             </td></tr>\n",
       "<tr><td>7      </td><td>5375.0            </td><td>60 months</td><td>12.69             </td><td>0.0              </td><td>RENT            </td><td>15000.0          </td><td>other             </td><td>TX          </td><td>18.080000000000002</td><td>0.0                </td><td>36.5              </td><td>3.0               </td><td>1         </td><td>7.0                    </td><td>verified             </td></tr>\n",
       "<tr><td>8      </td><td>6500.0            </td><td>60 months</td><td>14.65             </td><td>5.0              </td><td>OWN             </td><td>72000.0          </td><td>debt_consolidation</td><td>AZ          </td><td>16.12             </td><td>0.0                </td><td>20.6              </td><td>23.0              </td><td>0         </td><td>13.0                   </td><td>not verified         </td></tr>\n",
       "<tr><td>9      </td><td>12000.0           </td><td>36 months</td><td>12.69             </td><td>10.0             </td><td>OWN             </td><td>75000.0          </td><td>debt_consolidation</td><td>CA          </td><td>10.78             </td><td>0.0                </td><td>67.10000000000001 </td><td>34.0              </td><td>0         </td><td>22.0                   </td><td>verified             </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "frame.describe() # summarize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# split into 40% training, 30% validation, and 30% test\n",
    "train, valid, test = frame.split_frame([0.4, 0.3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bad_loan\n",
      "['loan_amnt', 'term', 'int_rate', 'emp_length', 'home_ownership', 'annual_inc', 'purpose', 'addr_state', 'dti', 'delinq_2yrs', 'revol_util', 'total_acc', 'longest_credit_length', 'verification_status']\n"
     ]
    }
   ],
   "source": [
    "# assign target and inputs\n",
    "y = 'bad_loan'\n",
    "X = [name for name in frame.columns if name != y]\n",
    "print(y)\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['addr_state', 'term', 'purpose', 'home_ownership', 'verification_status']\n",
      "['dti', 'loan_amnt', 'total_acc', 'revol_util', 'emp_length', 'annual_inc', 'delinq_2yrs', 'int_rate', 'longest_credit_length']\n"
     ]
    }
   ],
   "source": [
    "# determine column types\n",
    "reals, enums = [], []\n",
    "for key, val in frame.types.items():\n",
    "    if key in X:\n",
    "        if val == 'enum':\n",
    "            enums.append(key)\n",
    "        else: \n",
    "            reals.append(key)\n",
    "\n",
    "print(enums)\n",
    "print(reals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# impute missing values\n",
    "_ = frame[reals].impute(method='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# set target to factor - for binary classification\n",
    "# just to be safe ...\n",
    "train[y] = train[y].asfactor()\n",
    "valid[y] = valid[y].asfactor()\n",
    "test[y] = test[y].asfactor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n",
      "Model Details\n",
      "=============\n",
      "H2ODeepLearningEstimator :  Deep Learning\n",
      "Model Key:  nn_model\n",
      "\n",
      "\n",
      "ModelMetricsBinomial: deeplearning\n",
      "** Reported on train data. **\n",
      "\n",
      "MSE: 0.13726220105689477\n",
      "RMSE: 0.3704891375693689\n",
      "LogLoss: 0.4370756865457397\n",
      "Mean Per-Class Error: 0.3535416684525945\n",
      "AUC: 0.6936333270332602\n",
      "Gini: 0.3872666540665204\n",
      "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.1590477876705046: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>0</b></td>\n",
       "<td><b>1</b></td>\n",
       "<td><b>Error</b></td>\n",
       "<td><b>Rate</b></td></tr>\n",
       "<tr><td>0</td>\n",
       "<td>5140.0</td>\n",
       "<td>3053.0</td>\n",
       "<td>0.3726</td>\n",
       "<td> (3053.0/8193.0)</td></tr>\n",
       "<tr><td>1</td>\n",
       "<td>600.0</td>\n",
       "<td>1194.0</td>\n",
       "<td>0.3344</td>\n",
       "<td> (600.0/1794.0)</td></tr>\n",
       "<tr><td>Total</td>\n",
       "<td>5740.0</td>\n",
       "<td>4247.0</td>\n",
       "<td>0.3658</td>\n",
       "<td> (3653.0/9987.0)</td></tr></table></div>"
      ],
      "text/plain": [
       "       0     1     Error    Rate\n",
       "-----  ----  ----  -------  ---------------\n",
       "0      5140  3053  0.3726   (3053.0/8193.0)\n",
       "1      600   1194  0.3344   (600.0/1794.0)\n",
       "Total  5740  4247  0.3658   (3653.0/9987.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum Metrics: Maximum metrics at their respective thresholds\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>metric</b></td>\n",
       "<td><b>threshold</b></td>\n",
       "<td><b>value</b></td>\n",
       "<td><b>idx</b></td></tr>\n",
       "<tr><td>max f1</td>\n",
       "<td>0.1590478</td>\n",
       "<td>0.3952988</td>\n",
       "<td>262.0</td></tr>\n",
       "<tr><td>max f2</td>\n",
       "<td>0.1059466</td>\n",
       "<td>0.5582335</td>\n",
       "<td>326.0</td></tr>\n",
       "<tr><td>max f0point5</td>\n",
       "<td>0.2206463</td>\n",
       "<td>0.3480028</td>\n",
       "<td>201.0</td></tr>\n",
       "<tr><td>max accuracy</td>\n",
       "<td>0.4967869</td>\n",
       "<td>0.8217683</td>\n",
       "<td>26.0</td></tr>\n",
       "<tr><td>max precision</td>\n",
       "<td>0.5763859</td>\n",
       "<td>0.6</td>\n",
       "<td>3.0</td></tr>\n",
       "<tr><td>max recall</td>\n",
       "<td>0.0472462</td>\n",
       "<td>1.0</td>\n",
       "<td>397.0</td></tr>\n",
       "<tr><td>max specificity</td>\n",
       "<td>0.6152552</td>\n",
       "<td>0.9998779</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max absolute_mcc</td>\n",
       "<td>0.1906367</td>\n",
       "<td>0.2290269</td>\n",
       "<td>228.0</td></tr>\n",
       "<tr><td>max min_per_class_accuracy</td>\n",
       "<td>0.1633571</td>\n",
       "<td>0.6438127</td>\n",
       "<td>257.0</td></tr>\n",
       "<tr><td>max mean_per_class_accuracy</td>\n",
       "<td>0.1590478</td>\n",
       "<td>0.6464583</td>\n",
       "<td>262.0</td></tr></table></div>"
      ],
      "text/plain": [
       "metric                       threshold    value     idx\n",
       "---------------------------  -----------  --------  -----\n",
       "max f1                       0.159048     0.395299  262\n",
       "max f2                       0.105947     0.558234  326\n",
       "max f0point5                 0.220646     0.348003  201\n",
       "max accuracy                 0.496787     0.821768  26\n",
       "max precision                0.576386     0.6       3\n",
       "max recall                   0.0472462    1         397\n",
       "max specificity              0.615255     0.999878  0\n",
       "max absolute_mcc             0.190637     0.229027  228\n",
       "max min_per_class_accuracy   0.163357     0.643813  257\n",
       "max mean_per_class_accuracy  0.159048     0.646458  262"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gains/Lift Table: Avg response rate: 17.96 %\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>group</b></td>\n",
       "<td><b>cumulative_data_fraction</b></td>\n",
       "<td><b>lower_threshold</b></td>\n",
       "<td><b>lift</b></td>\n",
       "<td><b>cumulative_lift</b></td>\n",
       "<td><b>response_rate</b></td>\n",
       "<td><b>cumulative_response_rate</b></td>\n",
       "<td><b>capture_rate</b></td>\n",
       "<td><b>cumulative_capture_rate</b></td>\n",
       "<td><b>gain</b></td>\n",
       "<td><b>cumulative_gain</b></td></tr>\n",
       "<tr><td></td>\n",
       "<td>1</td>\n",
       "<td>0.0100130</td>\n",
       "<td>0.4949650</td>\n",
       "<td>3.1731271</td>\n",
       "<td>3.1731271</td>\n",
       "<td>0.57</td>\n",
       "<td>0.57</td>\n",
       "<td>0.0317726</td>\n",
       "<td>0.0317726</td>\n",
       "<td>217.3127090</td>\n",
       "<td>217.3127090</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2</td>\n",
       "<td>0.0200260</td>\n",
       "<td>0.4580062</td>\n",
       "<td>2.3937625</td>\n",
       "<td>2.7834448</td>\n",
       "<td>0.43</td>\n",
       "<td>0.5</td>\n",
       "<td>0.0239688</td>\n",
       "<td>0.0557414</td>\n",
       "<td>139.3762542</td>\n",
       "<td>178.3444816</td></tr>\n",
       "<tr><td></td>\n",
       "<td>3</td>\n",
       "<td>0.0300391</td>\n",
       "<td>0.4353030</td>\n",
       "<td>1.8370736</td>\n",
       "<td>2.4679877</td>\n",
       "<td>0.33</td>\n",
       "<td>0.4433333</td>\n",
       "<td>0.0183946</td>\n",
       "<td>0.0741360</td>\n",
       "<td>83.7073579</td>\n",
       "<td>146.7987737</td></tr>\n",
       "<tr><td></td>\n",
       "<td>4</td>\n",
       "<td>0.0400521</td>\n",
       "<td>0.4145941</td>\n",
       "<td>2.8391137</td>\n",
       "<td>2.5607692</td>\n",
       "<td>0.51</td>\n",
       "<td>0.46</td>\n",
       "<td>0.0284281</td>\n",
       "<td>0.1025641</td>\n",
       "<td>183.9113712</td>\n",
       "<td>156.0769231</td></tr>\n",
       "<tr><td></td>\n",
       "<td>5</td>\n",
       "<td>0.0500651</td>\n",
       "<td>0.3977234</td>\n",
       "<td>1.7814047</td>\n",
       "<td>2.4048963</td>\n",
       "<td>0.32</td>\n",
       "<td>0.432</td>\n",
       "<td>0.0178372</td>\n",
       "<td>0.1204013</td>\n",
       "<td>78.1404682</td>\n",
       "<td>140.4896321</td></tr>\n",
       "<tr><td></td>\n",
       "<td>6</td>\n",
       "<td>0.1000300</td>\n",
       "<td>0.3280945</td>\n",
       "<td>1.8965355</td>\n",
       "<td>2.1509704</td>\n",
       "<td>0.3406814</td>\n",
       "<td>0.3863864</td>\n",
       "<td>0.0947603</td>\n",
       "<td>0.2151616</td>\n",
       "<td>89.6535546</td>\n",
       "<td>115.0970368</td></tr>\n",
       "<tr><td></td>\n",
       "<td>7</td>\n",
       "<td>0.1499950</td>\n",
       "<td>0.2804690</td>\n",
       "<td>1.5618528</td>\n",
       "<td>1.9547289</td>\n",
       "<td>0.2805611</td>\n",
       "<td>0.3511348</td>\n",
       "<td>0.0780379</td>\n",
       "<td>0.2931996</td>\n",
       "<td>56.1852803</td>\n",
       "<td>95.4728936</td></tr>\n",
       "<tr><td></td>\n",
       "<td>8</td>\n",
       "<td>0.2000601</td>\n",
       "<td>0.2441262</td>\n",
       "<td>1.6812007</td>\n",
       "<td>1.8862784</td>\n",
       "<td>0.302</td>\n",
       "<td>0.3388388</td>\n",
       "<td>0.0841695</td>\n",
       "<td>0.3773690</td>\n",
       "<td>68.1200669</td>\n",
       "<td>88.6278419</td></tr>\n",
       "<tr><td></td>\n",
       "<td>9</td>\n",
       "<td>0.2999900</td>\n",
       "<td>0.1971468</td>\n",
       "<td>1.4279797</td>\n",
       "<td>1.7336142</td>\n",
       "<td>0.2565130</td>\n",
       "<td>0.3114152</td>\n",
       "<td>0.1426979</td>\n",
       "<td>0.5200669</td>\n",
       "<td>42.7979705</td>\n",
       "<td>73.3614161</td></tr>\n",
       "<tr><td></td>\n",
       "<td>10</td>\n",
       "<td>0.4000200</td>\n",
       "<td>0.1649253</td>\n",
       "<td>1.1312098</td>\n",
       "<td>1.5829754</td>\n",
       "<td>0.2032032</td>\n",
       "<td>0.2843554</td>\n",
       "<td>0.1131550</td>\n",
       "<td>0.6332219</td>\n",
       "<td>13.1209805</td>\n",
       "<td>58.2975375</td></tr>\n",
       "<tr><td></td>\n",
       "<td>11</td>\n",
       "<td>0.5000501</td>\n",
       "<td>0.1410358</td>\n",
       "<td>0.9306012</td>\n",
       "<td>1.4524744</td>\n",
       "<td>0.1671672</td>\n",
       "<td>0.2609131</td>\n",
       "<td>0.0930881</td>\n",
       "<td>0.7263099</td>\n",
       "<td>-6.9398830</td>\n",
       "<td>45.2474407</td></tr>\n",
       "<tr><td></td>\n",
       "<td>12</td>\n",
       "<td>0.5999800</td>\n",
       "<td>0.1211145</td>\n",
       "<td>0.8422849</td>\n",
       "<td>1.3508440</td>\n",
       "<td>0.1513026</td>\n",
       "<td>0.2426569</td>\n",
       "<td>0.0841695</td>\n",
       "<td>0.8104794</td>\n",
       "<td>-15.7715096</td>\n",
       "<td>35.0844046</td></tr>\n",
       "<tr><td></td>\n",
       "<td>13</td>\n",
       "<td>0.7000100</td>\n",
       "<td>0.1048356</td>\n",
       "<td>0.7021302</td>\n",
       "<td>1.2581441</td>\n",
       "<td>0.1261261</td>\n",
       "<td>0.2260049</td>\n",
       "<td>0.0702341</td>\n",
       "<td>0.8807135</td>\n",
       "<td>-29.7869776</td>\n",
       "<td>25.8144131</td></tr>\n",
       "<tr><td></td>\n",
       "<td>14</td>\n",
       "<td>0.7999399</td>\n",
       "<td>0.0897412</td>\n",
       "<td>0.5410704</td>\n",
       "<td>1.1685660</td>\n",
       "<td>0.0971944</td>\n",
       "<td>0.2099136</td>\n",
       "<td>0.0540691</td>\n",
       "<td>0.9347826</td>\n",
       "<td>-45.8929565</td>\n",
       "<td>16.8566017</td></tr>\n",
       "<tr><td></td>\n",
       "<td>15</td>\n",
       "<td>0.8999700</td>\n",
       "<td>0.0747443</td>\n",
       "<td>0.3287753</td>\n",
       "<td>1.0752248</td>\n",
       "<td>0.0590591</td>\n",
       "<td>0.1931464</td>\n",
       "<td>0.0328874</td>\n",
       "<td>0.9676700</td>\n",
       "<td>-67.1224736</td>\n",
       "<td>7.5224789</td></tr>\n",
       "<tr><td></td>\n",
       "<td>16</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0410339</td>\n",
       "<td>0.3232028</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0580581</td>\n",
       "<td>0.1796335</td>\n",
       "<td>0.0323300</td>\n",
       "<td>1.0</td>\n",
       "<td>-67.6797199</td>\n",
       "<td>0.0</td></tr></table></div>"
      ],
      "text/plain": [
       "    group    cumulative_data_fraction    lower_threshold    lift      cumulative_lift    response_rate    cumulative_response_rate    capture_rate    cumulative_capture_rate    gain      cumulative_gain\n",
       "--  -------  --------------------------  -----------------  --------  -----------------  ---------------  --------------------------  --------------  -------------------------  --------  -----------------\n",
       "    1        0.010013                    0.494965           3.17313   3.17313            0.57             0.57                        0.0317726       0.0317726                  217.313   217.313\n",
       "    2        0.020026                    0.458006           2.39376   2.78344            0.43             0.5                         0.0239688       0.0557414                  139.376   178.344\n",
       "    3        0.0300391                   0.435303           1.83707   2.46799            0.33             0.443333                    0.0183946       0.074136                   83.7074   146.799\n",
       "    4        0.0400521                   0.414594           2.83911   2.56077            0.51             0.46                        0.0284281       0.102564                   183.911   156.077\n",
       "    5        0.0500651                   0.397723           1.7814    2.4049             0.32             0.432                       0.0178372       0.120401                   78.1405   140.49\n",
       "    6        0.10003                     0.328094           1.89654   2.15097            0.340681         0.386386                    0.0947603       0.215162                   89.6536   115.097\n",
       "    7        0.149995                    0.280469           1.56185   1.95473            0.280561         0.351135                    0.0780379       0.2932                     56.1853   95.4729\n",
       "    8        0.20006                     0.244126           1.6812    1.88628            0.302            0.338839                    0.0841695       0.377369                   68.1201   88.6278\n",
       "    9        0.29999                     0.197147           1.42798   1.73361            0.256513         0.311415                    0.142698        0.520067                   42.798    73.3614\n",
       "    10       0.40002                     0.164925           1.13121   1.58298            0.203203         0.284355                    0.113155        0.633222                   13.121    58.2975\n",
       "    11       0.50005                     0.141036           0.930601  1.45247            0.167167         0.260913                    0.0930881       0.72631                    -6.93988  45.2474\n",
       "    12       0.59998                     0.121114           0.842285  1.35084            0.151303         0.242657                    0.0841695       0.810479                   -15.7715  35.0844\n",
       "    13       0.70001                     0.104836           0.70213   1.25814            0.126126         0.226005                    0.0702341       0.880713                   -29.787   25.8144\n",
       "    14       0.79994                     0.0897412          0.54107   1.16857            0.0971944        0.209914                    0.0540691       0.934783                   -45.893   16.8566\n",
       "    15       0.89997                     0.0747443          0.328775  1.07522            0.0590591        0.193146                    0.0328874       0.96767                    -67.1225  7.52248\n",
       "    16       1                           0.0410339          0.323203  1                  0.0580581        0.179634                    0.03233         1                          -67.6797  0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "ModelMetricsBinomial: deeplearning\n",
      "** Reported on validation data. **\n",
      "\n",
      "MSE: 0.1376869934004904\n",
      "RMSE: 0.3710619805376056\n",
      "LogLoss: 0.4379198829084778\n",
      "Mean Per-Class Error: 0.3534463552679302\n",
      "AUC: 0.6986077275726538\n",
      "Gini: 0.39721545514530754\n",
      "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.17079314843567872: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>0</b></td>\n",
       "<td><b>1</b></td>\n",
       "<td><b>Error</b></td>\n",
       "<td><b>Rate</b></td></tr>\n",
       "<tr><td>0</td>\n",
       "<td>27130.0</td>\n",
       "<td>13234.0</td>\n",
       "<td>0.3279</td>\n",
       "<td> (13234.0/40364.0)</td></tr>\n",
       "<tr><td>1</td>\n",
       "<td>3411.0</td>\n",
       "<td>5571.0</td>\n",
       "<td>0.3798</td>\n",
       "<td> (3411.0/8982.0)</td></tr>\n",
       "<tr><td>Total</td>\n",
       "<td>30541.0</td>\n",
       "<td>18805.0</td>\n",
       "<td>0.3373</td>\n",
       "<td> (16645.0/49346.0)</td></tr></table></div>"
      ],
      "text/plain": [
       "       0      1      Error    Rate\n",
       "-----  -----  -----  -------  -----------------\n",
       "0      27130  13234  0.3279   (13234.0/40364.0)\n",
       "1      3411   5571   0.3798   (3411.0/8982.0)\n",
       "Total  30541  18805  0.3373   (16645.0/49346.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum Metrics: Maximum metrics at their respective thresholds\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>metric</b></td>\n",
       "<td><b>threshold</b></td>\n",
       "<td><b>value</b></td>\n",
       "<td><b>idx</b></td></tr>\n",
       "<tr><td>max f1</td>\n",
       "<td>0.1707931</td>\n",
       "<td>0.4009789</td>\n",
       "<td>247.0</td></tr>\n",
       "<tr><td>max f2</td>\n",
       "<td>0.1125146</td>\n",
       "<td>0.5593188</td>\n",
       "<td>316.0</td></tr>\n",
       "<tr><td>max f0point5</td>\n",
       "<td>0.2687035</td>\n",
       "<td>0.3660425</td>\n",
       "<td>159.0</td></tr>\n",
       "<tr><td>max accuracy</td>\n",
       "<td>0.5386495</td>\n",
       "<td>0.8185061</td>\n",
       "<td>12.0</td></tr>\n",
       "<tr><td>max precision</td>\n",
       "<td>0.6166139</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max recall</td>\n",
       "<td>0.0415721</td>\n",
       "<td>1.0</td>\n",
       "<td>399.0</td></tr>\n",
       "<tr><td>max specificity</td>\n",
       "<td>0.6166139</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max absolute_mcc</td>\n",
       "<td>0.2140907</td>\n",
       "<td>0.2333419</td>\n",
       "<td>203.0</td></tr>\n",
       "<tr><td>max min_per_class_accuracy</td>\n",
       "<td>0.1637622</td>\n",
       "<td>0.6448320</td>\n",
       "<td>255.0</td></tr>\n",
       "<tr><td>max mean_per_class_accuracy</td>\n",
       "<td>0.1689692</td>\n",
       "<td>0.6465536</td>\n",
       "<td>249.0</td></tr></table></div>"
      ],
      "text/plain": [
       "metric                       threshold    value     idx\n",
       "---------------------------  -----------  --------  -----\n",
       "max f1                       0.170793     0.400979  247\n",
       "max f2                       0.112515     0.559319  316\n",
       "max f0point5                 0.268703     0.366042  159\n",
       "max accuracy                 0.53865      0.818506  12\n",
       "max precision                0.616614     1         0\n",
       "max recall                   0.0415721    1         399\n",
       "max specificity              0.616614     1         0\n",
       "max absolute_mcc             0.214091     0.233342  203\n",
       "max min_per_class_accuracy   0.163762     0.644832  255\n",
       "max mean_per_class_accuracy  0.168969     0.646554  249"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gains/Lift Table: Avg response rate: 18.20 %\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>group</b></td>\n",
       "<td><b>cumulative_data_fraction</b></td>\n",
       "<td><b>lower_threshold</b></td>\n",
       "<td><b>lift</b></td>\n",
       "<td><b>cumulative_lift</b></td>\n",
       "<td><b>response_rate</b></td>\n",
       "<td><b>cumulative_response_rate</b></td>\n",
       "<td><b>capture_rate</b></td>\n",
       "<td><b>cumulative_capture_rate</b></td>\n",
       "<td><b>gain</b></td>\n",
       "<td><b>cumulative_gain</b></td></tr>\n",
       "<tr><td></td>\n",
       "<td>1</td>\n",
       "<td>0.0100109</td>\n",
       "<td>0.4910842</td>\n",
       "<td>2.8359080</td>\n",
       "<td>2.8359080</td>\n",
       "<td>0.5161943</td>\n",
       "<td>0.5161943</td>\n",
       "<td>0.0283901</td>\n",
       "<td>0.0283901</td>\n",
       "<td>183.5907983</td>\n",
       "<td>183.5907983</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2</td>\n",
       "<td>0.0200016</td>\n",
       "<td>0.4604492</td>\n",
       "<td>2.4516285</td>\n",
       "<td>2.6439629</td>\n",
       "<td>0.4462475</td>\n",
       "<td>0.4812563</td>\n",
       "<td>0.0244934</td>\n",
       "<td>0.0528835</td>\n",
       "<td>145.1628522</td>\n",
       "<td>164.3962923</td></tr>\n",
       "<tr><td></td>\n",
       "<td>3</td>\n",
       "<td>0.0300126</td>\n",
       "<td>0.4358523</td>\n",
       "<td>2.2798476</td>\n",
       "<td>2.5225092</td>\n",
       "<td>0.4149798</td>\n",
       "<td>0.4591492</td>\n",
       "<td>0.0228234</td>\n",
       "<td>0.0757070</td>\n",
       "<td>127.9847594</td>\n",
       "<td>152.2509194</td></tr>\n",
       "<tr><td></td>\n",
       "<td>4</td>\n",
       "<td>0.0400032</td>\n",
       "<td>0.4146525</td>\n",
       "<td>2.4627723</td>\n",
       "<td>2.5075901</td>\n",
       "<td>0.4482759</td>\n",
       "<td>0.4564336</td>\n",
       "<td>0.0246048</td>\n",
       "<td>0.1003117</td>\n",
       "<td>146.2772288</td>\n",
       "<td>150.7590099</td></tr>\n",
       "<tr><td></td>\n",
       "<td>5</td>\n",
       "<td>0.0500142</td>\n",
       "<td>0.3976686</td>\n",
       "<td>2.3354536</td>\n",
       "<td>2.4731349</td>\n",
       "<td>0.4251012</td>\n",
       "<td>0.4501621</td>\n",
       "<td>0.0233801</td>\n",
       "<td>0.1236918</td>\n",
       "<td>133.5453633</td>\n",
       "<td>147.3134907</td></tr>\n",
       "<tr><td></td>\n",
       "<td>6</td>\n",
       "<td>0.1000081</td>\n",
       "<td>0.3249227</td>\n",
       "<td>1.9641667</td>\n",
       "<td>2.2187024</td>\n",
       "<td>0.3575193</td>\n",
       "<td>0.4038501</td>\n",
       "<td>0.0981964</td>\n",
       "<td>0.2218882</td>\n",
       "<td>96.4166680</td>\n",
       "<td>121.8702360</td></tr>\n",
       "<tr><td></td>\n",
       "<td>7</td>\n",
       "<td>0.1500020</td>\n",
       "<td>0.2784483</td>\n",
       "<td>1.7949188</td>\n",
       "<td>2.0774602</td>\n",
       "<td>0.3267126</td>\n",
       "<td>0.3781410</td>\n",
       "<td>0.0897350</td>\n",
       "<td>0.3116232</td>\n",
       "<td>79.4918757</td>\n",
       "<td>107.7460243</td></tr>\n",
       "<tr><td></td>\n",
       "<td>8</td>\n",
       "<td>0.2000162</td>\n",
       "<td>0.2440977</td>\n",
       "<td>1.5982996</td>\n",
       "<td>1.9576458</td>\n",
       "<td>0.2909238</td>\n",
       "<td>0.3563323</td>\n",
       "<td>0.0799377</td>\n",
       "<td>0.3915609</td>\n",
       "<td>59.8299607</td>\n",
       "<td>95.7645811</td></tr>\n",
       "<tr><td></td>\n",
       "<td>9</td>\n",
       "<td>0.3000041</td>\n",
       "<td>0.1967167</td>\n",
       "<td>1.3228061</td>\n",
       "<td>1.7460612</td>\n",
       "<td>0.2407783</td>\n",
       "<td>0.3178195</td>\n",
       "<td>0.1322645</td>\n",
       "<td>0.5238254</td>\n",
       "<td>32.2806131</td>\n",
       "<td>74.6061173</td></tr>\n",
       "<tr><td></td>\n",
       "<td>10</td>\n",
       "<td>0.4000122</td>\n",
       "<td>0.1654110</td>\n",
       "<td>1.1577774</td>\n",
       "<td>1.5989828</td>\n",
       "<td>0.2107396</td>\n",
       "<td>0.2910482</td>\n",
       "<td>0.1157871</td>\n",
       "<td>0.6396126</td>\n",
       "<td>15.7777448</td>\n",
       "<td>59.8982791</td></tr>\n",
       "<tr><td></td>\n",
       "<td>11</td>\n",
       "<td>0.5</td>\n",
       "<td>0.1422269</td>\n",
       "<td>0.9074806</td>\n",
       "<td>1.4606992</td>\n",
       "<td>0.1651804</td>\n",
       "<td>0.2658777</td>\n",
       "<td>0.0907370</td>\n",
       "<td>0.7303496</td>\n",
       "<td>-9.2519363</td>\n",
       "<td>46.0699176</td></tr>\n",
       "<tr><td></td>\n",
       "<td>12</td>\n",
       "<td>0.6000081</td>\n",
       "<td>0.1229131</td>\n",
       "<td>0.7870660</td>\n",
       "<td>1.3484194</td>\n",
       "<td>0.1432624</td>\n",
       "<td>0.2454404</td>\n",
       "<td>0.0787130</td>\n",
       "<td>0.8090626</td>\n",
       "<td>-21.2933985</td>\n",
       "<td>34.8419399</td></tr>\n",
       "<tr><td></td>\n",
       "<td>13</td>\n",
       "<td>0.6999959</td>\n",
       "<td>0.1060618</td>\n",
       "<td>0.6736513</td>\n",
       "<td>1.2520351</td>\n",
       "<td>0.1226186</td>\n",
       "<td>0.2278965</td>\n",
       "<td>0.0673569</td>\n",
       "<td>0.8764195</td>\n",
       "<td>-32.6348730</td>\n",
       "<td>25.2035115</td></tr>\n",
       "<tr><td></td>\n",
       "<td>14</td>\n",
       "<td>0.8000041</td>\n",
       "<td>0.0903467</td>\n",
       "<td>0.5499443</td>\n",
       "<td>1.1642671</td>\n",
       "<td>0.1001013</td>\n",
       "<td>0.2119209</td>\n",
       "<td>0.0549989</td>\n",
       "<td>0.9314184</td>\n",
       "<td>-45.0055712</td>\n",
       "<td>16.4267092</td></tr>\n",
       "<tr><td></td>\n",
       "<td>15</td>\n",
       "<td>0.8999919</td>\n",
       "<td>0.0751876</td>\n",
       "<td>0.4298006</td>\n",
       "<td>1.0826689</td>\n",
       "<td>0.0782327</td>\n",
       "<td>0.1970683</td>\n",
       "<td>0.0429748</td>\n",
       "<td>0.9743932</td>\n",
       "<td>-57.0199355</td>\n",
       "<td>8.2668897</td></tr>\n",
       "<tr><td></td>\n",
       "<td>16</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0410154</td>\n",
       "<td>0.2560469</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0466059</td>\n",
       "<td>0.1820208</td>\n",
       "<td>0.0256068</td>\n",
       "<td>1.0</td>\n",
       "<td>-74.3953064</td>\n",
       "<td>0.0</td></tr></table></div>"
      ],
      "text/plain": [
       "    group    cumulative_data_fraction    lower_threshold    lift      cumulative_lift    response_rate    cumulative_response_rate    capture_rate    cumulative_capture_rate    gain      cumulative_gain\n",
       "--  -------  --------------------------  -----------------  --------  -----------------  ---------------  --------------------------  --------------  -------------------------  --------  -----------------\n",
       "    1        0.0100109                   0.491084           2.83591   2.83591            0.516194         0.516194                    0.0283901       0.0283901                  183.591   183.591\n",
       "    2        0.0200016                   0.460449           2.45163   2.64396            0.446247         0.481256                    0.0244934       0.0528835                  145.163   164.396\n",
       "    3        0.0300126                   0.435852           2.27985   2.52251            0.41498          0.459149                    0.0228234       0.075707                   127.985   152.251\n",
       "    4        0.0400032                   0.414652           2.46277   2.50759            0.448276         0.456434                    0.0246048       0.100312                   146.277   150.759\n",
       "    5        0.0500142                   0.397669           2.33545   2.47313            0.425101         0.450162                    0.0233801       0.123692                   133.545   147.313\n",
       "    6        0.100008                    0.324923           1.96417   2.2187             0.357519         0.40385                     0.0981964       0.221888                   96.4167   121.87\n",
       "    7        0.150002                    0.278448           1.79492   2.07746            0.326713         0.378141                    0.089735        0.311623                   79.4919   107.746\n",
       "    8        0.200016                    0.244098           1.5983    1.95765            0.290924         0.356332                    0.0799377       0.391561                   59.83     95.7646\n",
       "    9        0.300004                    0.196717           1.32281   1.74606            0.240778         0.31782                     0.132265        0.523825                   32.2806   74.6061\n",
       "    10       0.400012                    0.165411           1.15778   1.59898            0.21074          0.291048                    0.115787        0.639613                   15.7777   59.8983\n",
       "    11       0.5                         0.142227           0.907481  1.4607             0.16518          0.265878                    0.090737        0.73035                    -9.25194  46.0699\n",
       "    12       0.600008                    0.122913           0.787066  1.34842            0.143262         0.24544                     0.078713        0.809063                   -21.2934  34.8419\n",
       "    13       0.699996                    0.106062           0.673651  1.25204            0.122619         0.227896                    0.0673569       0.87642                    -32.6349  25.2035\n",
       "    14       0.800004                    0.0903467          0.549944  1.16427            0.100101         0.211921                    0.0549989       0.931418                   -45.0056  16.4267\n",
       "    15       0.899992                    0.0751876          0.429801  1.08267            0.0782327        0.197068                    0.0429748       0.974393                   -57.0199  8.26689\n",
       "    16       1                           0.0410154          0.256047  1                  0.0466059        0.182021                    0.0256068       1                          -74.3953  0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Scoring History: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>timestamp</b></td>\n",
       "<td><b>duration</b></td>\n",
       "<td><b>training_speed</b></td>\n",
       "<td><b>epochs</b></td>\n",
       "<td><b>iterations</b></td>\n",
       "<td><b>samples</b></td>\n",
       "<td><b>training_rmse</b></td>\n",
       "<td><b>training_logloss</b></td>\n",
       "<td><b>training_auc</b></td>\n",
       "<td><b>training_lift</b></td>\n",
       "<td><b>training_classification_error</b></td>\n",
       "<td><b>validation_rmse</b></td>\n",
       "<td><b>validation_logloss</b></td>\n",
       "<td><b>validation_auc</b></td>\n",
       "<td><b>validation_lift</b></td>\n",
       "<td><b>validation_classification_error</b></td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-07-06 17:31:06</td>\n",
       "<td> 0.000 sec</td>\n",
       "<td>None</td>\n",
       "<td>0.0</td>\n",
       "<td>0</td>\n",
       "<td>0.0</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-07-06 17:31:09</td>\n",
       "<td> 3.294 sec</td>\n",
       "<td>69672 obs/sec</td>\n",
       "<td>1.5304249</td>\n",
       "<td>1</td>\n",
       "<td>100050.0</td>\n",
       "<td>0.3720781</td>\n",
       "<td>0.4396404</td>\n",
       "<td>0.6898352</td>\n",
       "<td>2.8947826</td>\n",
       "<td>0.3302293</td>\n",
       "<td>0.3721194</td>\n",
       "<td>0.4396922</td>\n",
       "<td>0.6951212</td>\n",
       "<td>2.6690899</td>\n",
       "<td>0.3196814</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-07-06 17:31:11</td>\n",
       "<td> 4.997 sec</td>\n",
       "<td>73710 obs/sec</td>\n",
       "<td>3.0612170</td>\n",
       "<td>2</td>\n",
       "<td>200124.0</td>\n",
       "<td>0.3724915</td>\n",
       "<td>0.4426069</td>\n",
       "<td>0.6909704</td>\n",
       "<td>3.1174582</td>\n",
       "<td>0.3146090</td>\n",
       "<td>0.3742821</td>\n",
       "<td>0.4458793</td>\n",
       "<td>0.6924385</td>\n",
       "<td>2.8025444</td>\n",
       "<td>0.3079277</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-07-06 17:31:12</td>\n",
       "<td> 6.506 sec</td>\n",
       "<td>77125 obs/sec</td>\n",
       "<td>4.5916113</td>\n",
       "<td>3</td>\n",
       "<td>300172.0</td>\n",
       "<td>0.3715599</td>\n",
       "<td>0.4396212</td>\n",
       "<td>0.6929769</td>\n",
       "<td>2.9504515</td>\n",
       "<td>0.3340342</td>\n",
       "<td>0.3720627</td>\n",
       "<td>0.4402496</td>\n",
       "<td>0.6986440</td>\n",
       "<td>2.7803019</td>\n",
       "<td>0.3349410</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-07-06 17:31:15</td>\n",
       "<td> 9.756 sec</td>\n",
       "<td>70280 obs/sec</td>\n",
       "<td>6.1213326</td>\n",
       "<td>4</td>\n",
       "<td>400176.0</td>\n",
       "<td>0.3757477</td>\n",
       "<td>0.4520945</td>\n",
       "<td>0.6885250</td>\n",
       "<td>2.9504515</td>\n",
       "<td>0.3528587</td>\n",
       "<td>0.3771811</td>\n",
       "<td>0.4545032</td>\n",
       "<td>0.6928438</td>\n",
       "<td>2.7246959</td>\n",
       "<td>0.3162364</td></tr>\n",
       "<tr><td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-07-06 17:31:42</td>\n",
       "<td>36.525 sec</td>\n",
       "<td>77746 obs/sec</td>\n",
       "<td>32.1311531</td>\n",
       "<td>21</td>\n",
       "<td>2100542.0</td>\n",
       "<td>0.3727413</td>\n",
       "<td>0.4429178</td>\n",
       "<td>0.6902204</td>\n",
       "<td>2.7834448</td>\n",
       "<td>0.3113047</td>\n",
       "<td>0.3730032</td>\n",
       "<td>0.4433122</td>\n",
       "<td>0.6962201</td>\n",
       "<td>2.7914232</td>\n",
       "<td>0.3219309</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-07-06 17:31:44</td>\n",
       "<td>38.092 sec</td>\n",
       "<td>77855 obs/sec</td>\n",
       "<td>33.6650809</td>\n",
       "<td>22</td>\n",
       "<td>2200821.0</td>\n",
       "<td>0.3708046</td>\n",
       "<td>0.4382515</td>\n",
       "<td>0.6919762</td>\n",
       "<td>3.2844649</td>\n",
       "<td>0.3205167</td>\n",
       "<td>0.3719537</td>\n",
       "<td>0.4401377</td>\n",
       "<td>0.6957829</td>\n",
       "<td>2.9359989</td>\n",
       "<td>0.3176752</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-07-06 17:31:46</td>\n",
       "<td>39.654 sec</td>\n",
       "<td>77942 obs/sec</td>\n",
       "<td>35.1928595</td>\n",
       "<td>23</td>\n",
       "<td>2300698.0</td>\n",
       "<td>0.3711444</td>\n",
       "<td>0.4386998</td>\n",
       "<td>0.6889364</td>\n",
       "<td>3.1174582</td>\n",
       "<td>0.3523581</td>\n",
       "<td>0.3719498</td>\n",
       "<td>0.4400195</td>\n",
       "<td>0.6928548</td>\n",
       "<td>2.6802111</td>\n",
       "<td>0.3368459</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-07-06 17:31:47</td>\n",
       "<td>41.263 sec</td>\n",
       "<td>77951 obs/sec</td>\n",
       "<td>36.7198733</td>\n",
       "<td>24</td>\n",
       "<td>2400525.0</td>\n",
       "<td>0.3752984</td>\n",
       "<td>0.4506241</td>\n",
       "<td>0.6926611</td>\n",
       "<td>3.2844649</td>\n",
       "<td>0.3158106</td>\n",
       "<td>0.3759548</td>\n",
       "<td>0.4517770</td>\n",
       "<td>0.6965470</td>\n",
       "<td>2.7914232</td>\n",
       "<td>0.3349208</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-07-06 17:31:47</td>\n",
       "<td>41.584 sec</td>\n",
       "<td>77934 obs/sec</td>\n",
       "<td>36.7198733</td>\n",
       "<td>24</td>\n",
       "<td>2400525.0</td>\n",
       "<td>0.3704891</td>\n",
       "<td>0.4370757</td>\n",
       "<td>0.6936333</td>\n",
       "<td>3.1731271</td>\n",
       "<td>0.3657755</td>\n",
       "<td>0.3710620</td>\n",
       "<td>0.4379199</td>\n",
       "<td>0.6986077</td>\n",
       "<td>2.8359080</td>\n",
       "<td>0.3373120</td></tr></table></div>"
      ],
      "text/plain": [
       "     timestamp            duration    training_speed    epochs              iterations    samples    training_rmse        training_logloss     training_auc        training_lift       training_classification_error    validation_rmse      validation_logloss    validation_auc      validation_lift     validation_classification_error\n",
       "---  -------------------  ----------  ----------------  ------------------  ------------  ---------  -------------------  -------------------  ------------------  ------------------  -------------------------------  -------------------  --------------------  ------------------  ------------------  ---------------------------------\n",
       "     2017-07-06 17:31:06  0.000 sec                     0.0                 0             0.0        nan                  nan                  nan                 nan                 nan                              nan                  nan                   nan                 nan                 nan\n",
       "     2017-07-06 17:31:09  3.294 sec   69672 obs/sec     1.5304249395784257  1             100050.0   0.3720780831614838   0.4396404070766485   0.6898351857317359  2.8947826086956523  0.3302292980875138               0.37211939122348037  0.43969218120745496   0.6951212169546593  2.6690898666428673  0.3196814331455437\n",
       "     2017-07-06 17:31:11  4.997 sec   73710 obs/sec     3.061216997583137   2             200124.0   0.37249147221987233  0.4426069424181255   0.6909703895200529  3.1174581939799335  0.3146089916891959               0.37428212640124375  0.445879283526428     0.6924385042781805  2.802544359975011   0.30792769424066796\n",
       "     2017-07-06 17:31:12  6.506 sec   77125 obs/sec     4.591611343959372   3             300172.0   0.3715598907781696   0.4396211832489188   0.6929768879842909  2.950451505016723   0.3340342445178732               0.3720626789778633   0.44024955454921305   0.6986440302068809  2.7803019444196537  0.33494102865480485\n",
       "     2017-07-06 17:31:15  9.756 sec   70280 obs/sec     6.121332639887417   4             400176.0   0.3757477038125849   0.4520944963174825   0.6885249950300179  2.950451505016723   0.3528587163312306               0.37718112642099705  0.4545031888566039    0.692843768996719   2.7246959055312607  0.31623637174239044\n",
       "---  ---                  ---         ---               ---                 ---           ---        ---                  ---                  ---                 ---                 ---                              ---                  ---                   ---                 ---                 ---\n",
       "     2017-07-06 17:31:42  36.525 sec  77746 obs/sec     32.131153057790556  21            2100542.0  0.3727412930703484   0.4429178085591974   0.6902203678507947  2.7834448160535117  0.31130469610493644              0.3730032068852304   0.4433121786991078    0.6962201125734441  2.791423152197332   0.321930855591132\n",
       "     2017-07-06 17:31:44  38.092 sec  77855 obs/sec     33.66508091901979   22            2200821.0  0.3708046095754872   0.43825149491224447  0.6919761900776977  3.2844648829431438  0.3205166716731751               0.3719536693550059   0.4401376562118971    0.695782919796364   2.9359988533071544  0.3176751915048839\n",
       "     2017-07-06 17:31:46  39.654 sec  77942 obs/sec     35.192859546608744  23            2300698.0  0.3711443592305464   0.43869980312673956  0.688936370757809   3.1174581939799335  0.35235806548513066              0.3719497897012814   0.44001948148138853   0.692854782666777   2.680211074420546   0.3368459449600778\n",
       "     2017-07-06 17:31:47  41.263 sec  77951 obs/sec     36.71987334414293   24            2400525.0  0.3752984415964589   0.4506240607048924   0.6926611359372095  3.2844648829431438  0.3158105537198358               0.3759548175318367   0.4517770093468979    0.6965470293585994  2.791423152197332   0.3349207635877275\n",
       "     2017-07-06 17:31:47  41.584 sec  77934 obs/sec     36.71987334414293   24            2400525.0  0.3704891375693689   0.4370756865457397   0.6936333270332602  3.173127090301003   0.3657755081606088               0.3710619805376056   0.4379198829084778    0.6986077275726538  2.8359079833080467  0.3373120415028574"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "See the whole table with table.as_data_frame()\n",
      "Variable Importances: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>variable</b></td>\n",
       "<td><b>relative_importance</b></td>\n",
       "<td><b>scaled_importance</b></td>\n",
       "<td><b>percentage</b></td></tr>\n",
       "<tr><td>purpose.small_business</td>\n",
       "<td>1.0</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0478510</td></tr>\n",
       "<tr><td>addr_state.WV</td>\n",
       "<td>0.7957225</td>\n",
       "<td>0.7957225</td>\n",
       "<td>0.0380761</td></tr>\n",
       "<tr><td>addr_state.DC</td>\n",
       "<td>0.7101347</td>\n",
       "<td>0.7101347</td>\n",
       "<td>0.0339806</td></tr>\n",
       "<tr><td>addr_state.MT</td>\n",
       "<td>0.6937076</td>\n",
       "<td>0.6937076</td>\n",
       "<td>0.0331946</td></tr>\n",
       "<tr><td>int_rate</td>\n",
       "<td>0.6142963</td>\n",
       "<td>0.6142963</td>\n",
       "<td>0.0293947</td></tr>\n",
       "<tr><td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td></tr>\n",
       "<tr><td>addr_state.missing(NA)</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>purpose.missing(NA)</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>home_ownership.missing(NA)</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>term.missing(NA)</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>verification_status.missing(NA)</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td></tr></table></div>"
      ],
      "text/plain": [
       "variable                         relative_importance    scaled_importance    percentage\n",
       "-------------------------------  ---------------------  -------------------  --------------------\n",
       "purpose.small_business           1.0                    1.0                  0.04785098010501216\n",
       "addr_state.WV                    0.7957225441932678     0.7957225441932678   0.03807610363130172\n",
       "addr_state.DC                    0.710134744644165      0.710134744644165    0.033980643537845835\n",
       "addr_state.MT                    0.6937076449394226     0.6937076449394226   0.033194590716691154\n",
       "int_rate                         0.6142963171005249     0.6142963171005249   0.02939468084815946\n",
       "---                              ---                    ---                  ---\n",
       "addr_state.missing(NA)           0.0                    0.0                  0.0\n",
       "purpose.missing(NA)              0.0                    0.0                  0.0\n",
       "home_ownership.missing(NA)       0.0                    0.0                  0.0\n",
       "term.missing(NA)                 0.0                    0.0                  0.0\n",
       "verification_status.missing(NA)  0.0                    0.0                  0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "See the whole table with table.as_data_frame()\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# neural network\n",
    "\n",
    "# initialize nn model\n",
    "nn_model = H2ODeepLearningEstimator(\n",
    "    epochs=50,                    # read over the data 50 times, but in mini-batches\n",
    "    hidden=[100],                 # 100 hidden units in 1 hidden layer\n",
    "    input_dropout_ratio=0.2,      # randomly drop 20% of inputs for each iteration, helps w/ generalization\n",
    "    hidden_dropout_ratios=[0.05], # randomly set 5% of hidden weights to 0 each iteration, helps w/ generalization\n",
    "    activation='TanhWithDropout', # bounded activation function that allows for dropout, tanh\n",
    "    l1=0.001,                     # L1 penalty can help generalization   \n",
    "    l2=0.01,                      # L2 penalty can increase stability in presence of highly correlated inputs\n",
    "    adaptive_rate=True,           # adjust magnitude of weight updates automatically (+stability, +accuracy)\n",
    "    stopping_rounds=5,            # stop after validation error does not decrease for 5 iterations\n",
    "    score_each_iteration=True,    # score validation error on every iteration\n",
    "    model_id='nn_model')          # for easy lookup in flow\n",
    "\n",
    "# train nn model\n",
    "nn_model.train(\n",
    "    x=X,\n",
    "    y=y,\n",
    "    training_frame=train,\n",
    "    validation_frame=valid)\n",
    "\n",
    "# print model information\n",
    "nn_model\n",
    "\n",
    "# view detailed results at http://localhost:54321/flow/index.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6936333270332602\n",
      "0.6986077275726538\n",
      "0.6910466932655376\n"
     ]
    }
   ],
   "source": [
    "# measure nn AUC\n",
    "print(nn_model.auc(train=True))\n",
    "print(nn_model.auc(valid=True))\n",
    "print(nn_model.model_performance(test_data=test).auc())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning Grid Build progress: |███████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "# NN with random hyperparameter search\n",
    "# train many different NN models with random hyperparameters\n",
    "# and select best model based on validation error\n",
    "\n",
    "# define random grid search parameters\n",
    "hyper_parameters = {'hidden':[[170, 320], [80, 190], [320, 160, 80], [100], [50, 50, 50, 50]],\n",
    "                    'l1':[s/1e4 for s in range(0, 1000, 100)],\n",
    "                    'l2':[s/1e5 for s in range(0, 1000, 100)],\n",
    "                    'input_dropout_ratio':[s/1e2 for s in range(0, 20, 2)]}\n",
    "\n",
    "# define search strategy\n",
    "search_criteria = {'strategy':'RandomDiscrete',\n",
    "                   'max_models':20,\n",
    "                   'max_runtime_secs':600}\n",
    "\n",
    "# initialize grid search\n",
    "gsearch = H2OGridSearch(H2ODeepLearningEstimator,\n",
    "                        hyper_params=hyper_parameters,\n",
    "                        search_criteria=search_criteria)\n",
    "\n",
    "# execute training w/ grid search\n",
    "gsearch.train(x=X,\n",
    "              y=y,\n",
    "              training_frame=train,\n",
    "              validation_frame=valid)\n",
    "\n",
    "# view detailed results at http://ip:port/flow/index.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                hidden input_dropout_ratio    l1     l2  \\\n",
      "0     [50, 50, 50, 50]                 0.0   0.0  0.009   \n",
      "1                [100]                0.02  0.02  0.003   \n",
      "2     [50, 50, 50, 50]                 0.1  0.02  0.002   \n",
      "3                [100]                0.14  0.04  0.009   \n",
      "4       [320, 160, 80]                0.18  0.06  0.008   \n",
      "5       [320, 160, 80]                0.12  0.06  0.005   \n",
      "6       [320, 160, 80]                0.06  0.03  0.005   \n",
      "7            [80, 190]                0.18  0.05  0.002   \n",
      "8       [320, 160, 80]                0.06  0.08  0.004   \n",
      "9     [50, 50, 50, 50]                0.08  0.04  0.004   \n",
      "10    [50, 50, 50, 50]                0.02  0.06  0.004   \n",
      "11    [50, 50, 50, 50]                0.14  0.03    0.0   \n",
      "12           [80, 190]                0.02  0.07    0.0   \n",
      "13           [80, 190]                0.18  0.05  0.003   \n",
      "14           [80, 190]                 0.0  0.05  0.004   \n",
      "15          [170, 320]                0.06  0.09  0.001   \n",
      "16           [80, 190]                0.02  0.05  0.007   \n",
      "17    [50, 50, 50, 50]                0.02  0.04  0.002   \n",
      "18               [100]                0.04  0.08  0.001   \n",
      "19      [320, 160, 80]                0.06  0.08  0.008   \n",
      "\n",
      "                                                                model_ids  \\\n",
      "0   Grid_DeepLearning_py_7_sid_a1e2_model_python_1499376661571_52_model_8   \n",
      "1   Grid_DeepLearning_py_7_sid_a1e2_model_python_1499376661571_52_mode...   \n",
      "2   Grid_DeepLearning_py_7_sid_a1e2_model_python_1499376661571_52_mode...   \n",
      "3   Grid_DeepLearning_py_7_sid_a1e2_model_python_1499376661571_52_model_1   \n",
      "4   Grid_DeepLearning_py_7_sid_a1e2_model_python_1499376661571_52_model_5   \n",
      "5   Grid_DeepLearning_py_7_sid_a1e2_model_python_1499376661571_52_model_0   \n",
      "6   Grid_DeepLearning_py_7_sid_a1e2_model_python_1499376661571_52_model_6   \n",
      "7   Grid_DeepLearning_py_7_sid_a1e2_model_python_1499376661571_52_mode...   \n",
      "8   Grid_DeepLearning_py_7_sid_a1e2_model_python_1499376661571_52_model_7   \n",
      "9   Grid_DeepLearning_py_7_sid_a1e2_model_python_1499376661571_52_mode...   \n",
      "10  Grid_DeepLearning_py_7_sid_a1e2_model_python_1499376661571_52_model_4   \n",
      "11  Grid_DeepLearning_py_7_sid_a1e2_model_python_1499376661571_52_mode...   \n",
      "12  Grid_DeepLearning_py_7_sid_a1e2_model_python_1499376661571_52_model_2   \n",
      "13  Grid_DeepLearning_py_7_sid_a1e2_model_python_1499376661571_52_mode...   \n",
      "14  Grid_DeepLearning_py_7_sid_a1e2_model_python_1499376661571_52_model_9   \n",
      "15  Grid_DeepLearning_py_7_sid_a1e2_model_python_1499376661571_52_mode...   \n",
      "16  Grid_DeepLearning_py_7_sid_a1e2_model_python_1499376661571_52_mode...   \n",
      "17  Grid_DeepLearning_py_7_sid_a1e2_model_python_1499376661571_52_model_3   \n",
      "18  Grid_DeepLearning_py_7_sid_a1e2_model_python_1499376661571_52_mode...   \n",
      "19  Grid_DeepLearning_py_7_sid_a1e2_model_python_1499376661571_52_mode...   \n",
      "\n",
      "                logloss  \n",
      "0   0.43960895517572524  \n",
      "1    0.4419455792065834  \n",
      "2   0.47454254126677625  \n",
      "3   0.47457199584809734  \n",
      "4   0.47479852810010065  \n",
      "5   0.47482814115304983  \n",
      "6    0.4749727034832043  \n",
      "7   0.47538052684174054  \n",
      "8    0.4754852535340486  \n",
      "9   0.47588597310931186  \n",
      "10  0.47683656725543255  \n",
      "11  0.47684839165381304  \n",
      "12   0.4814131776403884  \n",
      "13  0.48174093920637096  \n",
      "14  0.48178810738695893  \n",
      "15   0.4848993084822765  \n",
      "16   0.4924394840949194  \n",
      "17   0.4976374250221638  \n",
      "18   0.5075562207279443  \n",
      "19   0.5136788291585326  \n",
      "Model Details\n",
      "=============\n",
      "H2ODeepLearningEstimator :  Deep Learning\n",
      "Model Key:  Grid_DeepLearning_py_7_sid_a1e2_model_python_1499376661571_52_model_8\n",
      "\n",
      "\n",
      "ModelMetricsBinomial: deeplearning\n",
      "** Reported on train data. **\n",
      "\n",
      "MSE: 0.14003698532608153\n",
      "RMSE: 0.3742151591345299\n",
      "LogLoss: 0.4421453666452714\n",
      "Mean Per-Class Error: 0.3496311584487062\n",
      "AUC: 0.7018377632818947\n",
      "Gini: 0.40367552656378947\n",
      "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.1912002098445722: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>0</b></td>\n",
       "<td><b>1</b></td>\n",
       "<td><b>Error</b></td>\n",
       "<td><b>Rate</b></td></tr>\n",
       "<tr><td>0</td>\n",
       "<td>5041.0</td>\n",
       "<td>3054.0</td>\n",
       "<td>0.3773</td>\n",
       "<td> (3054.0/8095.0)</td></tr>\n",
       "<tr><td>1</td>\n",
       "<td>601.0</td>\n",
       "<td>1244.0</td>\n",
       "<td>0.3257</td>\n",
       "<td> (601.0/1845.0)</td></tr>\n",
       "<tr><td>Total</td>\n",
       "<td>5642.0</td>\n",
       "<td>4298.0</td>\n",
       "<td>0.3677</td>\n",
       "<td> (3655.0/9940.0)</td></tr></table></div>"
      ],
      "text/plain": [
       "       0     1     Error    Rate\n",
       "-----  ----  ----  -------  ---------------\n",
       "0      5041  3054  0.3773   (3054.0/8095.0)\n",
       "1      601   1244  0.3257   (601.0/1845.0)\n",
       "Total  5642  4298  0.3677   (3655.0/9940.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum Metrics: Maximum metrics at their respective thresholds\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>metric</b></td>\n",
       "<td><b>threshold</b></td>\n",
       "<td><b>value</b></td>\n",
       "<td><b>idx</b></td></tr>\n",
       "<tr><td>max f1</td>\n",
       "<td>0.1912002</td>\n",
       "<td>0.4050138</td>\n",
       "<td>202.0</td></tr>\n",
       "<tr><td>max f2</td>\n",
       "<td>0.0929216</td>\n",
       "<td>0.5706448</td>\n",
       "<td>311.0</td></tr>\n",
       "<tr><td>max f0point5</td>\n",
       "<td>0.3410043</td>\n",
       "<td>0.3656546</td>\n",
       "<td>77.0</td></tr>\n",
       "<tr><td>max accuracy</td>\n",
       "<td>0.4245313</td>\n",
       "<td>0.8163984</td>\n",
       "<td>2.0</td></tr>\n",
       "<tr><td>max precision</td>\n",
       "<td>0.4255959</td>\n",
       "<td>0.5701754</td>\n",
       "<td>1.0</td></tr>\n",
       "<tr><td>max recall</td>\n",
       "<td>0.0209484</td>\n",
       "<td>1.0</td>\n",
       "<td>390.0</td></tr>\n",
       "<tr><td>max specificity</td>\n",
       "<td>0.4262851</td>\n",
       "<td>0.9970352</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max absolute_mcc</td>\n",
       "<td>0.1654997</td>\n",
       "<td>0.2338504</td>\n",
       "<td>229.0</td></tr>\n",
       "<tr><td>max min_per_class_accuracy</td>\n",
       "<td>0.2008647</td>\n",
       "<td>0.6427424</td>\n",
       "<td>193.0</td></tr>\n",
       "<tr><td>max mean_per_class_accuracy</td>\n",
       "<td>0.1654997</td>\n",
       "<td>0.6503688</td>\n",
       "<td>229.0</td></tr></table></div>"
      ],
      "text/plain": [
       "metric                       threshold    value     idx\n",
       "---------------------------  -----------  --------  -----\n",
       "max f1                       0.1912       0.405014  202\n",
       "max f2                       0.0929216    0.570645  311\n",
       "max f0point5                 0.341004     0.365655  77\n",
       "max accuracy                 0.424531     0.816398  2\n",
       "max precision                0.425596     0.570175  1\n",
       "max recall                   0.0209484    1         390\n",
       "max specificity              0.426285     0.997035  0\n",
       "max absolute_mcc             0.1655       0.23385   229\n",
       "max min_per_class_accuracy   0.200865     0.642742  193\n",
       "max mean_per_class_accuracy  0.1655       0.650369  229"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gains/Lift Table: Avg response rate: 18.56 %\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>group</b></td>\n",
       "<td><b>cumulative_data_fraction</b></td>\n",
       "<td><b>lower_threshold</b></td>\n",
       "<td><b>lift</b></td>\n",
       "<td><b>cumulative_lift</b></td>\n",
       "<td><b>response_rate</b></td>\n",
       "<td><b>cumulative_response_rate</b></td>\n",
       "<td><b>capture_rate</b></td>\n",
       "<td><b>cumulative_capture_rate</b></td>\n",
       "<td><b>gain</b></td>\n",
       "<td><b>cumulative_gain</b></td></tr>\n",
       "<tr><td></td>\n",
       "<td>1</td>\n",
       "<td>0.0100604</td>\n",
       "<td>0.4254772</td>\n",
       "<td>3.1247696</td>\n",
       "<td>3.1247696</td>\n",
       "<td>0.58</td>\n",
       "<td>0.58</td>\n",
       "<td>0.0314363</td>\n",
       "<td>0.0314363</td>\n",
       "<td>212.4769648</td>\n",
       "<td>212.4769648</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2</td>\n",
       "<td>0.0200201</td>\n",
       "<td>0.4227846</td>\n",
       "<td>2.5032986</td>\n",
       "<td>2.8155956</td>\n",
       "<td>0.4646465</td>\n",
       "<td>0.5226131</td>\n",
       "<td>0.0249322</td>\n",
       "<td>0.0563686</td>\n",
       "<td>150.3298568</td>\n",
       "<td>181.5595593</td></tr>\n",
       "<tr><td></td>\n",
       "<td>3</td>\n",
       "<td>0.0300805</td>\n",
       "<td>0.4202108</td>\n",
       "<td>2.0472629</td>\n",
       "<td>2.5586281</td>\n",
       "<td>0.38</td>\n",
       "<td>0.4749164</td>\n",
       "<td>0.0205962</td>\n",
       "<td>0.0769648</td>\n",
       "<td>104.7262873</td>\n",
       "<td>155.8628128</td></tr>\n",
       "<tr><td></td>\n",
       "<td>4</td>\n",
       "<td>0.0400402</td>\n",
       "<td>0.4175355</td>\n",
       "<td>2.3944595</td>\n",
       "<td>2.5177922</td>\n",
       "<td>0.4444444</td>\n",
       "<td>0.4673367</td>\n",
       "<td>0.0238482</td>\n",
       "<td>0.1008130</td>\n",
       "<td>139.4459500</td>\n",
       "<td>151.7792213</td></tr>\n",
       "<tr><td></td>\n",
       "<td>5</td>\n",
       "<td>0.05</td>\n",
       "<td>0.4146446</td>\n",
       "<td>2.0679423</td>\n",
       "<td>2.4281843</td>\n",
       "<td>0.3838384</td>\n",
       "<td>0.4507042</td>\n",
       "<td>0.0205962</td>\n",
       "<td>0.1214092</td>\n",
       "<td>106.7942296</td>\n",
       "<td>142.8184282</td></tr>\n",
       "<tr><td></td>\n",
       "<td>6</td>\n",
       "<td>0.1</td>\n",
       "<td>0.3988114</td>\n",
       "<td>1.9945799</td>\n",
       "<td>2.2113821</td>\n",
       "<td>0.3702213</td>\n",
       "<td>0.4104628</td>\n",
       "<td>0.0997290</td>\n",
       "<td>0.2211382</td>\n",
       "<td>99.4579946</td>\n",
       "<td>121.1382114</td></tr>\n",
       "<tr><td></td>\n",
       "<td>7</td>\n",
       "<td>0.15</td>\n",
       "<td>0.3666512</td>\n",
       "<td>1.6260163</td>\n",
       "<td>2.0162602</td>\n",
       "<td>0.3018109</td>\n",
       "<td>0.3742455</td>\n",
       "<td>0.0813008</td>\n",
       "<td>0.3024390</td>\n",
       "<td>62.6016260</td>\n",
       "<td>101.6260163</td></tr>\n",
       "<tr><td></td>\n",
       "<td>8</td>\n",
       "<td>0.2</td>\n",
       "<td>0.3288440</td>\n",
       "<td>1.6802168</td>\n",
       "<td>1.9322493</td>\n",
       "<td>0.3118712</td>\n",
       "<td>0.3586519</td>\n",
       "<td>0.0840108</td>\n",
       "<td>0.3864499</td>\n",
       "<td>68.0216802</td>\n",
       "<td>93.2249322</td></tr>\n",
       "<tr><td></td>\n",
       "<td>9</td>\n",
       "<td>0.3</td>\n",
       "<td>0.2592265</td>\n",
       "<td>1.3387534</td>\n",
       "<td>1.7344173</td>\n",
       "<td>0.2484909</td>\n",
       "<td>0.3219316</td>\n",
       "<td>0.1338753</td>\n",
       "<td>0.5203252</td>\n",
       "<td>33.8753388</td>\n",
       "<td>73.4417344</td></tr>\n",
       "<tr><td></td>\n",
       "<td>10</td>\n",
       "<td>0.4</td>\n",
       "<td>0.2055017</td>\n",
       "<td>1.1273713</td>\n",
       "<td>1.5826558</td>\n",
       "<td>0.2092555</td>\n",
       "<td>0.2937626</td>\n",
       "<td>0.1127371</td>\n",
       "<td>0.6330623</td>\n",
       "<td>12.7371274</td>\n",
       "<td>58.2655827</td></tr>\n",
       "<tr><td></td>\n",
       "<td>11</td>\n",
       "<td>0.5</td>\n",
       "<td>0.1647871</td>\n",
       "<td>1.1165312</td>\n",
       "<td>1.4894309</td>\n",
       "<td>0.2072435</td>\n",
       "<td>0.2764588</td>\n",
       "<td>0.1116531</td>\n",
       "<td>0.7447154</td>\n",
       "<td>11.6531165</td>\n",
       "<td>48.9430894</td></tr>\n",
       "<tr><td></td>\n",
       "<td>12</td>\n",
       "<td>0.6</td>\n",
       "<td>0.1319031</td>\n",
       "<td>0.7859079</td>\n",
       "<td>1.3721771</td>\n",
       "<td>0.1458753</td>\n",
       "<td>0.2546948</td>\n",
       "<td>0.0785908</td>\n",
       "<td>0.8233062</td>\n",
       "<td>-21.4092141</td>\n",
       "<td>37.2177055</td></tr>\n",
       "<tr><td></td>\n",
       "<td>13</td>\n",
       "<td>0.7</td>\n",
       "<td>0.1031684</td>\n",
       "<td>0.5799458</td>\n",
       "<td>1.2590012</td>\n",
       "<td>0.1076459</td>\n",
       "<td>0.2336878</td>\n",
       "<td>0.0579946</td>\n",
       "<td>0.8813008</td>\n",
       "<td>-42.0054201</td>\n",
       "<td>25.9001161</td></tr>\n",
       "<tr><td></td>\n",
       "<td>14</td>\n",
       "<td>0.8</td>\n",
       "<td>0.0772138</td>\n",
       "<td>0.5528455</td>\n",
       "<td>1.1707317</td>\n",
       "<td>0.1026157</td>\n",
       "<td>0.2173038</td>\n",
       "<td>0.0552846</td>\n",
       "<td>0.9365854</td>\n",
       "<td>-44.7154472</td>\n",
       "<td>17.0731707</td></tr>\n",
       "<tr><td></td>\n",
       "<td>15</td>\n",
       "<td>0.9</td>\n",
       "<td>0.0534114</td>\n",
       "<td>0.4444444</td>\n",
       "<td>1.0900331</td>\n",
       "<td>0.0824950</td>\n",
       "<td>0.2023251</td>\n",
       "<td>0.0444444</td>\n",
       "<td>0.9810298</td>\n",
       "<td>-55.5555556</td>\n",
       "<td>9.0033123</td></tr>\n",
       "<tr><td></td>\n",
       "<td>16</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0000190</td>\n",
       "<td>0.1897019</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0352113</td>\n",
       "<td>0.1856137</td>\n",
       "<td>0.0189702</td>\n",
       "<td>1.0</td>\n",
       "<td>-81.0298103</td>\n",
       "<td>0.0</td></tr></table></div>"
      ],
      "text/plain": [
       "    group    cumulative_data_fraction    lower_threshold    lift      cumulative_lift    response_rate    cumulative_response_rate    capture_rate    cumulative_capture_rate    gain      cumulative_gain\n",
       "--  -------  --------------------------  -----------------  --------  -----------------  ---------------  --------------------------  --------------  -------------------------  --------  -----------------\n",
       "    1        0.0100604                   0.425477           3.12477   3.12477            0.58             0.58                        0.0314363       0.0314363                  212.477   212.477\n",
       "    2        0.0200201                   0.422785           2.5033    2.8156             0.464646         0.522613                    0.0249322       0.0563686                  150.33    181.56\n",
       "    3        0.0300805                   0.420211           2.04726   2.55863            0.38             0.474916                    0.0205962       0.0769648                  104.726   155.863\n",
       "    4        0.0400402                   0.417535           2.39446   2.51779            0.444444         0.467337                    0.0238482       0.100813                   139.446   151.779\n",
       "    5        0.05                        0.414645           2.06794   2.42818            0.383838         0.450704                    0.0205962       0.121409                   106.794   142.818\n",
       "    6        0.1                         0.398811           1.99458   2.21138            0.370221         0.410463                    0.099729        0.221138                   99.458    121.138\n",
       "    7        0.15                        0.366651           1.62602   2.01626            0.301811         0.374245                    0.0813008       0.302439                   62.6016   101.626\n",
       "    8        0.2                         0.328844           1.68022   1.93225            0.311871         0.358652                    0.0840108       0.38645                    68.0217   93.2249\n",
       "    9        0.3                         0.259226           1.33875   1.73442            0.248491         0.321932                    0.133875        0.520325                   33.8753   73.4417\n",
       "    10       0.4                         0.205502           1.12737   1.58266            0.209256         0.293763                    0.112737        0.633062                   12.7371   58.2656\n",
       "    11       0.5                         0.164787           1.11653   1.48943            0.207243         0.276459                    0.111653        0.744715                   11.6531   48.9431\n",
       "    12       0.6                         0.131903           0.785908  1.37218            0.145875         0.254695                    0.0785908       0.823306                   -21.4092  37.2177\n",
       "    13       0.7                         0.103168           0.579946  1.259              0.107646         0.233688                    0.0579946       0.881301                   -42.0054  25.9001\n",
       "    14       0.8                         0.0772138          0.552846  1.17073            0.102616         0.217304                    0.0552846       0.936585                   -44.7154  17.0732\n",
       "    15       0.9                         0.0534114          0.444444  1.09003            0.082495         0.202325                    0.0444444       0.98103                    -55.5556  9.00331\n",
       "    16       1                           1.90499e-05        0.189702  1                  0.0352113        0.185614                    0.0189702       1                          -81.0298  0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "ModelMetricsBinomial: deeplearning\n",
      "** Reported on validation data. **\n",
      "\n",
      "MSE: 0.13839502352355335\n",
      "RMSE: 0.37201481626885957\n",
      "LogLoss: 0.43960895517572524\n",
      "Mean Per-Class Error: 0.3546866026396488\n",
      "AUC: 0.6972734654391199\n",
      "Gini: 0.3945469308782399\n",
      "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.2186940042193634: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>0</b></td>\n",
       "<td><b>1</b></td>\n",
       "<td><b>Error</b></td>\n",
       "<td><b>Rate</b></td></tr>\n",
       "<tr><td>0</td>\n",
       "<td>27573.0</td>\n",
       "<td>12791.0</td>\n",
       "<td>0.3169</td>\n",
       "<td> (12791.0/40364.0)</td></tr>\n",
       "<tr><td>1</td>\n",
       "<td>3540.0</td>\n",
       "<td>5442.0</td>\n",
       "<td>0.3941</td>\n",
       "<td> (3540.0/8982.0)</td></tr>\n",
       "<tr><td>Total</td>\n",
       "<td>31113.0</td>\n",
       "<td>18233.0</td>\n",
       "<td>0.3309</td>\n",
       "<td> (16331.0/49346.0)</td></tr></table></div>"
      ],
      "text/plain": [
       "       0      1      Error    Rate\n",
       "-----  -----  -----  -------  -----------------\n",
       "0      27573  12791  0.3169   (12791.0/40364.0)\n",
       "1      3540   5442   0.3941   (3540.0/8982.0)\n",
       "Total  31113  18233  0.3309   (16331.0/49346.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum Metrics: Maximum metrics at their respective thresholds\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>metric</b></td>\n",
       "<td><b>threshold</b></td>\n",
       "<td><b>value</b></td>\n",
       "<td><b>idx</b></td></tr>\n",
       "<tr><td>max f1</td>\n",
       "<td>0.2186940</td>\n",
       "<td>0.3999265</td>\n",
       "<td>178.0</td></tr>\n",
       "<tr><td>max f2</td>\n",
       "<td>0.1124487</td>\n",
       "<td>0.5596831</td>\n",
       "<td>288.0</td></tr>\n",
       "<tr><td>max f0point5</td>\n",
       "<td>0.3397501</td>\n",
       "<td>0.3640428</td>\n",
       "<td>75.0</td></tr>\n",
       "<tr><td>max accuracy</td>\n",
       "<td>0.4256849</td>\n",
       "<td>0.8183845</td>\n",
       "<td>1.0</td></tr>\n",
       "<tr><td>max precision</td>\n",
       "<td>0.4264541</td>\n",
       "<td>0.6117647</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max recall</td>\n",
       "<td>0.0006723</td>\n",
       "<td>1.0</td>\n",
       "<td>399.0</td></tr>\n",
       "<tr><td>max specificity</td>\n",
       "<td>0.4264541</td>\n",
       "<td>0.9991824</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max absolute_mcc</td>\n",
       "<td>0.2454506</td>\n",
       "<td>0.2322913</td>\n",
       "<td>153.0</td></tr>\n",
       "<tr><td>max min_per_class_accuracy</td>\n",
       "<td>0.2006461</td>\n",
       "<td>0.6446091</td>\n",
       "<td>194.0</td></tr>\n",
       "<tr><td>max mean_per_class_accuracy</td>\n",
       "<td>0.1951939</td>\n",
       "<td>0.6453134</td>\n",
       "<td>200.0</td></tr></table></div>"
      ],
      "text/plain": [
       "metric                       threshold    value     idx\n",
       "---------------------------  -----------  --------  -----\n",
       "max f1                       0.218694     0.399927  178\n",
       "max f2                       0.112449     0.559683  288\n",
       "max f0point5                 0.33975      0.364043  75\n",
       "max accuracy                 0.425685     0.818384  1\n",
       "max precision                0.426454     0.611765  0\n",
       "max recall                   0.000672284  1         399\n",
       "max specificity              0.426454     0.999182  0\n",
       "max absolute_mcc             0.245451     0.232291  153\n",
       "max min_per_class_accuracy   0.200646     0.644609  194\n",
       "max mean_per_class_accuracy  0.195194     0.645313  200"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gains/Lift Table: Avg response rate: 18.20 %\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>group</b></td>\n",
       "<td><b>cumulative_data_fraction</b></td>\n",
       "<td><b>lower_threshold</b></td>\n",
       "<td><b>lift</b></td>\n",
       "<td><b>cumulative_lift</b></td>\n",
       "<td><b>response_rate</b></td>\n",
       "<td><b>cumulative_response_rate</b></td>\n",
       "<td><b>capture_rate</b></td>\n",
       "<td><b>cumulative_capture_rate</b></td>\n",
       "<td><b>gain</b></td>\n",
       "<td><b>cumulative_gain</b></td></tr>\n",
       "<tr><td></td>\n",
       "<td>1</td>\n",
       "<td>0.0100109</td>\n",
       "<td>0.4250747</td>\n",
       "<td>2.9137564</td>\n",
       "<td>2.9137564</td>\n",
       "<td>0.5303644</td>\n",
       "<td>0.5303644</td>\n",
       "<td>0.0291695</td>\n",
       "<td>0.0291695</td>\n",
       "<td>191.3756438</td>\n",
       "<td>191.3756438</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2</td>\n",
       "<td>0.0200016</td>\n",
       "<td>0.4220259</td>\n",
       "<td>2.3847659</td>\n",
       "<td>2.6495292</td>\n",
       "<td>0.4340771</td>\n",
       "<td>0.4822695</td>\n",
       "<td>0.0238254</td>\n",
       "<td>0.0529949</td>\n",
       "<td>138.4765926</td>\n",
       "<td>164.9529161</td></tr>\n",
       "<tr><td></td>\n",
       "<td>3</td>\n",
       "<td>0.0300126</td>\n",
       "<td>0.4193939</td>\n",
       "<td>2.2798476</td>\n",
       "<td>2.5262188</td>\n",
       "<td>0.4149798</td>\n",
       "<td>0.4598244</td>\n",
       "<td>0.0228234</td>\n",
       "<td>0.0758183</td>\n",
       "<td>127.9847594</td>\n",
       "<td>152.6218767</td></tr>\n",
       "<tr><td></td>\n",
       "<td>4</td>\n",
       "<td>0.0400032</td>\n",
       "<td>0.4166768</td>\n",
       "<td>2.2510407</td>\n",
       "<td>2.4574940</td>\n",
       "<td>0.4097363</td>\n",
       "<td>0.4473151</td>\n",
       "<td>0.0224894</td>\n",
       "<td>0.0983077</td>\n",
       "<td>125.1040734</td>\n",
       "<td>145.7493959</td></tr>\n",
       "<tr><td></td>\n",
       "<td>5</td>\n",
       "<td>0.0500142</td>\n",
       "<td>0.4141412</td>\n",
       "<td>2.1130295</td>\n",
       "<td>2.3885452</td>\n",
       "<td>0.3846154</td>\n",
       "<td>0.4347650</td>\n",
       "<td>0.0211534</td>\n",
       "<td>0.1194611</td>\n",
       "<td>111.3029478</td>\n",
       "<td>138.8545234</td></tr>\n",
       "<tr><td></td>\n",
       "<td>6</td>\n",
       "<td>0.1000081</td>\n",
       "<td>0.3925712</td>\n",
       "<td>1.9753014</td>\n",
       "<td>2.1819652</td>\n",
       "<td>0.3595460</td>\n",
       "<td>0.3971631</td>\n",
       "<td>0.0987531</td>\n",
       "<td>0.2182142</td>\n",
       "<td>97.5301411</td>\n",
       "<td>118.1965191</td></tr>\n",
       "<tr><td></td>\n",
       "<td>7</td>\n",
       "<td>0.1500020</td>\n",
       "<td>0.3615394</td>\n",
       "<td>1.8661810</td>\n",
       "<td>2.0767180</td>\n",
       "<td>0.3396838</td>\n",
       "<td>0.3780059</td>\n",
       "<td>0.0932977</td>\n",
       "<td>0.3115119</td>\n",
       "<td>86.6181040</td>\n",
       "<td>107.6718028</td></tr>\n",
       "<tr><td></td>\n",
       "<td>8</td>\n",
       "<td>0.2000162</td>\n",
       "<td>0.3231408</td>\n",
       "<td>1.5760392</td>\n",
       "<td>1.9515229</td>\n",
       "<td>0.2868720</td>\n",
       "<td>0.3552178</td>\n",
       "<td>0.0788243</td>\n",
       "<td>0.3903362</td>\n",
       "<td>57.6039166</td>\n",
       "<td>95.1522949</td></tr>\n",
       "<tr><td></td>\n",
       "<td>9</td>\n",
       "<td>0.3000041</td>\n",
       "<td>0.2552185</td>\n",
       "<td>1.3328274</td>\n",
       "<td>1.7453190</td>\n",
       "<td>0.2426024</td>\n",
       "<td>0.3176844</td>\n",
       "<td>0.1332665</td>\n",
       "<td>0.5236028</td>\n",
       "<td>33.2827390</td>\n",
       "<td>74.5318958</td></tr>\n",
       "<tr><td></td>\n",
       "<td>10</td>\n",
       "<td>0.4000122</td>\n",
       "<td>0.2037680</td>\n",
       "<td>1.1310595</td>\n",
       "<td>1.5917463</td>\n",
       "<td>0.2058764</td>\n",
       "<td>0.2897310</td>\n",
       "<td>0.1131151</td>\n",
       "<td>0.6367179</td>\n",
       "<td>13.1059507</td>\n",
       "<td>59.1746315</td></tr>\n",
       "<tr><td></td>\n",
       "<td>11</td>\n",
       "<td>0.5</td>\n",
       "<td>0.1639419</td>\n",
       "<td>0.9319770</td>\n",
       "<td>1.4598085</td>\n",
       "<td>0.1696392</td>\n",
       "<td>0.2657156</td>\n",
       "<td>0.0931864</td>\n",
       "<td>0.7299043</td>\n",
       "<td>-6.8022953</td>\n",
       "<td>45.9808506</td></tr>\n",
       "<tr><td></td>\n",
       "<td>12</td>\n",
       "<td>0.6000081</td>\n",
       "<td>0.1313579</td>\n",
       "<td>0.7870660</td>\n",
       "<td>1.3476772</td>\n",
       "<td>0.1432624</td>\n",
       "<td>0.2453053</td>\n",
       "<td>0.0787130</td>\n",
       "<td>0.8086172</td>\n",
       "<td>-21.2933985</td>\n",
       "<td>34.7677184</td></tr>\n",
       "<tr><td></td>\n",
       "<td>13</td>\n",
       "<td>0.6999959</td>\n",
       "<td>0.1031024</td>\n",
       "<td>0.6870129</td>\n",
       "<td>1.2533075</td>\n",
       "<td>0.1250507</td>\n",
       "<td>0.2281281</td>\n",
       "<td>0.0686929</td>\n",
       "<td>0.8773102</td>\n",
       "<td>-31.2987051</td>\n",
       "<td>25.3307508</td></tr>\n",
       "<tr><td></td>\n",
       "<td>14</td>\n",
       "<td>0.8000041</td>\n",
       "<td>0.0776220</td>\n",
       "<td>0.5488310</td>\n",
       "<td>1.1652413</td>\n",
       "<td>0.0998987</td>\n",
       "<td>0.2120982</td>\n",
       "<td>0.0548876</td>\n",
       "<td>0.9321977</td>\n",
       "<td>-45.1168960</td>\n",
       "<td>16.5241258</td></tr>\n",
       "<tr><td></td>\n",
       "<td>15</td>\n",
       "<td>0.8999919</td>\n",
       "<td>0.0530868</td>\n",
       "<td>0.4231198</td>\n",
       "<td>1.0827926</td>\n",
       "<td>0.0770166</td>\n",
       "<td>0.1970908</td>\n",
       "<td>0.0423068</td>\n",
       "<td>0.9745046</td>\n",
       "<td>-57.6880194</td>\n",
       "<td>8.2792602</td></tr>\n",
       "<tr><td></td>\n",
       "<td>16</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0000000</td>\n",
       "<td>0.2549337</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0464032</td>\n",
       "<td>0.1820208</td>\n",
       "<td>0.0254954</td>\n",
       "<td>1.0</td>\n",
       "<td>-74.5066312</td>\n",
       "<td>0.0</td></tr></table></div>"
      ],
      "text/plain": [
       "    group    cumulative_data_fraction    lower_threshold    lift      cumulative_lift    response_rate    cumulative_response_rate    capture_rate    cumulative_capture_rate    gain      cumulative_gain\n",
       "--  -------  --------------------------  -----------------  --------  -----------------  ---------------  --------------------------  --------------  -------------------------  --------  -----------------\n",
       "    1        0.0100109                   0.425075           2.91376   2.91376            0.530364         0.530364                    0.0291695       0.0291695                  191.376   191.376\n",
       "    2        0.0200016                   0.422026           2.38477   2.64953            0.434077         0.48227                     0.0238254       0.0529949                  138.477   164.953\n",
       "    3        0.0300126                   0.419394           2.27985   2.52622            0.41498          0.459824                    0.0228234       0.0758183                  127.985   152.622\n",
       "    4        0.0400032                   0.416677           2.25104   2.45749            0.409736         0.447315                    0.0224894       0.0983077                  125.104   145.749\n",
       "    5        0.0500142                   0.414141           2.11303   2.38855            0.384615         0.434765                    0.0211534       0.119461                   111.303   138.855\n",
       "    6        0.100008                    0.392571           1.9753    2.18197            0.359546         0.397163                    0.0987531       0.218214                   97.5301   118.197\n",
       "    7        0.150002                    0.361539           1.86618   2.07672            0.339684         0.378006                    0.0932977       0.311512                   86.6181   107.672\n",
       "    8        0.200016                    0.323141           1.57604   1.95152            0.286872         0.355218                    0.0788243       0.390336                   57.6039   95.1523\n",
       "    9        0.300004                    0.255218           1.33283   1.74532            0.242602         0.317684                    0.133267        0.523603                   33.2827   74.5319\n",
       "    10       0.400012                    0.203768           1.13106   1.59175            0.205876         0.289731                    0.113115        0.636718                   13.106    59.1746\n",
       "    11       0.5                         0.163942           0.931977  1.45981            0.169639         0.265716                    0.0931864       0.729904                   -6.8023   45.9809\n",
       "    12       0.600008                    0.131358           0.787066  1.34768            0.143262         0.245305                    0.078713        0.808617                   -21.2934  34.7677\n",
       "    13       0.699996                    0.103102           0.687013  1.25331            0.125051         0.228128                    0.0686929       0.87731                    -31.2987  25.3308\n",
       "    14       0.800004                    0.077622           0.548831  1.16524            0.0998987        0.212098                    0.0548876       0.932198                   -45.1169  16.5241\n",
       "    15       0.899992                    0.0530868          0.42312   1.08279            0.0770166        0.197091                    0.0423068       0.974505                   -57.688   8.27926\n",
       "    16       1                           4.49904e-17        0.254934  1                  0.0464032        0.182021                    0.0254954       1                          -74.5066  0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Scoring History: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>timestamp</b></td>\n",
       "<td><b>duration</b></td>\n",
       "<td><b>training_speed</b></td>\n",
       "<td><b>epochs</b></td>\n",
       "<td><b>iterations</b></td>\n",
       "<td><b>samples</b></td>\n",
       "<td><b>training_rmse</b></td>\n",
       "<td><b>training_logloss</b></td>\n",
       "<td><b>training_auc</b></td>\n",
       "<td><b>training_lift</b></td>\n",
       "<td><b>training_classification_error</b></td>\n",
       "<td><b>validation_rmse</b></td>\n",
       "<td><b>validation_logloss</b></td>\n",
       "<td><b>validation_auc</b></td>\n",
       "<td><b>validation_lift</b></td>\n",
       "<td><b>validation_classification_error</b></td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-07-06 17:33:13</td>\n",
       "<td> 0.000 sec</td>\n",
       "<td>None</td>\n",
       "<td>0.0</td>\n",
       "<td>0</td>\n",
       "<td>0.0</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-07-06 17:33:14</td>\n",
       "<td> 1 min 25.850 sec</td>\n",
       "<td>65113 obs/sec</td>\n",
       "<td>1.0</td>\n",
       "<td>1</td>\n",
       "<td>65374.0</td>\n",
       "<td>0.3742152</td>\n",
       "<td>0.4421454</td>\n",
       "<td>0.7018378</td>\n",
       "<td>3.1247696</td>\n",
       "<td>0.3677062</td>\n",
       "<td>0.3720148</td>\n",
       "<td>0.4396090</td>\n",
       "<td>0.6972735</td>\n",
       "<td>2.9137564</td>\n",
       "<td>0.3309488</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-07-06 17:33:19</td>\n",
       "<td> 1 min 30.482 sec</td>\n",
       "<td>122837 obs/sec</td>\n",
       "<td>10.0</td>\n",
       "<td>10</td>\n",
       "<td>653740.0</td>\n",
       "<td>0.3788216</td>\n",
       "<td>0.4540224</td>\n",
       "<td>0.7030458</td>\n",
       "<td>3.1247696</td>\n",
       "<td>0.3250503</td>\n",
       "<td>0.3761411</td>\n",
       "<td>0.4502070</td>\n",
       "<td>0.6975623</td>\n",
       "<td>2.8136656</td>\n",
       "<td>0.3261865</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-07-06 17:33:19</td>\n",
       "<td> 1 min 30.793 sec</td>\n",
       "<td>122791 obs/sec</td>\n",
       "<td>10.0</td>\n",
       "<td>10</td>\n",
       "<td>653740.0</td>\n",
       "<td>0.3742152</td>\n",
       "<td>0.4421454</td>\n",
       "<td>0.7018378</td>\n",
       "<td>3.1247696</td>\n",
       "<td>0.3677062</td>\n",
       "<td>0.3720148</td>\n",
       "<td>0.4396090</td>\n",
       "<td>0.6972735</td>\n",
       "<td>2.9137564</td>\n",
       "<td>0.3309488</td></tr></table></div>"
      ],
      "text/plain": [
       "    timestamp            duration          training_speed    epochs    iterations    samples    training_rmse    training_logloss    training_auc    training_lift    training_classification_error    validation_rmse    validation_logloss    validation_auc    validation_lift    validation_classification_error\n",
       "--  -------------------  ----------------  ----------------  --------  ------------  ---------  ---------------  ------------------  --------------  ---------------  -------------------------------  -----------------  --------------------  ----------------  -----------------  ---------------------------------\n",
       "    2017-07-06 17:33:13  0.000 sec                           0         0             0          nan              nan                 nan             nan              nan                              nan                nan                   nan               nan                nan\n",
       "    2017-07-06 17:33:14  1 min 25.850 sec  65113 obs/sec     1         1             65374      0.374215         0.442145            0.701838        3.12477          0.367706                         0.372015           0.439609              0.697273          2.91376            0.330949\n",
       "    2017-07-06 17:33:19  1 min 30.482 sec  122837 obs/sec    10        10            653740     0.378822         0.454022            0.703046        3.12477          0.32505                          0.376141           0.450207              0.697562          2.81367            0.326187\n",
       "    2017-07-06 17:33:19  1 min 30.793 sec  122791 obs/sec    10        10            653740     0.374215         0.442145            0.701838        3.12477          0.367706                         0.372015           0.439609              0.697273          2.91376            0.330949"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable Importances: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>variable</b></td>\n",
       "<td><b>relative_importance</b></td>\n",
       "<td><b>scaled_importance</b></td>\n",
       "<td><b>percentage</b></td></tr>\n",
       "<tr><td>home_ownership.NONE</td>\n",
       "<td>1.0</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0173319</td></tr>\n",
       "<tr><td>addr_state.AK</td>\n",
       "<td>0.9781761</td>\n",
       "<td>0.9781761</td>\n",
       "<td>0.0169537</td></tr>\n",
       "<tr><td>addr_state.SD</td>\n",
       "<td>0.9561462</td>\n",
       "<td>0.9561462</td>\n",
       "<td>0.0165719</td></tr>\n",
       "<tr><td>addr_state.MO</td>\n",
       "<td>0.9485161</td>\n",
       "<td>0.9485161</td>\n",
       "<td>0.0164396</td></tr>\n",
       "<tr><td>addr_state.OK</td>\n",
       "<td>0.9468167</td>\n",
       "<td>0.9468167</td>\n",
       "<td>0.0164102</td></tr>\n",
       "<tr><td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td></tr>\n",
       "<tr><td>addr_state.missing(NA)</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>purpose.missing(NA)</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>home_ownership.missing(NA)</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>term.missing(NA)</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>verification_status.missing(NA)</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td></tr></table></div>"
      ],
      "text/plain": [
       "variable                         relative_importance    scaled_importance    percentage\n",
       "-------------------------------  ---------------------  -------------------  --------------------\n",
       "home_ownership.NONE              1.0                    1.0                  0.017331946618628986\n",
       "addr_state.AK                    0.9781760573387146     0.9781760573387146   0.01695369520941557\n",
       "addr_state.SD                    0.9561461806297302     0.9561461806297302   0.016571874562280473\n",
       "addr_state.MO                    0.9485160708427429     0.9485160708427429   0.01643962990675813\n",
       "addr_state.OK                    0.9468167424201965     0.9468167424201965   0.01641017723725104\n",
       "---                              ---                    ---                  ---\n",
       "addr_state.missing(NA)           0.0                    0.0                  0.0\n",
       "purpose.missing(NA)              0.0                    0.0                  0.0\n",
       "home_ownership.missing(NA)       0.0                    0.0                  0.0\n",
       "term.missing(NA)                 0.0                    0.0                  0.0\n",
       "verification_status.missing(NA)  0.0                    0.0                  0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "See the whole table with table.as_data_frame()\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show grid search results\n",
    "gsearch.show()\n",
    "\n",
    "# select best model\n",
    "nn_model2 = gsearch.get_grid()[0]\n",
    "\n",
    "# print model information\n",
    "nn_model2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7018377632818947\n",
      "0.6972734654391199\n",
      "0.6903184118555723\n"
     ]
    }
   ],
   "source": [
    "# measure nn AUC\n",
    "print(nn_model2.auc(train=True))\n",
    "print(nn_model2.auc(valid=True))\n",
    "print(nn_model2.model_performance(test_data=test).auc())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PartialDependencePlot progress: |█████████████████████████████████████████| 100%\n"
     ]
    },
    {
     "data": {
      "image/png": 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mzctdgVqNDbEkSeoIjz8OEybAm94EDz2Uuxq1EhtiNaynpyd3CZVkbmnMLY25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F2ZpHo14xxiSZIGzbPPwtVXw9y58Oc/w4knws03FxfWkCQbYklSU+vqgje+EVavhjPOgOnTYcyY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1196f6128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# partial dependence plots are a powerful machine learning interpretation tool\n",
    "# to calculate partial dependence across the domain a variable\n",
    "# hold column of interest at constant value\n",
    "# find the mean prediction of the model with this column constant\n",
    "# repeat for multiple values of the variable of interest\n",
    "# h2o has a built-in function for partial dependence as well\n",
    "par_dep_dti1 = nn_model2.partial_plot(data=train, cols=['dti'], server=True, plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Are you sure you want to shutdown the H2O instance running at http://127.0.0.1:54321 (Y/N)? y\n",
      "H2O session _sid_a1e2 closed.\n"
     ]
    }
   ],
   "source": [
    "# shutdown h2o\n",
    "# be careful ... this can erase your work\n",
    "h2o.cluster().shutdown(prompt=True)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
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