diff --git a/training/Dockerfile b/training/Dockerfile
index 36f688921..f6fbfd194 100644
--- a/training/Dockerfile
+++ b/training/Dockerfile
@@ -93,9 +93,9 @@ RUN \
   rm ${MINICONDA_FILE}
 
 # H2O
-ENV H2O_BRANCH_NAME=rel-xia
-ENV H2O_BUILD_NUMBER=2
-ENV H2O_PROJECT_VERSION=3.22.0.${H2O_BUILD_NUMBER}
+ENV H2O_BRANCH_NAME=rel-xu
+ENV H2O_BUILD_NUMBER=3
+ENV H2O_PROJECT_VERSION=3.22.1.${H2O_BUILD_NUMBER}
 ENV H2O_DIRECTORY=h2o-${H2O_PROJECT_VERSION}
 RUN \
   wget http://h2o-release.s3.amazonaws.com/h2o/${H2O_BRANCH_NAME}/${H2O_BUILD_NUMBER}/h2o-${H2O_PROJECT_VERSION}.zip && \
diff --git a/training/h2o_3_hands_on/automl/lending_club_automl.ipynb b/training/h2o_3_hands_on/automl/lending_club_automl.ipynb
index 9872f436c..3635fa63c 100644
--- a/training/h2o_3_hands_on/automl/lending_club_automl.ipynb
+++ b/training/h2o_3_hands_on/automl/lending_club_automl.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,85 +25,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "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_181\"; Java(TM) SE Runtime Environment (build 1.8.0_181-b13); Java HotSpot(TM) 64-Bit Server VM (build 25.181-b13, mixed mode)\n",
-      "  Starting server from /home/h2o/Miniconda3/envs/h2o/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar\n",
-      "  Ice root: /tmp/tmp7mtn8x0f\n",
-      "  JVM stdout: /tmp/tmp7mtn8x0f/h2o_unknownUser_started_from_python.out\n",
-      "  JVM stderr: /tmp/tmp7mtn8x0f/h2o_unknownUser_started_from_python.err\n",
-      "  Server is running at http://127.0.0.1:54321\n",
-      "Connecting to H2O server at http://127.0.0.1:54321... successful.\n"
-     ]
-    },
-    {
-     "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 timezone:</td>\n",
-       "<td>Etc/UTC</td></tr>\n",
-       "<tr><td>H2O data parsing timezone:</td>\n",
-       "<td>UTC</td></tr>\n",
-       "<tr><td>H2O cluster version:</td>\n",
-       "<td>3.20.0.4</td></tr>\n",
-       "<tr><td>H2O cluster version age:</td>\n",
-       "<td>15 days </td></tr>\n",
-       "<tr><td>H2O cluster name:</td>\n",
-       "<td>H2O_from_python_unknownUser_ablowc</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>5.333 Gb</td></tr>\n",
-       "<tr><td>H2O cluster total cores:</td>\n",
-       "<td>4</td></tr>\n",
-       "<tr><td>H2O cluster allowed cores:</td>\n",
-       "<td>4</td></tr>\n",
-       "<tr><td>H2O cluster status:</td>\n",
-       "<td>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>H2O API Extensions:</td>\n",
-       "<td>XGBoost, Algos, AutoML, Core V3, Core V4</td></tr>\n",
-       "<tr><td>Python version:</td>\n",
-       "<td>3.6.5 final</td></tr></table></div>"
-      ],
-      "text/plain": [
-       "--------------------------  ----------------------------------------\n",
-       "H2O cluster uptime:         01 secs\n",
-       "H2O cluster timezone:       Etc/UTC\n",
-       "H2O data parsing timezone:  UTC\n",
-       "H2O cluster version:        3.20.0.4\n",
-       "H2O cluster version age:    15 days\n",
-       "H2O cluster name:           H2O_from_python_unknownUser_ablowc\n",
-       "H2O cluster total nodes:    1\n",
-       "H2O cluster free memory:    5.333 Gb\n",
-       "H2O cluster total cores:    4\n",
-       "H2O cluster allowed cores:  4\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",
-       "H2O API Extensions:         XGBoost, Algos, AutoML, Core V3, Core V4\n",
-       "Python version:             3.6.5 final\n",
-       "--------------------------  ----------------------------------------"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "h2o.init(max_mem_size = \"6g\")"
    ]
@@ -137,17 +61,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# https://s3-us-west-2.amazonaws.com/h2o-tutorials/data/topics/data/automl/loan.csv\n",
     "train = h2o.import_file(\"../../data/topics/automl/loan.csv\")\n",
@@ -156,52 +72,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "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>13074.169141456341</td><td>         </td><td>13.715904065566184</td><td>5.6843529329953295</td><td>                </td><td>71915.67051974907</td><td>                  </td><td>            </td><td>15.881530121290105</td><td>0.22735700606252796</td><td>54.07917280242258 </td><td>24.57973383427464 </td><td>          </td><td>14.854273655448349     </td><td>                     </td></tr>\n",
-       "<tr><td>maxs   </td><td>35000.0           </td><td>         </td><td>26.06             </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>7993.55618873465  </td><td>         </td><td>4.391939870545794 </td><td>3.6106637311002348</td><td>                </td><td>59070.91565491827</td><td>                  </td><td>            </td><td>7.587668224192542 </td><td>0.6941679229284178 </td><td>25.285366766770508</td><td>11.685190365910653</td><td>          </td><td>6.947732922546688      </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>27.65             </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>11.2              </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>18.08             </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"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "train.describe()"
    ]
@@ -215,17 +88,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "AutoML progress: |████████████████████████████████████████████████████████| 100%\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Set target and predictor variables\n",
     "y = \"bad_loan\"\n",
@@ -241,40 +106,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<thead>\n",
-       "<tr><th>model_id                                             </th><th style=\"text-align: right;\">     auc</th><th style=\"text-align: right;\">  logloss</th><th style=\"text-align: right;\">  mean_per_class_error</th><th style=\"text-align: right;\">    rmse</th><th style=\"text-align: right;\">     mse</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "<tr><td>StackedEnsemble_AllModels_0_AutoML_20180816_011533   </td><td style=\"text-align: right;\">0.682867</td><td style=\"text-align: right;\"> 0.446775</td><td style=\"text-align: right;\">              0.367415</td><td style=\"text-align: right;\">0.374918</td><td style=\"text-align: right;\">0.140563</td></tr>\n",
-       "<tr><td>StackedEnsemble_BestOfFamily_0_AutoML_20180816_011533</td><td style=\"text-align: right;\">0.682505</td><td style=\"text-align: right;\"> 0.446838</td><td style=\"text-align: right;\">              0.3684  </td><td style=\"text-align: right;\">0.374941</td><td style=\"text-align: right;\">0.140581</td></tr>\n",
-       "<tr><td>GBM_grid_0_AutoML_20180816_011533_model_0            </td><td style=\"text-align: right;\">0.680493</td><td style=\"text-align: right;\"> 0.445819</td><td style=\"text-align: right;\">              0.369521</td><td style=\"text-align: right;\">0.374655</td><td style=\"text-align: right;\">0.140366</td></tr>\n",
-       "<tr><td>GBM_grid_0_AutoML_20180816_011533_model_1            </td><td style=\"text-align: right;\">0.677665</td><td style=\"text-align: right;\"> 0.447037</td><td style=\"text-align: right;\">              0.372697</td><td style=\"text-align: right;\">0.375264</td><td style=\"text-align: right;\">0.140823</td></tr>\n",
-       "<tr><td>GBM_grid_0_AutoML_20180816_011533_model_2            </td><td style=\"text-align: right;\">0.676167</td><td style=\"text-align: right;\"> 0.44776 </td><td style=\"text-align: right;\">              0.372026</td><td style=\"text-align: right;\">0.375597</td><td style=\"text-align: right;\">0.141073</td></tr>\n",
-       "<tr><td>GLM_grid_0_AutoML_20180816_011533_model_0            </td><td style=\"text-align: right;\">0.674138</td><td style=\"text-align: right;\"> 0.44873 </td><td style=\"text-align: right;\">              0.373348</td><td style=\"text-align: right;\">0.375609</td><td style=\"text-align: right;\">0.141082</td></tr>\n",
-       "<tr><td>XRT_0_AutoML_20180816_011533                         </td><td style=\"text-align: right;\">0.664243</td><td style=\"text-align: right;\"> 0.452034</td><td style=\"text-align: right;\">              0.379151</td><td style=\"text-align: right;\">0.377113</td><td style=\"text-align: right;\">0.142214</td></tr>\n",
-       "<tr><td>DRF_0_AutoML_20180816_011533                         </td><td style=\"text-align: right;\">0.658237</td><td style=\"text-align: right;\"> 0.456314</td><td style=\"text-align: right;\">              0.385645</td><td style=\"text-align: right;\">0.378535</td><td style=\"text-align: right;\">0.143289</td></tr>\n",
-       "</tbody>\n",
-       "</table>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(aml.leaderboard)"
    ]
@@ -288,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -297,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -311,58 +145,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1008x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "best_model.varimp_plot()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "AUC: train = 0.7264, valid = 0.6875, xval = 0.6805\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
    "source": [
-    "print(\"AUC: train = {:.4f}, valid = {:.4f}, xval = {:.4f}\" \\\n",
-    "      .format(best_model.auc(train = True), best_model.auc(valid = True), best_model.auc(xval = True)))"
+    "print(\"AUC: train = {:.4f}, xval = {:.4f}\"\\\n",
+    "      .format(best_model.auc(train = True), best_model.auc(xval = True)))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Logloss: train = 0.4277, valid = 0.4384, xval = 0.4458\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "print(\"Logloss: train = {:.4f}, valid = {:.4f}, xval = {:.4f}\" \\\n",
-    "      .format(best_model.logloss(train = True), best_model.logloss(valid = True), best_model.logloss(xval = True)))"
+    "print(\"Logloss: train = {:.4f}, xval = {:.4f}\" \\\n",
+    "      .format(best_model.logloss(train = True), best_model.logloss(xval = True)))"
    ]
   },
   {
@@ -390,7 +199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -415,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -425,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -434,45 +243,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<thead>\n",
-       "<tr><th style=\"text-align: right;\">  cv_fold</th><th style=\"text-align: right;\">  Count</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "<tr><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">  32848</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">  32808</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">        2</td><td style=\"text-align: right;\">  33035</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">        3</td><td style=\"text-align: right;\">  32574</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">        4</td><td style=\"text-align: right;\">  32722</td></tr>\n",
-       "</tbody>\n",
-       "</table>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "train[\"cv_fold\"].table()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -482,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -492,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -504,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -552,47 +332,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<thead>\n",
-       "<tr><th style=\"text-align: right;\">  Probs</th><th style=\"text-align: right;\">  loan_amntQuantiles</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "<tr><td style=\"text-align: right;\">   0   </td><td style=\"text-align: right;\">                 500</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.05</td><td style=\"text-align: right;\">                3000</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.25</td><td style=\"text-align: right;\">                7000</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.5 </td><td style=\"text-align: right;\">               11325</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.75</td><td style=\"text-align: right;\">               18000</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.95</td><td style=\"text-align: right;\">               30000</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   1   </td><td style=\"text-align: right;\">               35000</td></tr>\n",
-       "</tbody>\n",
-       "</table>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "train[\"loan_amnt\"].quantile([0, 0.05, 0.25, 0.5, 0.75, 0.95, 1])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -602,7 +351,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -611,30 +360,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f3bc79bb128>]"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.xticks(rotation = 90)\n",
     "plt.yscale(\"logit\")\n",
@@ -643,105 +371,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>loan_amnt_cat</th>\n",
-       "      <th>nrow</th>\n",
-       "      <th>mean_bad_loan</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>(0.0,5000.0]</td>\n",
-       "      <td>25785</td>\n",
-       "      <td>0.163234</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>(5000.0,10000.0]</td>\n",
-       "      <td>50384</td>\n",
-       "      <td>0.164278</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>(10000.0,15000.0]</td>\n",
-       "      <td>35552</td>\n",
-       "      <td>0.173436</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>(15000.0,20000.0]</td>\n",
-       "      <td>24659</td>\n",
-       "      <td>0.208281</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>(20000.0,25000.0]</td>\n",
-       "      <td>14447</td>\n",
-       "      <td>0.214508</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>(25000.0,30000.0]</td>\n",
-       "      <td>6913</td>\n",
-       "      <td>0.238825</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>(30000.0,35000.0]</td>\n",
-       "      <td>6247</td>\n",
-       "      <td>0.236594</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       loan_amnt_cat   nrow  mean_bad_loan\n",
-       "0       (0.0,5000.0]  25785       0.163234\n",
-       "1   (5000.0,10000.0]  50384       0.164278\n",
-       "2  (10000.0,15000.0]  35552       0.173436\n",
-       "3  (15000.0,20000.0]  24659       0.208281\n",
-       "4  (20000.0,25000.0]  14447       0.214508\n",
-       "5  (25000.0,30000.0]   6913       0.238825\n",
-       "6  (30000.0,35000.0]   6247       0.236594"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -766,42 +405,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<thead>\n",
-       "<tr><th>term     </th><th style=\"text-align: right;\">  Count</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "<tr><td>36 months</td><td style=\"text-align: right;\"> 129950</td></tr>\n",
-       "<tr><td>60 months</td><td style=\"text-align: right;\">  34037</td></tr>\n",
-       "</tbody>\n",
-       "</table>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "train[\"term\"].table()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -813,35 +426,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<thead>\n",
-       "<tr><th style=\"text-align: right;\">  term_60months</th><th style=\"text-align: right;\">  Count</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "<tr><td style=\"text-align: right;\">              0</td><td style=\"text-align: right;\"> 129950</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">              1</td><td style=\"text-align: right;\">  34037</td></tr>\n",
-       "</tbody>\n",
-       "</table>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "train[\"term_60months\"].table()"
    ]
@@ -857,49 +444,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<thead>\n",
-       "<tr><th>       </th><th>emp_length        </th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "<tr><td>type   </td><td>int               </td></tr>\n",
-       "<tr><td>mins   </td><td>0.0               </td></tr>\n",
-       "<tr><td>mean   </td><td>5.6843529329953295</td></tr>\n",
-       "<tr><td>maxs   </td><td>10.0              </td></tr>\n",
-       "<tr><td>sigma  </td><td>3.6106637311002348</td></tr>\n",
-       "<tr><td>zeros  </td><td>14248             </td></tr>\n",
-       "<tr><td>missing</td><td>5804              </td></tr>\n",
-       "<tr><td>0      </td><td>10.0              </td></tr>\n",
-       "<tr><td>1      </td><td>0.0               </td></tr>\n",
-       "<tr><td>2      </td><td>10.0              </td></tr>\n",
-       "<tr><td>3      </td><td>10.0              </td></tr>\n",
-       "<tr><td>4      </td><td>3.0               </td></tr>\n",
-       "<tr><td>5      </td><td>9.0               </td></tr>\n",
-       "<tr><td>6      </td><td>4.0               </td></tr>\n",
-       "<tr><td>7      </td><td>0.0               </td></tr>\n",
-       "<tr><td>8      </td><td>5.0               </td></tr>\n",
-       "<tr><td>9      </td><td>10.0              </td></tr>\n",
-       "</tbody>\n",
-       "</table>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "train[\"emp_length\"].summary()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -910,65 +464,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/h2o/Miniconda3/envs/h2o/lib/python3.6/site-packages/h2o/utils/shared_utils.py:177: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
-      "  data = _handle_python_lists(python_obj.as_matrix().tolist(), -1)[1]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<thead>\n",
-       "<tr><th style=\"text-align: right;\">  emp_length_missing</th><th style=\"text-align: right;\">  cv_fold</th><th style=\"text-align: right;\">  te_emp_length_missing</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "<tr><td style=\"text-align: right;\">                   0</td><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">               -1.52145</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">                   1</td><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">               -1.03907</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">                   0</td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">               -1.51691</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">                   1</td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">               -1.02443</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">                   0</td><td style=\"text-align: right;\">        2</td><td style=\"text-align: right;\">               -1.51666</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">                   1</td><td style=\"text-align: right;\">        2</td><td style=\"text-align: right;\">               -1.05314</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">                   0</td><td style=\"text-align: right;\">        3</td><td style=\"text-align: right;\">               -1.51154</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">                   1</td><td style=\"text-align: right;\">        3</td><td style=\"text-align: right;\">               -1.02223</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">                   0</td><td style=\"text-align: right;\">        4</td><td style=\"text-align: right;\">               -1.51157</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">                   1</td><td style=\"text-align: right;\">        4</td><td style=\"text-align: right;\">               -1.01582</td></tr>\n",
-       "</tbody>\n",
-       "</table>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "mean_target_encoding(train, \"emp_length_missing\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -977,30 +482,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f3bc78aa588>]"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.yscale(\"logit\")\n",
     "plt.plot(df[\"emp_length\"], df[\"mean_bad_loan\"])"
@@ -1008,133 +492,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>emp_length</th>\n",
-       "      <th>nrow</th>\n",
-       "      <th>mean_bad_loan</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.262922</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>14248</td>\n",
-       "      <td>0.184307</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>11414</td>\n",
-       "      <td>0.177238</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>15766</td>\n",
-       "      <td>0.173728</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>13611</td>\n",
-       "      <td>0.178679</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>4.0</td>\n",
-       "      <td>11024</td>\n",
-       "      <td>0.173258</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>5.0</td>\n",
-       "      <td>12347</td>\n",
-       "      <td>0.181664</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>6.0</td>\n",
-       "      <td>10000</td>\n",
-       "      <td>0.188500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>7.0</td>\n",
-       "      <td>9079</td>\n",
-       "      <td>0.190770</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>8.0</td>\n",
-       "      <td>7424</td>\n",
-       "      <td>0.185614</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>9.0</td>\n",
-       "      <td>6087</td>\n",
-       "      <td>0.187120</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>10.0</td>\n",
-       "      <td>47183</td>\n",
-       "      <td>0.177670</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    emp_length   nrow  mean_bad_loan\n",
-       "0          NaN      0       0.262922\n",
-       "1          0.0  14248       0.184307\n",
-       "2          1.0  11414       0.177238\n",
-       "3          2.0  15766       0.173728\n",
-       "4          3.0  13611       0.178679\n",
-       "5          4.0  11024       0.173258\n",
-       "6          5.0  12347       0.181664\n",
-       "7          6.0  10000       0.188500\n",
-       "8          7.0   9079       0.190770\n",
-       "9          8.0   7424       0.185614\n",
-       "10         9.0   6087       0.187120\n",
-       "11        10.0  47183       0.177670"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df"
    ]
@@ -1150,98 +510,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>home_ownership</th>\n",
-       "      <th>nrow</th>\n",
-       "      <th>mean_bad_loan</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>ANY</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>MORTGAGE</td>\n",
-       "      <td>79714</td>\n",
-       "      <td>0.164137</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>NONE</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.233333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>OTHER</td>\n",
-       "      <td>156</td>\n",
-       "      <td>0.224359</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>OWN</td>\n",
-       "      <td>13560</td>\n",
-       "      <td>0.188348</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>RENT</td>\n",
-       "      <td>70526</td>\n",
-       "      <td>0.203273</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  home_ownership   nrow  mean_bad_loan\n",
-       "0            ANY      1       0.000000\n",
-       "1       MORTGAGE  79714       0.164137\n",
-       "2           NONE     30       0.233333\n",
-       "3          OTHER    156       0.224359\n",
-       "4            OWN  13560       0.188348\n",
-       "5           RENT  70526       0.203273"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "mean_target(train, \"home_ownership\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1251,116 +529,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<thead>\n",
-       "<tr><th>home_ownership  </th><th>home_ownership_3cat  </th><th style=\"text-align: right;\">  Counts</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "<tr><td>ANY             </td><td>OTHER                </td><td style=\"text-align: right;\">       1</td></tr>\n",
-       "<tr><td>MORTGAGE        </td><td>MORTGAGE             </td><td style=\"text-align: right;\">   79714</td></tr>\n",
-       "<tr><td>NONE            </td><td>OTHER                </td><td style=\"text-align: right;\">      30</td></tr>\n",
-       "<tr><td>OTHER           </td><td>OTHER                </td><td style=\"text-align: right;\">     156</td></tr>\n",
-       "<tr><td>OWN             </td><td>OWN                  </td><td style=\"text-align: right;\">   13560</td></tr>\n",
-       "<tr><td>RENT            </td><td>OTHER                </td><td style=\"text-align: right;\">   70526</td></tr>\n",
-       "</tbody>\n",
-       "</table>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "train[[\"home_ownership\", \"home_ownership_3cat\"]].table()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>home_ownership_3cat</th>\n",
-       "      <th>nrow</th>\n",
-       "      <th>mean_bad_loan</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>MORTGAGE</td>\n",
-       "      <td>79714</td>\n",
-       "      <td>0.164137</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>OTHER</td>\n",
-       "      <td>70713</td>\n",
-       "      <td>0.203329</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>OWN</td>\n",
-       "      <td>13560</td>\n",
-       "      <td>0.188348</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  home_ownership_3cat   nrow  mean_bad_loan\n",
-       "0            MORTGAGE  79714       0.164137\n",
-       "1               OTHER  70713       0.203329\n",
-       "2                 OWN  13560       0.188348"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "mean_target(train, \"home_ownership_3cat\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1384,47 +571,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<thead>\n",
-       "<tr><th style=\"text-align: right;\">  Probs</th><th style=\"text-align: right;\">  annual_incQuantiles</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "<tr><td style=\"text-align: right;\">   0   </td><td style=\"text-align: right;\">       1896          </td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.05</td><td style=\"text-align: right;\">      27000          </td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.25</td><td style=\"text-align: right;\">      45000          </td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.5 </td><td style=\"text-align: right;\">      61000          </td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.75</td><td style=\"text-align: right;\">      85000          </td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.95</td><td style=\"text-align: right;\">     145000          </td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   1   </td><td style=\"text-align: right;\">          7.14178e+06</td></tr>\n",
-       "</tbody>\n",
-       "</table>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "train[\"annual_inc\"].quantile([0, 0.05, 0.25, 0.5, 0.75, 0.95, 1])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1434,7 +590,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1443,30 +599,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f3bc7825dd8>]"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.yscale(\"logit\")\n",
     "plt.plot(df[\"annual_inc_cat\"].index, df[\"mean_bad_loan\"])"
@@ -1474,224 +609,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>annual_inc_cat</th>\n",
-       "      <th>nrow</th>\n",
-       "      <th>mean_bad_loan</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.126695</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>(0.0,5000.0]</td>\n",
-       "      <td>25</td>\n",
-       "      <td>0.240000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>(5000.0,10000.0]</td>\n",
-       "      <td>192</td>\n",
-       "      <td>0.213542</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>(10000.0,15000.0]</td>\n",
-       "      <td>868</td>\n",
-       "      <td>0.288018</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>(15000.0,20000.0]</td>\n",
-       "      <td>1847</td>\n",
-       "      <td>0.251760</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>(20000.0,25000.0]</td>\n",
-       "      <td>3975</td>\n",
-       "      <td>0.241509</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      annual_inc_cat  nrow  mean_bad_loan\n",
-       "0                NaN     0       0.126695\n",
-       "1       (0.0,5000.0]    25       0.240000\n",
-       "2   (5000.0,10000.0]   192       0.213542\n",
-       "3  (10000.0,15000.0]   868       0.288018\n",
-       "4  (15000.0,20000.0]  1847       0.251760\n",
-       "5  (20000.0,25000.0]  3975       0.241509"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df[0:6]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>annual_inc_cat</th>\n",
-       "      <th>nrow</th>\n",
-       "      <th>mean_bad_loan</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>(95000.0,100000.0]</td>\n",
-       "      <td>5473</td>\n",
-       "      <td>0.138315</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>(100000.0,105000.0]</td>\n",
-       "      <td>2860</td>\n",
-       "      <td>0.129021</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>(105000.0,110000.0]</td>\n",
-       "      <td>3275</td>\n",
-       "      <td>0.133740</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>(110000.0,115000.0]</td>\n",
-       "      <td>1908</td>\n",
-       "      <td>0.108491</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>(115000.0,120000.0]</td>\n",
-       "      <td>3009</td>\n",
-       "      <td>0.147557</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>(120000.0,125000.0]</td>\n",
-       "      <td>1741</td>\n",
-       "      <td>0.125790</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>(125000.0,130000.0]</td>\n",
-       "      <td>1642</td>\n",
-       "      <td>0.107186</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>(130000.0,135000.0]</td>\n",
-       "      <td>974</td>\n",
-       "      <td>0.120123</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>(135000.0,140000.0]</td>\n",
-       "      <td>1235</td>\n",
-       "      <td>0.132794</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>(140000.0,145000.0]</td>\n",
-       "      <td>746</td>\n",
-       "      <td>0.107239</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>(145000.0,150000.0]</td>\n",
-       "      <td>1492</td>\n",
-       "      <td>0.136729</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         annual_inc_cat  nrow  mean_bad_loan\n",
-       "20   (95000.0,100000.0]  5473       0.138315\n",
-       "21  (100000.0,105000.0]  2860       0.129021\n",
-       "22  (105000.0,110000.0]  3275       0.133740\n",
-       "23  (110000.0,115000.0]  1908       0.108491\n",
-       "24  (115000.0,120000.0]  3009       0.147557\n",
-       "25  (120000.0,125000.0]  1741       0.125790\n",
-       "26  (125000.0,130000.0]  1642       0.107186\n",
-       "27  (130000.0,135000.0]   974       0.120123\n",
-       "28  (135000.0,140000.0]  1235       0.132794\n",
-       "29  (140000.0,145000.0]   746       0.107239\n",
-       "30  (145000.0,150000.0]  1492       0.136729"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df[20:31]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1717,31 +653,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "               purpose  Count  Percent\n",
-      "0   debt_consolidation  93261    56.87\n",
-      "1          credit_card  30792    18.78\n",
-      "2                other  10492     6.40\n",
-      "3     home_improvement   9872     6.02\n",
-      "4       major_purchase   4686     2.86\n",
-      "5       small_business   3841     2.34\n",
-      "6                  car   2842     1.73\n",
-      "7              medical   2029     1.24\n",
-      "8              wedding   1751     1.07\n",
-      "9               moving   1464     0.89\n",
-      "10               house   1245     0.76\n",
-      "11            vacation   1096     0.67\n",
-      "12         educational    418     0.25\n",
-      "13    renewable_energy    198     0.12\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "tbl = train[\"purpose\"].table().as_data_frame()\n",
     "tbl[\"Percent\"] = np.round((100 * tbl[\"Count\"]/train.nrows), 2)\n",
@@ -1752,7 +666,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1761,147 +675,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>purpose</th>\n",
-       "      <th>nrow</th>\n",
-       "      <th>mean_bad_loan</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>small_business</td>\n",
-       "      <td>3841</td>\n",
-       "      <td>0.309034</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>renewable_energy</td>\n",
-       "      <td>198</td>\n",
-       "      <td>0.227273</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>other</td>\n",
-       "      <td>10492</td>\n",
-       "      <td>0.214354</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>educational</td>\n",
-       "      <td>418</td>\n",
-       "      <td>0.210526</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>moving</td>\n",
-       "      <td>1464</td>\n",
-       "      <td>0.207650</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>medical</td>\n",
-       "      <td>2029</td>\n",
-       "      <td>0.203056</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>debt_consolidation</td>\n",
-       "      <td>93261</td>\n",
-       "      <td>0.189479</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>house</td>\n",
-       "      <td>1245</td>\n",
-       "      <td>0.182329</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>vacation</td>\n",
-       "      <td>1096</td>\n",
-       "      <td>0.178832</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>credit_card</td>\n",
-       "      <td>30792</td>\n",
-       "      <td>0.157281</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>home_improvement</td>\n",
-       "      <td>9872</td>\n",
-       "      <td>0.155895</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>wedding</td>\n",
-       "      <td>1751</td>\n",
-       "      <td>0.145060</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>major_purchase</td>\n",
-       "      <td>4686</td>\n",
-       "      <td>0.137217</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>car</td>\n",
-       "      <td>2842</td>\n",
-       "      <td>0.125968</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               purpose   nrow  mean_bad_loan\n",
-       "0       small_business   3841       0.309034\n",
-       "1     renewable_energy    198       0.227273\n",
-       "2                other  10492       0.214354\n",
-       "3          educational    418       0.210526\n",
-       "4               moving   1464       0.207650\n",
-       "5              medical   2029       0.203056\n",
-       "6   debt_consolidation  93261       0.189479\n",
-       "7                house   1245       0.182329\n",
-       "8             vacation   1096       0.178832\n",
-       "9          credit_card  30792       0.157281\n",
-       "10    home_improvement   9872       0.155895\n",
-       "11             wedding   1751       0.145060\n",
-       "12      major_purchase   4686       0.137217\n",
-       "13                 car   2842       0.125968"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df = df.sort_values(by = \"mean_bad_loan\", ascending = 0)\n",
     "df = df.reset_index(drop = True)\n",
@@ -1910,30 +686,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f3bc77ad978>]"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.xticks(rotation = 90)\n",
     "plt.yscale(\"logit\")\n",
@@ -1942,32 +697,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/h2o/Miniconda3/envs/h2o/lib/python3.6/site-packages/h2o/utils/shared_utils.py:177: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
-      "  data = _handle_python_lists(python_obj.as_matrix().tolist(), -1)[1]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "te_purpose = mean_target_encoding(train, \"purpose\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1976,7 +715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1986,20 +725,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "train[\"te_purpose\"].hist()"
    ]
@@ -2015,22 +743,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  addr_state  Count  Percent\n",
-      "0         CA  28702    17.50\n",
-      "1         NY  14285     8.71\n",
-      "2         TX  12128     7.40\n",
-      "3         FL  11396     6.95\n",
-      "4         NJ   6457     3.94\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "tbl = train[\"addr_state\"].table().as_data_frame()\n",
     "tbl[\"Percent\"] = np.round((100 * tbl[\"Count\"]/train.nrows), 2)\n",
@@ -2041,7 +756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2050,22 +765,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  addr_state  nrow  mean_bad_loan\n",
-      "0         NE    13       0.538462\n",
-      "1         TN  1327       0.238885\n",
-      "2         NV  2387       0.227901\n",
-      "3         MS   163       0.226994\n",
-      "4         IN  1463       0.217362\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "df = df.sort_values(by = \"mean_bad_loan\", ascending = 0)\n",
     "df = df.reset_index(drop = True)\n",
@@ -2074,52 +776,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "   addr_state  nrow  mean_bad_loan\n",
-      "45         WV   714       0.131653\n",
-      "46         DC   584       0.114726\n",
-      "47         ID     9       0.111111\n",
-      "48         WY   376       0.109043\n",
-      "49         ME     3       0.000000\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(df[45:50])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f3bc51fba90>]"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.yscale(\"logit\")\n",
     "plt.plot(df[\"addr_state\"], df[\"mean_bad_loan\"])"
@@ -2127,32 +795,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/h2o/Miniconda3/envs/h2o/lib/python3.6/site-packages/h2o/utils/shared_utils.py:177: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
-      "  data = _handle_python_lists(python_obj.as_matrix().tolist(), -1)[1]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "te_addr_state = mean_target_encoding(train, \"addr_state\", prior_count = 30)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2162,7 +814,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2172,37 +824,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f3bc5155470>]"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.plot(df[\"addr_state\"], df[\"mean_te_addr_state\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2211,7 +842,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2221,20 +852,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "train[\"te_addr_state\"].hist()"
    ]
@@ -2255,47 +875,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<thead>\n",
-       "<tr><th style=\"text-align: right;\">  Probs</th><th style=\"text-align: right;\">  dtiQuantiles</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "<tr><td style=\"text-align: right;\">   0   </td><td style=\"text-align: right;\">          0   </td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.05</td><td style=\"text-align: right;\">          3.79</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.25</td><td style=\"text-align: right;\">         10.23</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.5 </td><td style=\"text-align: right;\">         15.62</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.75</td><td style=\"text-align: right;\">         21.26</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.95</td><td style=\"text-align: right;\">         29.02</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   1   </td><td style=\"text-align: right;\">         39.99</td></tr>\n",
-       "</tbody>\n",
-       "</table>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "train[\"dti\"].quantile([0, 0.05, 0.25, 0.5, 0.75, 0.95, 1])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2305,7 +894,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2314,30 +903,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f3bc4fac1d0>]"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.yscale(\"logit\")\n",
     "plt.plot(df[\"dti_cat\"].index, df[\"mean_bad_loan\"])"
@@ -2345,133 +913,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>dti_cat</th>\n",
-       "      <th>nrow</th>\n",
-       "      <th>mean_bad_loan</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>(29.0,30.0]</td>\n",
-       "      <td>2329</td>\n",
-       "      <td>0.280378</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>(30.0,31.0]</td>\n",
-       "      <td>1496</td>\n",
-       "      <td>0.293449</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>(31.0,32.0]</td>\n",
-       "      <td>1282</td>\n",
-       "      <td>0.287051</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>(32.0,33.0]</td>\n",
-       "      <td>1051</td>\n",
-       "      <td>0.308278</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>(33.0,34.0]</td>\n",
-       "      <td>1025</td>\n",
-       "      <td>0.290732</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>(34.0,35.0]</td>\n",
-       "      <td>750</td>\n",
-       "      <td>0.318667</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>(35.0,36.0]</td>\n",
-       "      <td>75</td>\n",
-       "      <td>0.160000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>(36.0,37.0]</td>\n",
-       "      <td>66</td>\n",
-       "      <td>0.227273</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>(37.0,38.0]</td>\n",
-       "      <td>65</td>\n",
-       "      <td>0.123077</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>(38.0,39.0]</td>\n",
-       "      <td>59</td>\n",
-       "      <td>0.322034</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>(39.0,40.0]</td>\n",
-       "      <td>47</td>\n",
-       "      <td>0.063830</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        dti_cat  nrow  mean_bad_loan\n",
-       "30  (29.0,30.0]  2329       0.280378\n",
-       "31  (30.0,31.0]  1496       0.293449\n",
-       "32  (31.0,32.0]  1282       0.287051\n",
-       "33  (32.0,33.0]  1051       0.308278\n",
-       "34  (33.0,34.0]  1025       0.290732\n",
-       "35  (34.0,35.0]   750       0.318667\n",
-       "36  (35.0,36.0]    75       0.160000\n",
-       "37  (36.0,37.0]    66       0.227273\n",
-       "38  (37.0,38.0]    65       0.123077\n",
-       "39  (38.0,39.0]    59       0.322034\n",
-       "40  (39.0,40.0]    47       0.063830"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df[30:41]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2501,47 +952,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<thead>\n",
-       "<tr><th style=\"text-align: right;\">  Probs</th><th style=\"text-align: right;\">  delinq_2yrsQuantiles</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "<tr><td style=\"text-align: right;\">   0   </td><td style=\"text-align: right;\">                     0</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.05</td><td style=\"text-align: right;\">                     0</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.25</td><td style=\"text-align: right;\">                     0</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.5 </td><td style=\"text-align: right;\">                     0</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.75</td><td style=\"text-align: right;\">                     0</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.95</td><td style=\"text-align: right;\">                     1</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   1   </td><td style=\"text-align: right;\">                    29</td></tr>\n",
-       "</tbody>\n",
-       "</table>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "train[\"delinq_2yrs\"].quantile([0, 0.05, 0.25, 0.5, 0.75, 0.95, 1])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2551,98 +971,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>delinq_2yrs_cat</th>\n",
-       "      <th>nrow</th>\n",
-       "      <th>mean_bad_loan</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.181037</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>(0.0,1.0]</td>\n",
-       "      <td>17158</td>\n",
-       "      <td>0.189299</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>(1.0,2.0]</td>\n",
-       "      <td>4635</td>\n",
-       "      <td>0.201510</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>(2.0,3.0]</td>\n",
-       "      <td>1488</td>\n",
-       "      <td>0.221774</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>(3.0,4.0]</td>\n",
-       "      <td>579</td>\n",
-       "      <td>0.215889</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>(4.0,5.0]</td>\n",
-       "      <td>310</td>\n",
-       "      <td>0.216129</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  delinq_2yrs_cat   nrow  mean_bad_loan\n",
-       "0             NaN      0       0.181037\n",
-       "1       (0.0,1.0]  17158       0.189299\n",
-       "2       (1.0,2.0]   4635       0.201510\n",
-       "3       (2.0,3.0]   1488       0.221774\n",
-       "4       (3.0,4.0]    579       0.215889\n",
-       "5       (4.0,5.0]    310       0.216129"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "mean_target(train, \"delinq_2yrs_cat\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2672,47 +1010,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<thead>\n",
-       "<tr><th style=\"text-align: right;\">  Probs</th><th style=\"text-align: right;\">  revol_utilQuantiles</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "<tr><td style=\"text-align: right;\">   0   </td><td style=\"text-align: right;\">                  0  </td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.05</td><td style=\"text-align: right;\">                  8.7</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.25</td><td style=\"text-align: right;\">                 35.6</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.5 </td><td style=\"text-align: right;\">                 55.8</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.75</td><td style=\"text-align: right;\">                 74.2</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.95</td><td style=\"text-align: right;\">                 92.5</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   1   </td><td style=\"text-align: right;\">                150.7</td></tr>\n",
-       "</tbody>\n",
-       "</table>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "train[\"revol_util\"].quantile([0, 0.05, 0.25, 0.5, 0.75, 0.95, 1])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2722,7 +1029,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2731,30 +1038,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f3bc4f53470>]"
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.yscale(\"logit\")\n",
     "plt.plot(df[\"revol_util_cat\"].index, df[\"mean_bad_loan\"])"
@@ -2762,91 +1048,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>revol_util_cat</th>\n",
-       "      <th>nrow</th>\n",
-       "      <th>mean_bad_loan</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>(95.0,100.0]</td>\n",
-       "      <td>4316</td>\n",
-       "      <td>0.255097</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>(100.0,105.0]</td>\n",
-       "      <td>238</td>\n",
-       "      <td>0.260504</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>(105.0,110.0]</td>\n",
-       "      <td>35</td>\n",
-       "      <td>0.200000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>(110.0,115.0]</td>\n",
-       "      <td>9</td>\n",
-       "      <td>0.333333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>(115.0,120.0]</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   revol_util_cat  nrow  mean_bad_loan\n",
-       "20   (95.0,100.0]  4316       0.255097\n",
-       "21  (100.0,105.0]   238       0.260504\n",
-       "22  (105.0,110.0]    35       0.200000\n",
-       "23  (110.0,115.0]     9       0.333333\n",
-       "24  (115.0,120.0]     3       0.000000"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df[20:25]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2878,47 +1089,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<thead>\n",
-       "<tr><th style=\"text-align: right;\">  Probs</th><th style=\"text-align: right;\">  total_accQuantiles</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "<tr><td style=\"text-align: right;\">   0   </td><td style=\"text-align: right;\">                   1</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.05</td><td style=\"text-align: right;\">                   8</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.25</td><td style=\"text-align: right;\">                  16</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.5 </td><td style=\"text-align: right;\">                  23</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.75</td><td style=\"text-align: right;\">                  31</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.95</td><td style=\"text-align: right;\">                  46</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   1   </td><td style=\"text-align: right;\">                 118</td></tr>\n",
-       "</tbody>\n",
-       "</table>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "train[\"total_acc\"].quantile([0, 0.05, 0.25, 0.5, 0.75, 0.95, 1])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2928,7 +1108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2937,30 +1117,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f3bc502b8d0>]"
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.yscale(\"logit\")\n",
     "plt.plot(df[\"total_acc_cat\"].index, df[\"mean_bad_loan\"])"
@@ -2968,196 +1127,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<thead>\n",
-       "<tr><th style=\"text-align: right;\">  C1</th><th style=\"text-align: right;\">  Count</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "<tr><td style=\"text-align: right;\">   0</td><td style=\"text-align: right;\"> 163958</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   1</td><td style=\"text-align: right;\">     29</td></tr>\n",
-       "</tbody>\n",
-       "</table>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "(train[\"total_acc\"] == None).table()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>total_acc_cat</th>\n",
-       "      <th>nrow</th>\n",
-       "      <th>mean_bad_loan</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.153788</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>(0.0,5.0]</td>\n",
-       "      <td>2309</td>\n",
-       "      <td>0.226072</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>(5.0,10.0]</td>\n",
-       "      <td>12717</td>\n",
-       "      <td>0.210034</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  total_acc_cat   nrow  mean_bad_loan\n",
-       "0           NaN      0       0.153788\n",
-       "1     (0.0,5.0]   2309       0.226072\n",
-       "2    (5.0,10.0]  12717       0.210034"
-      ]
-     },
-     "execution_count": 88,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df[0:3]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>total_acc_cat</th>\n",
-       "      <th>nrow</th>\n",
-       "      <th>mean_bad_loan</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>(35.0,40.0]</td>\n",
-       "      <td>11251</td>\n",
-       "      <td>0.163719</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>(40.0,45.0]</td>\n",
-       "      <td>7174</td>\n",
-       "      <td>0.161137</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>(45.0,50.0]</td>\n",
-       "      <td>4203</td>\n",
-       "      <td>0.157507</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>(50.0,55.0]</td>\n",
-       "      <td>2350</td>\n",
-       "      <td>0.140426</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>(55.0,60.0]</td>\n",
-       "      <td>1287</td>\n",
-       "      <td>0.165501</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   total_acc_cat   nrow  mean_bad_loan\n",
-       "8    (35.0,40.0]  11251       0.163719\n",
-       "9    (40.0,45.0]   7174       0.161137\n",
-       "10   (45.0,50.0]   4203       0.157507\n",
-       "11   (50.0,55.0]   2350       0.140426\n",
-       "12   (55.0,60.0]   1287       0.165501"
-      ]
-     },
-     "execution_count": 89,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df[8:13]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3186,47 +1183,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<thead>\n",
-       "<tr><th style=\"text-align: right;\">  Probs</th><th style=\"text-align: right;\">  longest_credit_lengthQuantiles</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "<tr><td style=\"text-align: right;\">   0   </td><td style=\"text-align: right;\">                               0</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.05</td><td style=\"text-align: right;\">                               6</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.25</td><td style=\"text-align: right;\">                              10</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.5 </td><td style=\"text-align: right;\">                              14</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.75</td><td style=\"text-align: right;\">                              18</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   0.95</td><td style=\"text-align: right;\">                              28</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">   1   </td><td style=\"text-align: right;\">                              65</td></tr>\n",
-       "</tbody>\n",
-       "</table>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 91,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "train[\"longest_credit_length\"].quantile([0, 0.05, 0.25, 0.5, 0.75, 0.95, 1])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3236,7 +1202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3245,30 +1211,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f3bc52ddf60>]"
-      ]
-     },
-     "execution_count": 94,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.yscale(\"logit\")\n",
     "plt.plot(df[\"longest_credit_length_cat\"].index, df[\"mean_bad_loan\"])"
@@ -3276,175 +1221,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>longest_credit_length_cat</th>\n",
-       "      <th>nrow</th>\n",
-       "      <th>mean_bad_loan</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.171094</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>(0.0,1.0]</td>\n",
-       "      <td>67</td>\n",
-       "      <td>0.253731</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>(1.0,2.0]</td>\n",
-       "      <td>100</td>\n",
-       "      <td>0.300000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>(2.0,3.0]</td>\n",
-       "      <td>914</td>\n",
-       "      <td>0.205689</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  longest_credit_length_cat  nrow  mean_bad_loan\n",
-       "0                       NaN     0       0.171094\n",
-       "1                 (0.0,1.0]    67       0.253731\n",
-       "2                 (1.0,2.0]   100       0.300000\n",
-       "3                 (2.0,3.0]   914       0.205689"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df[0:4]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>longest_credit_length_cat</th>\n",
-       "      <th>nrow</th>\n",
-       "      <th>mean_bad_loan</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>(19.0,20.0]</td>\n",
-       "      <td>4682</td>\n",
-       "      <td>0.163392</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>(20.0,21.0]</td>\n",
-       "      <td>3892</td>\n",
-       "      <td>0.157760</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>(21.0,22.0]</td>\n",
-       "      <td>3350</td>\n",
-       "      <td>0.160000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>(22.0,23.0]</td>\n",
-       "      <td>3092</td>\n",
-       "      <td>0.167206</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>(23.0,24.0]</td>\n",
-       "      <td>2856</td>\n",
-       "      <td>0.176821</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>(24.0,25.0]</td>\n",
-       "      <td>2471</td>\n",
-       "      <td>0.164711</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   longest_credit_length_cat  nrow  mean_bad_loan\n",
-       "20               (19.0,20.0]  4682       0.163392\n",
-       "21               (20.0,21.0]  3892       0.157760\n",
-       "22               (21.0,22.0]  3350       0.160000\n",
-       "23               (22.0,23.0]  3092       0.167206\n",
-       "24               (23.0,24.0]  2856       0.176821\n",
-       "25               (24.0,25.0]  2471       0.164711"
-      ]
-     },
-     "execution_count": 96,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df[20:26]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3471,42 +1266,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<thead>\n",
-       "<tr><th>verification_status  </th><th style=\"text-align: right;\">  Count</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "<tr><td>not verified         </td><td style=\"text-align: right;\">  59155</td></tr>\n",
-       "<tr><td>verified             </td><td style=\"text-align: right;\"> 104832</td></tr>\n",
-       "</tbody>\n",
-       "</table>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 98,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "train[\"verification_status\"].table()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3518,35 +1287,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<thead>\n",
-       "<tr><th style=\"text-align: right;\">  verified</th><th style=\"text-align: right;\">  Count</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "<tr><td style=\"text-align: right;\">         0</td><td style=\"text-align: right;\">  59155</td></tr>\n",
-       "<tr><td style=\"text-align: right;\">         1</td><td style=\"text-align: right;\"> 104832</td></tr>\n",
-       "</tbody>\n",
-       "</table>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 100,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "train[\"verified\"].table()"
    ]
@@ -3560,74 +1303,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<thead>\n",
-       "<tr><th>model_id                                             </th><th style=\"text-align: right;\">     auc</th><th style=\"text-align: right;\">  logloss</th><th style=\"text-align: right;\">  mean_per_class_error</th><th style=\"text-align: right;\">    rmse</th><th style=\"text-align: right;\">     mse</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "<tr><td>StackedEnsemble_AllModels_0_AutoML_20180816_011533   </td><td style=\"text-align: right;\">0.682867</td><td style=\"text-align: right;\"> 0.446775</td><td style=\"text-align: right;\">              0.367415</td><td style=\"text-align: right;\">0.374918</td><td style=\"text-align: right;\">0.140563</td></tr>\n",
-       "<tr><td>StackedEnsemble_BestOfFamily_0_AutoML_20180816_011533</td><td style=\"text-align: right;\">0.682505</td><td style=\"text-align: right;\"> 0.446838</td><td style=\"text-align: right;\">              0.3684  </td><td style=\"text-align: right;\">0.374941</td><td style=\"text-align: right;\">0.140581</td></tr>\n",
-       "<tr><td>GBM_grid_0_AutoML_20180816_011533_model_0            </td><td style=\"text-align: right;\">0.680493</td><td style=\"text-align: right;\"> 0.445819</td><td style=\"text-align: right;\">              0.369521</td><td style=\"text-align: right;\">0.374655</td><td style=\"text-align: right;\">0.140366</td></tr>\n",
-       "<tr><td>GBM_grid_0_AutoML_20180816_011533_model_1            </td><td style=\"text-align: right;\">0.677665</td><td style=\"text-align: right;\"> 0.447037</td><td style=\"text-align: right;\">              0.372697</td><td style=\"text-align: right;\">0.375264</td><td style=\"text-align: right;\">0.140823</td></tr>\n",
-       "<tr><td>GBM_grid_0_AutoML_20180816_011533_model_2            </td><td style=\"text-align: right;\">0.676167</td><td style=\"text-align: right;\"> 0.44776 </td><td style=\"text-align: right;\">              0.372026</td><td style=\"text-align: right;\">0.375597</td><td style=\"text-align: right;\">0.141073</td></tr>\n",
-       "<tr><td>GLM_grid_0_AutoML_20180816_011533_model_0            </td><td style=\"text-align: right;\">0.674138</td><td style=\"text-align: right;\"> 0.44873 </td><td style=\"text-align: right;\">              0.373348</td><td style=\"text-align: right;\">0.375609</td><td style=\"text-align: right;\">0.141082</td></tr>\n",
-       "<tr><td>XRT_0_AutoML_20180816_011533                         </td><td style=\"text-align: right;\">0.664243</td><td style=\"text-align: right;\"> 0.452034</td><td style=\"text-align: right;\">              0.379151</td><td style=\"text-align: right;\">0.377113</td><td style=\"text-align: right;\">0.142214</td></tr>\n",
-       "<tr><td>DRF_0_AutoML_20180816_011533                         </td><td style=\"text-align: right;\">0.658237</td><td style=\"text-align: right;\"> 0.456314</td><td style=\"text-align: right;\">              0.385645</td><td style=\"text-align: right;\">0.378535</td><td style=\"text-align: right;\">0.143289</td></tr>\n",
-       "</tbody>\n",
-       "</table>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(aml.leaderboard)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['emp_length', 'loan_amnt_core', 'loan_amnt_delta', 'term_60months', 'emp_length_missing', 'home_ownership_3cat', 'annual_inc_core', 'annual_inc_delta', 'te_purpose', 'te_addr_state', 'dti_core', 'dti_delta', 'delinq_2yrs_core', 'delinq_2yrs_delta', 'revol_util_0', 'revol_util_core', 'revol_util_delta', 'total_acc_core', 'total_acc_delta', 'longest_credit_length_core', 'longest_credit_length_delta', 'verified']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(x_trans)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "AutoML progress: |████████████████████████████████████████████████████████| 100%\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "aml_trans = H2OAutoML(max_models = 6, exclude_algos = ['DeepLearning'])\n",
     "aml_trans.train(x = x_trans, y = y, training_frame = train)"
@@ -3635,47 +1331,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<thead>\n",
-       "<tr><th>model_id                                             </th><th style=\"text-align: right;\">     auc</th><th style=\"text-align: right;\">  logloss</th><th style=\"text-align: right;\">  mean_per_class_error</th><th style=\"text-align: right;\">    rmse</th><th style=\"text-align: right;\">     mse</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "<tr><td>StackedEnsemble_AllModels_0_AutoML_20180816_012302   </td><td style=\"text-align: right;\">0.683555</td><td style=\"text-align: right;\"> 0.445507</td><td style=\"text-align: right;\">              0.367222</td><td style=\"text-align: right;\">0.374212</td><td style=\"text-align: right;\">0.140034</td></tr>\n",
-       "<tr><td>StackedEnsemble_BestOfFamily_0_AutoML_20180816_012302</td><td style=\"text-align: right;\">0.682983</td><td style=\"text-align: right;\"> 0.445696</td><td style=\"text-align: right;\">              0.367389</td><td style=\"text-align: right;\">0.374318</td><td style=\"text-align: right;\">0.140114</td></tr>\n",
-       "<tr><td>GBM_grid_0_AutoML_20180816_012302_model_0            </td><td style=\"text-align: right;\">0.682355</td><td style=\"text-align: right;\"> 0.444445</td><td style=\"text-align: right;\">              0.367104</td><td style=\"text-align: right;\">0.373941</td><td style=\"text-align: right;\">0.139832</td></tr>\n",
-       "<tr><td>GBM_grid_0_AutoML_20180816_012302_model_1            </td><td style=\"text-align: right;\">0.680878</td><td style=\"text-align: right;\"> 0.444939</td><td style=\"text-align: right;\">              0.368658</td><td style=\"text-align: right;\">0.374165</td><td style=\"text-align: right;\">0.139999</td></tr>\n",
-       "<tr><td>GBM_grid_0_AutoML_20180816_012302_model_2            </td><td style=\"text-align: right;\">0.679493</td><td style=\"text-align: right;\"> 0.445494</td><td style=\"text-align: right;\">              0.37073 </td><td style=\"text-align: right;\">0.374446</td><td style=\"text-align: right;\">0.14021 </td></tr>\n",
-       "<tr><td>GLM_grid_0_AutoML_20180816_012302_model_0            </td><td style=\"text-align: right;\">0.676563</td><td style=\"text-align: right;\"> 0.446299</td><td style=\"text-align: right;\">              0.372203</td><td style=\"text-align: right;\">0.374714</td><td style=\"text-align: right;\">0.140411</td></tr>\n",
-       "<tr><td>XRT_0_AutoML_20180816_012302                         </td><td style=\"text-align: right;\">0.665792</td><td style=\"text-align: right;\"> 0.451347</td><td style=\"text-align: right;\">              0.38011 </td><td style=\"text-align: right;\">0.376881</td><td style=\"text-align: right;\">0.142039</td></tr>\n",
-       "<tr><td>DRF_0_AutoML_20180816_012302                         </td><td style=\"text-align: right;\">0.662781</td><td style=\"text-align: right;\"> 0.452671</td><td style=\"text-align: right;\">              0.383349</td><td style=\"text-align: right;\">0.377423</td><td style=\"text-align: right;\">0.142448</td></tr>\n",
-       "</tbody>\n",
-       "</table>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(aml_trans.leaderboard)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3684,101 +1349,63 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1008x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
+   "source": [
+    "best_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "best_model_trans.varimp_plot()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "AUC (orig): train = 0.7264, valid = 0.6875, xval = 0.6805\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "print(\"AUC (orig): train = {:.4f}, valid = {:.4f}, xval = {:.4f}\" \\\n",
+    "print(\"AUC (orig): train = {:.4f}, xval = {:.4f}\" \\\n",
     "      .format(best_model.auc(train = True),\n",
-    "              best_model.auc(valid = True),\n",
     "              best_model.auc(xval = True)))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "AUC (trans): train = 0.7153, valid = 0.6846, xval = 0.6824\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "print(\"AUC (trans): train = {:.4f}, valid = {:.4f}, xval = {:.4f}\" \\\n",
+    "print(\"AUC (trans): train = {:.4f}, xval = {:.4f}\" \\\n",
     "      .format(best_model_trans.auc(train = True),\n",
-    "              best_model_trans.auc(valid = True),\n",
     "              best_model_trans.auc(xval = True)))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Logloss (orig): train = 0.4277, valid = 0.4384, xval = 0.4458\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "print(\"Logloss (orig): train = {:.4f}, valid = {:.4f}, xval = {:.4f}\" \\\n",
+    "print(\"Logloss (orig): train = {:.4f}, xval = {:.4f}\" \\\n",
     "      .format(best_model.logloss(train = True),\n",
-    "              best_model.logloss(valid = True),\n",
     "              best_model.logloss(xval = True)))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Logloss (trans): train = 0.4308, valid = 0.4419, xval = 0.4444\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "print(\"Logloss (trans): train = {:.4f}, valid = {:.4f}, xval = {:.4f}\" \\\n",
+    "print(\"Logloss (trans): train = {:.4f}, xval = {:.4f}\" \\\n",
     "      .format(best_model_trans.logloss(train = True),\n",
-    "              best_model_trans.logloss(valid = True),\n",
     "              best_model_trans.logloss(xval = True)))"
    ]
   },
@@ -3791,20 +1418,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "H2O session _sid_b7ac closed.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "h2o.cluster().shutdown()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -3823,7 +1449,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.7.0"
   }
  },
  "nbformat": 4,
diff --git a/training/h2o_3_hands_on/automl/lending_club_automl_prerun.ipynb b/training/h2o_3_hands_on/automl/lending_club_automl_prerun.ipynb
new file mode 100644
index 000000000..e26ebca39
--- /dev/null
+++ b/training/h2o_3_hands_on/automl/lending_club_automl_prerun.ipynb
@@ -0,0 +1,5103 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lending Club Analysis Using AutoML"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup H2O Cluster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import h2o"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "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_191\"; Java(TM) SE Runtime Environment (build 1.8.0_191-b12); Java HotSpot(TM) 64-Bit Server VM (build 25.191-b12, mixed mode)\n",
+      "  Starting server from /home/h2o/Miniconda3/envs/h2o/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar\n",
+      "  Ice root: /tmp/tmpp_c36dvh\n",
+      "  JVM stdout: /tmp/tmpp_c36dvh/h2o_unknownUser_started_from_python.out\n",
+      "  JVM stderr: /tmp/tmpp_c36dvh/h2o_unknownUser_started_from_python.err\n",
+      "  Server is running at http://127.0.0.1:54321\n",
+      "Connecting to H2O server at http://127.0.0.1:54321... successful.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime:</td>\n",
+       "<td>02 secs</td></tr>\n",
+       "<tr><td>H2O cluster timezone:</td>\n",
+       "<td>Etc/UTC</td></tr>\n",
+       "<tr><td>H2O data parsing timezone:</td>\n",
+       "<td>UTC</td></tr>\n",
+       "<tr><td>H2O cluster version:</td>\n",
+       "<td>3.22.1.3</td></tr>\n",
+       "<tr><td>H2O cluster version age:</td>\n",
+       "<td>9 days </td></tr>\n",
+       "<tr><td>H2O cluster name:</td>\n",
+       "<td>H2O_from_python_unknownUser_u1uow5</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>5.333 Gb</td></tr>\n",
+       "<tr><td>H2O cluster total cores:</td>\n",
+       "<td>16</td></tr>\n",
+       "<tr><td>H2O cluster allowed cores:</td>\n",
+       "<td>16</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>H2O API Extensions:</td>\n",
+       "<td>XGBoost, Algos, AutoML, Core V3, Core V4</td></tr>\n",
+       "<tr><td>Python version:</td>\n",
+       "<td>3.6.6 final</td></tr></table></div>"
+      ],
+      "text/plain": [
+       "--------------------------  ----------------------------------------\n",
+       "H2O cluster uptime:         02 secs\n",
+       "H2O cluster timezone:       Etc/UTC\n",
+       "H2O data parsing timezone:  UTC\n",
+       "H2O cluster version:        3.22.1.3\n",
+       "H2O cluster version age:    9 days\n",
+       "H2O cluster name:           H2O_from_python_unknownUser_u1uow5\n",
+       "H2O cluster total nodes:    1\n",
+       "H2O cluster free memory:    5.333 Gb\n",
+       "H2O cluster total cores:    16\n",
+       "H2O cluster allowed cores:  16\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",
+       "H2O API Extensions:         XGBoost, Algos, AutoML, Core V3, Core V4\n",
+       "Python version:             3.6.6 final\n",
+       "--------------------------  ----------------------------------------"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "h2o.init(max_mem_size = \"6g\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import data and Manage Data Types\n",
+    "\n",
+    "This exploration of H2O will use a version of the Lending Club Loan Data that can be found on [Kaggle](https://www.kaggle.com/wendykan/lending-club-loan-data). This data consists of 15 variables:\n",
+    "\n",
+    "|     | Column Name | Description |\n",
+    "| --- | ----------- | ----------- |\n",
+    "|   1 | loan_amnt   | Requested loan amount (US dollars) |\n",
+    "|   2 | term        | Loan term length (months) |\n",
+    "|   3 | int_rate    | Recommended interest rate |\n",
+    "|   4 | emp_length  | Employment length (years) |\n",
+    "|   5 | home_ownership| Housing status |\n",
+    "|   6 | annual_inc  | Annual income (US dollars) |\n",
+    "|   7 | purpose     | Purpose for the loan |\n",
+    "|   8 | addr_state  | State of residence |\n",
+    "|   9 | dti         | Debt to income ratio |\n",
+    "|  10 | delinq_2yrs | Number of delinquencies in the past 2 years |\n",
+    "|  11 | revol_util  | Percent of revolving credit line utilized |\n",
+    "|  12 | total_acc   | Number of active accounts |\n",
+    "|  13 | bad_loan    | Bad loan indicator |\n",
+    "|  14 | longest_credit_length | Age of oldest active account |\n",
+    "|  15 | verification_status | Income verification status |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# https://s3-us-west-2.amazonaws.com/h2o-tutorials/data/topics/data/automl/loan.csv\n",
+    "train = h2o.import_file(\"../../data/topics/automl/loan.csv\")\n",
+    "train[\"bad_loan\"] = train[\"bad_loan\"].asfactor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "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>13074.169141456337</td><td>         </td><td>13.715904065566173</td><td>5.684352932995329</td><td>                </td><td>71915.670519749  </td><td>                  </td><td>            </td><td>15.881530121290105</td><td>0.2273570060625282</td><td>54.07917280242256 </td><td>24.579733834274627</td><td>          </td><td>14.854273655448347     </td><td>                     </td></tr>\n",
+       "<tr><td>maxs   </td><td>35000.0           </td><td>         </td><td>26.06             </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>7993.55618873465  </td><td>         </td><td>4.3919398705457935</td><td>3.610663731100236</td><td>                </td><td>59070.91565491825</td><td>                  </td><td>            </td><td>7.587668224192548 </td><td>0.6941679229284182</td><td>25.285366766770505</td><td>11.685190365910659</td><td>          </td><td>6.947732922546696      </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>27.65             </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>11.2              </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>18.08             </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": [
+    "train.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train Models Using H2O's AutoML"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AutoML progress: |████████████████████████████████████████████████████████| 100%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set target and predictor variables\n",
+    "y = \"bad_loan\"\n",
+    "x = train.col_names\n",
+    "x.remove(y)\n",
+    "x.remove(\"int_rate\")\n",
+    "\n",
+    "# Use Auto ML to train models\n",
+    "from h2o.automl import H2OAutoML\n",
+    "aml = H2OAutoML(max_models = 6, exclude_algos = ['DeepLearning'])\n",
+    "aml.train(x = x, y = y, training_frame = train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th>model_id                                           </th><th style=\"text-align: right;\">     auc</th><th style=\"text-align: right;\">  logloss</th><th style=\"text-align: right;\">  mean_per_class_error</th><th style=\"text-align: right;\">    rmse</th><th style=\"text-align: right;\">     mse</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td>StackedEnsemble_AllModels_AutoML_20190204_105352   </td><td style=\"text-align: right;\">0.684653</td><td style=\"text-align: right;\"> 0.444916</td><td style=\"text-align: right;\">              0.366459</td><td style=\"text-align: right;\">0.374001</td><td style=\"text-align: right;\">0.139877</td></tr>\n",
+       "<tr><td>StackedEnsemble_BestOfFamily_AutoML_20190204_105352</td><td style=\"text-align: right;\">0.684095</td><td style=\"text-align: right;\"> 0.445076</td><td style=\"text-align: right;\">              0.3681  </td><td style=\"text-align: right;\">0.374064</td><td style=\"text-align: right;\">0.139924</td></tr>\n",
+       "<tr><td>XGBoost_1_AutoML_20190204_105352                   </td><td style=\"text-align: right;\">0.682381</td><td style=\"text-align: right;\"> 0.444104</td><td style=\"text-align: right;\">              0.36856 </td><td style=\"text-align: right;\">0.373716</td><td style=\"text-align: right;\">0.139664</td></tr>\n",
+       "<tr><td>XGBoost_2_AutoML_20190204_105352                   </td><td style=\"text-align: right;\">0.681244</td><td style=\"text-align: right;\"> 0.444554</td><td style=\"text-align: right;\">              0.369625</td><td style=\"text-align: right;\">0.37401 </td><td style=\"text-align: right;\">0.139883</td></tr>\n",
+       "<tr><td>XGBoost_3_AutoML_20190204_105352                   </td><td style=\"text-align: right;\">0.676053</td><td style=\"text-align: right;\"> 0.446428</td><td style=\"text-align: right;\">              0.372664</td><td style=\"text-align: right;\">0.374848</td><td style=\"text-align: right;\">0.140511</td></tr>\n",
+       "<tr><td>GLM_grid_1_AutoML_20190204_105352_model_1          </td><td style=\"text-align: right;\">0.674157</td><td style=\"text-align: right;\"> 0.447619</td><td style=\"text-align: right;\">              0.373543</td><td style=\"text-align: right;\">0.374973</td><td style=\"text-align: right;\">0.140605</td></tr>\n",
+       "<tr><td>XRT_1_AutoML_20190204_105352                       </td><td style=\"text-align: right;\">0.666562</td><td style=\"text-align: right;\"> 0.449967</td><td style=\"text-align: right;\">              0.379013</td><td style=\"text-align: right;\">0.376207</td><td style=\"text-align: right;\">0.141532</td></tr>\n",
+       "<tr><td>DRF_1_AutoML_20190204_105352                       </td><td style=\"text-align: right;\">0.662709</td><td style=\"text-align: right;\"> 0.45256 </td><td style=\"text-align: right;\">              0.382445</td><td style=\"text-align: right;\">0.377047</td><td style=\"text-align: right;\">0.142164</td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(aml.leaderboard)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Examine the Best Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_model = h2o.get_model(aml.leaderboard[2,'model_id'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import warnings\n",
+    "import matplotlib.cbook\n",
+    "warnings.filterwarnings(\"ignore\", category = matplotlib.cbook.mplDeprecation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "best_model.varimp_plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AUC: train = 0.6993, xval = 0.6824\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"AUC: train = {:.4f}, xval = {:.4f}\"\\\n",
+    "      .format(best_model.auc(train = True), best_model.auc(xval = True)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Logloss: train = 0.4380, xval = 0.4441\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Logloss: train = {:.4f}, xval = {:.4f}\" \\\n",
+    "      .format(best_model.logloss(train = True), best_model.logloss(xval = True)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Perform Feature Engineering\n",
+    "\n",
+    "The goal of this section is to improve upon these predictors through a number of feature engineering steps. In particular, we will perform three feature engineering tasks:\n",
+    "\n",
+    "1. **Separating Typical from Extreme Loan Amount**\n",
+    "1. **Converting Term to a 0/1 Indicator**\n",
+    "1. **Creating Missing Value Indicator for Employment Length**\n",
+    "1. **Combining Categories in Home Ownership**\n",
+    "1. **Separating Typical from Extreme Annual Income**\n",
+    "1. **Creating Target Encoding for Loan Purpose**\n",
+    "1. **Creating Target Encoding for State of Residence**\n",
+    "1. **Separating Typical from Extreme Debt to Income Ratio**\n",
+    "1. **Separating Typical from Extreme Number of Delinquencies in the Past 2 Years**\n",
+    "1. **Separating Typical from Extreme Revolving Credit Line Utilized**\n",
+    "1. **Separating Typical from Extreme Number of Credit Lines**\n",
+    "1. **Separating Typical from Extreme Longest Credit Length**\n",
+    "1. **Converting Income Verification Status to a 0/1 Indicator**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = \"bad_loan\"\n",
+    "x_orig = train.col_names\n",
+    "x_orig.remove(y)\n",
+    "x_orig.remove(\"int_rate\")\n",
+    "\n",
+    "x_trans = x_orig.copy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Cross Validation and Target Encoding\n",
+    "\n",
+    "Some of the engineered features will use [cross-validated mean target encoding](https://github.com/h2oai/h2o-tutorials/blob/master/best-practices/categorical-predictors/target_encoding.md) of categorical predictors since one hot encodings can lead to overfitting of infrequent categories.\n",
+    "\n",
+    "To achieve this goal, we will first create soft partitions using H2OFrame's [`kfold_column`](http://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/frame.html?highlight=kfold_column#h2o.frame.H2OFrame.kfold_column) function, then calculate summary statistics using H2O's [`group_by`](http://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/frame.html#groupby) function, and finally join these engineered features using H2OFrame's [`merge`](http://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/frame.html?highlight=merge#h2o.frame.H2OFrame.merge)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cv_nfolds = 5\n",
+    "cv_seed = 2307"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train[\"cv_fold\"] = train.kfold_column(n_folds = cv_nfolds, seed = cv_seed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th style=\"text-align: right;\">  cv_fold</th><th style=\"text-align: right;\">  Count</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">  32848</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">  32808</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">        2</td><td style=\"text-align: right;\">  33035</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">        3</td><td style=\"text-align: right;\">  32574</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">        4</td><td style=\"text-align: right;\">  32722</td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train[\"cv_fold\"].table()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def logit(p):\n",
+    "    return np.log(p) - np.log(1 - p)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mean_target(data, x, y = \"bad_loan\"):\n",
+    "    grouped_data = data[[x, y]].group_by([x])\n",
+    "    stats = grouped_data.count(na = \"ignore\").mean(na = \"ignore\")\n",
+    "    return stats.get_frame().as_data_frame()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mean_target_encoding(data, x, y = \"bad_loan\", fold_column = \"cv_fold\", prior_mean = 0.183, prior_count = 1):\n",
+    "    \"\"\"\n",
+    "    Creates target encoding for binary target\n",
+    "    data (H2OFrame) : data set\n",
+    "    x (string) : categorical predictor column name\n",
+    "    y (string) : binary target column name\n",
+    "    fold_column (string) : cross-validation fold column name\n",
+    "    prior_mean (float) : proportion of 1s in the target column\n",
+    "    prior_count (positive number) : weight to give to prior_mean\n",
+    "    \"\"\" \n",
+    "    grouped_data = data[[x, fold_column, y]].group_by([x, fold_column])\n",
+    "    grouped_data.sum(na = \"ignore\").count(na = \"ignore\")\n",
+    "    df = grouped_data.get_frame().as_data_frame()\n",
+    "    df_list = []\n",
+    "    nfold = int(data[fold_column].max()) + 1\n",
+    "    for j in range(0, nfold):\n",
+    "        te_x = \"te_{}\".format(x)\n",
+    "        sum_y = \"sum_{}\".format(y)\n",
+    "        oof = df.loc[df[fold_column] != j, [x, sum_y, \"nrow\"]]\n",
+    "        stats = oof.groupby([x]).sum()\n",
+    "        stats[x] = stats.index\n",
+    "        stats[fold_column] = j\n",
+    "        p = (stats[sum_y] + (prior_count * prior_mean)) / (stats[\"nrow\"] + prior_count)\n",
+    "        stats[te_x] = logit(p)\n",
+    "        df_list.append(stats[[x, fold_column, te_x]])\n",
+    "    return h2o.H2OFrame(pd.concat(df_list))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Separating Typical from Extreme Loan Amount\n",
+    "\n",
+    "After binning `loan_amt` using H2OFrame's [`cut`](http://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/frame.html?highlight=cut#h2o.frame.H2OFrame.cut) function and looking at the fraction of bad loans on a logit scale, we see that the chance of a bad loan roughly increases linearly in loan amount from \\\\$5,000 to \\\\$30,000 and is relatively flat below \\$5,000 and above \\$30,000. To reflect this finding in the modeling, we will replace the original `loan_amnt` measure with two derived measures:\n",
+    "\n",
+    "\\begin{eqnarray}\n",
+    "loan\\_amnt\\_core & = & \\max(5000, \\min(loan\\_amnt, 30000)) \\\\\n",
+    "loan\\_amnt\\_diff & = & loan\\_amnt - loan\\_amnt\\_core\n",
+    "\\end{eqnarray}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th style=\"text-align: right;\">  Probs</th><th style=\"text-align: right;\">  loan_amntQuantiles</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td style=\"text-align: right;\">   0   </td><td style=\"text-align: right;\">                 500</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.05</td><td style=\"text-align: right;\">                3000</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.25</td><td style=\"text-align: right;\">                7000</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.5 </td><td style=\"text-align: right;\">               11325</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.75</td><td style=\"text-align: right;\">               18000</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.95</td><td style=\"text-align: right;\">               30000</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   1   </td><td style=\"text-align: right;\">               35000</td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train[\"loan_amnt\"].quantile([0, 0.05, 0.25, 0.5, 0.75, 0.95, 1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "breaks = np.linspace(0, 35000, 8).tolist()\n",
+    "train[\"loan_amnt_cat\"] = train[\"loan_amnt\"].cut(breaks = breaks)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = mean_target(train, \"loan_amnt_cat\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7febe3a4cdd8>]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.xticks(rotation = 90)\n",
+    "plt.yscale(\"logit\")\n",
+    "plt.plot(df[\"loan_amnt_cat\"], df[\"mean_bad_loan\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>loan_amnt_cat</th>\n",
+       "      <th>nrow</th>\n",
+       "      <th>mean_bad_loan</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>(0.0,5000.0]</td>\n",
+       "      <td>25785</td>\n",
+       "      <td>0.163234</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>(5000.0,10000.0]</td>\n",
+       "      <td>50384</td>\n",
+       "      <td>0.164278</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>(10000.0,15000.0]</td>\n",
+       "      <td>35552</td>\n",
+       "      <td>0.173436</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>(15000.0,20000.0]</td>\n",
+       "      <td>24659</td>\n",
+       "      <td>0.208281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>(20000.0,25000.0]</td>\n",
+       "      <td>14447</td>\n",
+       "      <td>0.214508</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>(25000.0,30000.0]</td>\n",
+       "      <td>6913</td>\n",
+       "      <td>0.238825</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>(30000.0,35000.0]</td>\n",
+       "      <td>6247</td>\n",
+       "      <td>0.236594</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       loan_amnt_cat   nrow  mean_bad_loan\n",
+       "0       (0.0,5000.0]  25785       0.163234\n",
+       "1   (5000.0,10000.0]  50384       0.164278\n",
+       "2  (10000.0,15000.0]  35552       0.173436\n",
+       "3  (15000.0,20000.0]  24659       0.208281\n",
+       "4  (20000.0,25000.0]  14447       0.214508\n",
+       "5  (25000.0,30000.0]   6913       0.238825\n",
+       "6  (30000.0,35000.0]   6247       0.236594"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_trans.remove(\"loan_amnt\")\n",
+    "x_trans.append(\"loan_amnt_core\")\n",
+    "x_trans.append(\"loan_amnt_delta\")\n",
+    "\n",
+    "train[\"loan_amnt_core\"] = h2o.H2OFrame.ifelse(train[\"loan_amnt\"] <= 5000, 5000, train[\"loan_amnt\"])\n",
+    "train[\"loan_amnt_core\"] = h2o.H2OFrame.ifelse(train[\"loan_amnt_core\"] <= 30000, train[\"loan_amnt_core\"], 30000)\n",
+    "\n",
+    "train[\"loan_amnt_delta\"] = train[\"loan_amnt\"] - train[\"loan_amnt_core\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Converting Term to a 0/1 Indicator\n",
+    "\n",
+    "Given that term of the loans are either 3 or 5 years, we will create a simplifed `term_36month` binary indicator that is 1 when the terms of the loan is for 5 years and 0 for loans with a term of 3 years."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th>term     </th><th style=\"text-align: right;\">  Count</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td>36 months</td><td style=\"text-align: right;\"> 129950</td></tr>\n",
+       "<tr><td>60 months</td><td style=\"text-align: right;\">  34037</td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train[\"term\"].table()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_trans.remove(\"term\")\n",
+    "x_trans.append(\"term_60months\")\n",
+    "\n",
+    "train[\"term_60months\"] = train[\"term\"] == \"60 months\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th style=\"text-align: right;\">  term_60months</th><th style=\"text-align: right;\">  Count</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td style=\"text-align: right;\">              0</td><td style=\"text-align: right;\"> 129950</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">              1</td><td style=\"text-align: right;\">  34037</td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train[\"term_60months\"].table()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating Missing Value Indicator for Employment Length\n",
+    "\n",
+    "The most interesting characteristic about employment length is whether or not it is missing. The divide between those with missing values for employment length to those who have a recorded employment length is 26.3% bad loans to 18.0% bad loans respectively. Interestingly, there doesn't appear to be any differences in bad loans across employment lengths."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th>       </th><th>emp_length       </th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td>type   </td><td>int              </td></tr>\n",
+       "<tr><td>mins   </td><td>0.0              </td></tr>\n",
+       "<tr><td>mean   </td><td>5.684352932995329</td></tr>\n",
+       "<tr><td>maxs   </td><td>10.0             </td></tr>\n",
+       "<tr><td>sigma  </td><td>3.610663731100236</td></tr>\n",
+       "<tr><td>zeros  </td><td>14248            </td></tr>\n",
+       "<tr><td>missing</td><td>5804             </td></tr>\n",
+       "<tr><td>0      </td><td>10.0             </td></tr>\n",
+       "<tr><td>1      </td><td>0.0              </td></tr>\n",
+       "<tr><td>2      </td><td>10.0             </td></tr>\n",
+       "<tr><td>3      </td><td>10.0             </td></tr>\n",
+       "<tr><td>4      </td><td>3.0              </td></tr>\n",
+       "<tr><td>5      </td><td>9.0              </td></tr>\n",
+       "<tr><td>6      </td><td>4.0              </td></tr>\n",
+       "<tr><td>7      </td><td>0.0              </td></tr>\n",
+       "<tr><td>8      </td><td>5.0              </td></tr>\n",
+       "<tr><td>9      </td><td>10.0             </td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "train[\"emp_length\"].summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_trans.append(\"emp_length_missing\")\n",
+    "\n",
+    "train[\"emp_length_missing\"] = train[\"emp_length\"] == None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th style=\"text-align: right;\">  emp_length_missing</th><th style=\"text-align: right;\">  cv_fold</th><th style=\"text-align: right;\">  te_emp_length_missing</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td style=\"text-align: right;\">                   0</td><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">               -1.52145</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">                   1</td><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">               -1.03907</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">                   0</td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">               -1.51691</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">                   1</td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">               -1.02443</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">                   0</td><td style=\"text-align: right;\">        2</td><td style=\"text-align: right;\">               -1.51666</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">                   1</td><td style=\"text-align: right;\">        2</td><td style=\"text-align: right;\">               -1.05314</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">                   0</td><td style=\"text-align: right;\">        3</td><td style=\"text-align: right;\">               -1.51154</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">                   1</td><td style=\"text-align: right;\">        3</td><td style=\"text-align: right;\">               -1.02223</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">                   0</td><td style=\"text-align: right;\">        4</td><td style=\"text-align: right;\">               -1.51157</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">                   1</td><td style=\"text-align: right;\">        4</td><td style=\"text-align: right;\">               -1.01582</td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_target_encoding(train, \"emp_length_missing\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = mean_target(train, \"emp_length\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7febe39a6470>]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.yscale(\"logit\")\n",
+    "plt.plot(df[\"emp_length\"], df[\"mean_bad_loan\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>emp_length</th>\n",
+       "      <th>nrow</th>\n",
+       "      <th>mean_bad_loan</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.262922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>14248</td>\n",
+       "      <td>0.184307</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>11414</td>\n",
+       "      <td>0.177238</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>15766</td>\n",
+       "      <td>0.173728</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>13611</td>\n",
+       "      <td>0.178679</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>11024</td>\n",
+       "      <td>0.173258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>12347</td>\n",
+       "      <td>0.181664</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>0.188500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>9079</td>\n",
+       "      <td>0.190770</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>7424</td>\n",
+       "      <td>0.185614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>6087</td>\n",
+       "      <td>0.187120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>47183</td>\n",
+       "      <td>0.177670</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    emp_length   nrow  mean_bad_loan\n",
+       "0          NaN      0       0.262922\n",
+       "1          0.0  14248       0.184307\n",
+       "2          1.0  11414       0.177238\n",
+       "3          2.0  15766       0.173728\n",
+       "4          3.0  13611       0.178679\n",
+       "5          4.0  11024       0.173258\n",
+       "6          5.0  12347       0.181664\n",
+       "7          6.0  10000       0.188500\n",
+       "8          7.0   9079       0.190770\n",
+       "9          8.0   7424       0.185614\n",
+       "10         9.0   6087       0.187120\n",
+       "11        10.0  47183       0.177670"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Combining Categories in Home Ownership\n",
+    "\n",
+    "Although there are 6 recorded categories within home ownership, only three had over 200 observations: OWN, MORTGAGE, and RENT. The remaining three are so infrequent we will combine them {ANY, NONE, OTHER} with RENT to form an enlarged OTHER category. This new `home_ownership_3cat` variable will have values in {MORTGAGE, OTHER, OWN}."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>home_ownership</th>\n",
+       "      <th>nrow</th>\n",
+       "      <th>mean_bad_loan</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ANY</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>MORTGAGE</td>\n",
+       "      <td>79714</td>\n",
+       "      <td>0.164137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NONE</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.233333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>OTHER</td>\n",
+       "      <td>156</td>\n",
+       "      <td>0.224359</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>OWN</td>\n",
+       "      <td>13560</td>\n",
+       "      <td>0.188348</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>RENT</td>\n",
+       "      <td>70526</td>\n",
+       "      <td>0.203273</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  home_ownership   nrow  mean_bad_loan\n",
+       "0            ANY      1       0.000000\n",
+       "1       MORTGAGE  79714       0.164137\n",
+       "2           NONE     30       0.233333\n",
+       "3          OTHER    156       0.224359\n",
+       "4            OWN  13560       0.188348\n",
+       "5           RENT  70526       0.203273"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_target(train, \"home_ownership\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lvls = [\"OTHER\", \"MORTGAGE\", \"OTHER\", \"OTHER\", \"OWN\", \"OTHER\"]\n",
+    "train[\"home_ownership_3cat\"] = train[\"home_ownership\"].set_levels(lvls).ascharacter().asfactor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th>home_ownership  </th><th>home_ownership_3cat  </th><th style=\"text-align: right;\">  Counts</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td>ANY             </td><td>OTHER                </td><td style=\"text-align: right;\">       1</td></tr>\n",
+       "<tr><td>MORTGAGE        </td><td>MORTGAGE             </td><td style=\"text-align: right;\">   79714</td></tr>\n",
+       "<tr><td>NONE            </td><td>OTHER                </td><td style=\"text-align: right;\">      30</td></tr>\n",
+       "<tr><td>OTHER           </td><td>OTHER                </td><td style=\"text-align: right;\">     156</td></tr>\n",
+       "<tr><td>OWN             </td><td>OWN                  </td><td style=\"text-align: right;\">   13560</td></tr>\n",
+       "<tr><td>RENT            </td><td>OTHER                </td><td style=\"text-align: right;\">   70526</td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train[[\"home_ownership\", \"home_ownership_3cat\"]].table()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>home_ownership_3cat</th>\n",
+       "      <th>nrow</th>\n",
+       "      <th>mean_bad_loan</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>MORTGAGE</td>\n",
+       "      <td>79714</td>\n",
+       "      <td>0.164137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>OTHER</td>\n",
+       "      <td>70713</td>\n",
+       "      <td>0.203329</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>OWN</td>\n",
+       "      <td>13560</td>\n",
+       "      <td>0.188348</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  home_ownership_3cat   nrow  mean_bad_loan\n",
+       "0            MORTGAGE  79714       0.164137\n",
+       "1               OTHER  70713       0.203329\n",
+       "2                 OWN  13560       0.188348"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_target(train, \"home_ownership_3cat\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_trans.remove(\"home_ownership\")\n",
+    "x_trans.append(\"home_ownership_3cat\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Separating Typical from Extreme Annual Income\n",
+    "\n",
+    "Looking at the occurance of bad loans on a logit scale reveal that the chance of a bad loan roughly decreases linearly in annual income from \\$10,000 to \\$105,000 and is relatively flat above \\$105,000. To reflect this finding in the modeling, we will replace the original `annual_inc` measure with two derived measures:\n",
+    "\n",
+    "\\begin{eqnarray}\n",
+    "annual\\_inc\\_core & = & \\max(10000, \\min(annual\\_inc, 105000)) \\\\\n",
+    "annual\\_inc\\_diff & = & annual\\_inc - annual\\_inc\\_core\n",
+    "\\end{eqnarray}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th style=\"text-align: right;\">  Probs</th><th style=\"text-align: right;\">  annual_incQuantiles</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td style=\"text-align: right;\">   0   </td><td style=\"text-align: right;\">       1896          </td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.05</td><td style=\"text-align: right;\">      27000          </td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.25</td><td style=\"text-align: right;\">      45000          </td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.5 </td><td style=\"text-align: right;\">      61000          </td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.75</td><td style=\"text-align: right;\">      85000          </td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.95</td><td style=\"text-align: right;\">     145000          </td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   1   </td><td style=\"text-align: right;\">          7.14178e+06</td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train[\"annual_inc\"].quantile([0, 0.05, 0.25, 0.5, 0.75, 0.95, 1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "breaks = np.linspace(0, 150000, 31).tolist()\n",
+    "train[\"annual_inc_cat\"] = train[\"annual_inc\"].cut(breaks = breaks)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = mean_target(train, \"annual_inc_cat\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7febe38c4048>]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.yscale(\"logit\")\n",
+    "plt.plot(df[\"annual_inc_cat\"].index, df[\"mean_bad_loan\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>annual_inc_cat</th>\n",
+       "      <th>nrow</th>\n",
+       "      <th>mean_bad_loan</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.126695</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>(0.0,5000.0]</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.240000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>(5000.0,10000.0]</td>\n",
+       "      <td>192</td>\n",
+       "      <td>0.213542</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>(10000.0,15000.0]</td>\n",
+       "      <td>868</td>\n",
+       "      <td>0.288018</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>(15000.0,20000.0]</td>\n",
+       "      <td>1847</td>\n",
+       "      <td>0.251760</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>(20000.0,25000.0]</td>\n",
+       "      <td>3975</td>\n",
+       "      <td>0.241509</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      annual_inc_cat  nrow  mean_bad_loan\n",
+       "0                NaN     0       0.126695\n",
+       "1       (0.0,5000.0]    25       0.240000\n",
+       "2   (5000.0,10000.0]   192       0.213542\n",
+       "3  (10000.0,15000.0]   868       0.288018\n",
+       "4  (15000.0,20000.0]  1847       0.251760\n",
+       "5  (20000.0,25000.0]  3975       0.241509"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[0:6]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>annual_inc_cat</th>\n",
+       "      <th>nrow</th>\n",
+       "      <th>mean_bad_loan</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>(95000.0,100000.0]</td>\n",
+       "      <td>5473</td>\n",
+       "      <td>0.138315</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>(100000.0,105000.0]</td>\n",
+       "      <td>2860</td>\n",
+       "      <td>0.129021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>(105000.0,110000.0]</td>\n",
+       "      <td>3275</td>\n",
+       "      <td>0.133740</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>(110000.0,115000.0]</td>\n",
+       "      <td>1908</td>\n",
+       "      <td>0.108491</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>(115000.0,120000.0]</td>\n",
+       "      <td>3009</td>\n",
+       "      <td>0.147557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>(120000.0,125000.0]</td>\n",
+       "      <td>1741</td>\n",
+       "      <td>0.125790</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>(125000.0,130000.0]</td>\n",
+       "      <td>1642</td>\n",
+       "      <td>0.107186</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>(130000.0,135000.0]</td>\n",
+       "      <td>974</td>\n",
+       "      <td>0.120123</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>(135000.0,140000.0]</td>\n",
+       "      <td>1235</td>\n",
+       "      <td>0.132794</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>(140000.0,145000.0]</td>\n",
+       "      <td>746</td>\n",
+       "      <td>0.107239</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>(145000.0,150000.0]</td>\n",
+       "      <td>1492</td>\n",
+       "      <td>0.136729</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         annual_inc_cat  nrow  mean_bad_loan\n",
+       "20   (95000.0,100000.0]  5473       0.138315\n",
+       "21  (100000.0,105000.0]  2860       0.129021\n",
+       "22  (105000.0,110000.0]  3275       0.133740\n",
+       "23  (110000.0,115000.0]  1908       0.108491\n",
+       "24  (115000.0,120000.0]  3009       0.147557\n",
+       "25  (120000.0,125000.0]  1741       0.125790\n",
+       "26  (125000.0,130000.0]  1642       0.107186\n",
+       "27  (130000.0,135000.0]   974       0.120123\n",
+       "28  (135000.0,140000.0]  1235       0.132794\n",
+       "29  (140000.0,145000.0]   746       0.107239\n",
+       "30  (145000.0,150000.0]  1492       0.136729"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[20:31]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_trans.remove(\"annual_inc\")\n",
+    "x_trans.append(\"annual_inc_core\")\n",
+    "x_trans.append(\"annual_inc_delta\")\n",
+    "\n",
+    "train[\"annual_inc_core\"] = h2o.H2OFrame.ifelse(train[\"annual_inc\"] <= 10000, 10000, train[\"annual_inc\"])\n",
+    "train[\"annual_inc_core\"] = h2o.H2OFrame.ifelse(train[\"annual_inc_core\"] <= 105000,\n",
+    "                                               train[\"annual_inc_core\"], 105000)\n",
+    "\n",
+    "train[\"annual_inc_delta\"] = train[\"annual_inc\"] - train[\"annual_inc_core\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating Target Encoding for Loan Purpose\n",
+    "\n",
+    "Given that there is a high concentration of loans for debt consolidation (56.87%), a sizable number for credit card (18.78%), and the remaining 24.35% loans are spread amongst 12 other purposes, we will use mean target encoding to avoid overfitting of the later group."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "               purpose  Count  Percent\n",
+      "0   debt_consolidation  93261    56.87\n",
+      "1          credit_card  30792    18.78\n",
+      "2                other  10492     6.40\n",
+      "3     home_improvement   9872     6.02\n",
+      "4       major_purchase   4686     2.86\n",
+      "5       small_business   3841     2.34\n",
+      "6                  car   2842     1.73\n",
+      "7              medical   2029     1.24\n",
+      "8              wedding   1751     1.07\n",
+      "9               moving   1464     0.89\n",
+      "10               house   1245     0.76\n",
+      "11            vacation   1096     0.67\n",
+      "12         educational    418     0.25\n",
+      "13    renewable_energy    198     0.12\n"
+     ]
+    }
+   ],
+   "source": [
+    "tbl = train[\"purpose\"].table().as_data_frame()\n",
+    "tbl[\"Percent\"] = np.round((100 * tbl[\"Count\"]/train.nrows), 2)\n",
+    "tbl = tbl.sort_values(by = \"Count\", ascending = 0)\n",
+    "tbl = tbl.reset_index(drop = True)\n",
+    "print(tbl)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = mean_target(train, \"purpose\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>purpose</th>\n",
+       "      <th>nrow</th>\n",
+       "      <th>mean_bad_loan</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>small_business</td>\n",
+       "      <td>3841</td>\n",
+       "      <td>0.309034</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>renewable_energy</td>\n",
+       "      <td>198</td>\n",
+       "      <td>0.227273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>other</td>\n",
+       "      <td>10492</td>\n",
+       "      <td>0.214354</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>educational</td>\n",
+       "      <td>418</td>\n",
+       "      <td>0.210526</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>moving</td>\n",
+       "      <td>1464</td>\n",
+       "      <td>0.207650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>medical</td>\n",
+       "      <td>2029</td>\n",
+       "      <td>0.203056</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>debt_consolidation</td>\n",
+       "      <td>93261</td>\n",
+       "      <td>0.189479</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>house</td>\n",
+       "      <td>1245</td>\n",
+       "      <td>0.182329</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>vacation</td>\n",
+       "      <td>1096</td>\n",
+       "      <td>0.178832</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>credit_card</td>\n",
+       "      <td>30792</td>\n",
+       "      <td>0.157281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>home_improvement</td>\n",
+       "      <td>9872</td>\n",
+       "      <td>0.155895</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>wedding</td>\n",
+       "      <td>1751</td>\n",
+       "      <td>0.145060</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>major_purchase</td>\n",
+       "      <td>4686</td>\n",
+       "      <td>0.137217</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>car</td>\n",
+       "      <td>2842</td>\n",
+       "      <td>0.125968</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               purpose   nrow  mean_bad_loan\n",
+       "0       small_business   3841       0.309034\n",
+       "1     renewable_energy    198       0.227273\n",
+       "2                other  10492       0.214354\n",
+       "3          educational    418       0.210526\n",
+       "4               moving   1464       0.207650\n",
+       "5              medical   2029       0.203056\n",
+       "6   debt_consolidation  93261       0.189479\n",
+       "7                house   1245       0.182329\n",
+       "8             vacation   1096       0.178832\n",
+       "9          credit_card  30792       0.157281\n",
+       "10    home_improvement   9872       0.155895\n",
+       "11             wedding   1751       0.145060\n",
+       "12      major_purchase   4686       0.137217\n",
+       "13                 car   2842       0.125968"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df.sort_values(by = \"mean_bad_loan\", ascending = 0)\n",
+    "df = df.reset_index(drop = True)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7febe3867518>]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.xticks(rotation = 90)\n",
+    "plt.yscale(\"logit\")\n",
+    "plt.plot(df[\"purpose\"], df[\"mean_bad_loan\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
+     ]
+    }
+   ],
+   "source": [
+    "te_purpose = mean_target_encoding(train, \"purpose\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train = train.merge(te_purpose, all_x = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_trans.remove(\"purpose\")\n",
+    "x_trans.append(\"te_purpose\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "train[\"te_purpose\"].hist()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating Target Encoding for State of Residence\n",
+    "\n",
+    "We will also use a mean target encoding for state of residence for a reason similar to that for purpose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  addr_state  Count  Percent\n",
+      "0         CA  28702    17.50\n",
+      "1         NY  14285     8.71\n",
+      "2         TX  12128     7.40\n",
+      "3         FL  11396     6.95\n",
+      "4         NJ   6457     3.94\n"
+     ]
+    }
+   ],
+   "source": [
+    "tbl = train[\"addr_state\"].table().as_data_frame()\n",
+    "tbl[\"Percent\"] = np.round((100 * tbl[\"Count\"]/train.nrows), 2)\n",
+    "tbl = tbl.sort_values(by = \"Count\", ascending = 0)\n",
+    "tbl = tbl.reset_index(drop = True)\n",
+    "print(tbl[0:5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = mean_target(train, \"addr_state\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  addr_state  nrow  mean_bad_loan\n",
+      "0         NE    13       0.538462\n",
+      "1         TN  1327       0.238885\n",
+      "2         NV  2387       0.227901\n",
+      "3         MS   163       0.226994\n",
+      "4         IN  1463       0.217362\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = df.sort_values(by = \"mean_bad_loan\", ascending = 0)\n",
+    "df = df.reset_index(drop = True)\n",
+    "print(df[0:5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   addr_state  nrow  mean_bad_loan\n",
+      "45         WV   714       0.131653\n",
+      "46         DC   584       0.114726\n",
+      "47         ID     9       0.111111\n",
+      "48         WY   376       0.109043\n",
+      "49         ME     3       0.000000\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[45:50])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7febdfcf5630>]"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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XbeXlta/zzCsbac66HUQqYZQVpSgrTFJalKIolSCZMJIJa7f30L+8iEkj+zBpVF+OGFhBQheVifQIXQ39amB4xvthwJrsjszsbOCLwGnu3pDR7+lZ/T7Z3YLmOzOjpDBJSWHJbrs744gBO/9vaG5hyfptLH5tK1vqm6hvbKGuoTm84v9NLa07NxrpDcj2xhamL93EH+eEWVhRnOL4kX04YVRfjh/Zh6OHVlJepOv2RPJRV9fsmcA4MxsNrAauAN6T2YGZvQn4IXC+u6/P+OovwNczDt6eC3x+n0otABSlkkwYUsmEId2/17+7s6pmOzNX1DBrZQ0zV2zmyUWLADCDMf3LOGZoJccM680xQysZP6QXZYVJNR2JHOK6FPru3mxmNxACPAnc6+7zzew2YJa7TwP+GygHfhOD4VV3v8Tda8zsK4QNB8Bt6YO6kjtmxoh+pYzoV8o7jh8GQE1dI3NWbWZe9evMW13L9GWb+MOcth26hIUNTVFBguL4tyiVoKQwNicVJiktTO38W1SQIJUIB6FTydC8lEwYhakEBcnwKkwlKEyGzyqKCzh6SCUlhbqOQeRA0cVZslvrt+7gpdW1LFq3jfrGZnY0tdDQ3EpDUys7mlvY0dTC9qZW6huaqW9sob6xeWczU1OL09TaSncWsYKkcfTQSiaP6ssJ8VVZWnDgRlAkT+iKXDlotLaG8G9p9bAhaGkNr2ansaWFxmansaWVTdsamLVyMzOW1zC3egtNLWHZPHJQBeeOH8hFE4dw+MCKHI+NyMFJoS+HtB1NLcxZtYUZy2v4x5KNzFhRgzscPrCci44dwkXHDmZMVXmuiyly0FDoS15Zv3UHj85bx5/mrmHmis0AjB/ci1PG9efYYb05dlglw/qU6ECz9FgKfclba2u388i8dTwyby3zqmtpbAkXtfUtK+SYoZVMHFbJmKpyKksKqCwtoLKkgN4l4W8qqSeESn5S6EuP0NDcwqJ1W5lbXcvc6i3Mra5l8Wtb6ewRxkWpBAkzzMJVg4n4T8KMgmTbmUYFyXDmUWEqweDKEkb1K2Vkv1JG9itjZL9ShvYu0QZEDip6cpb0CEWpZGze6Q2EG7vWNzazrnYHtdub2LK9idr6pvB/fRN1jc24O+7hsnB3aHXH3WlqdZpbWmluaft/R1MLq2rqeXbJBnY0td0mI5UwhvUpYXT/Mkb3L2d0/9Lwt6qMARVFpBKmpiY5KCn0Je+UFqb2+0He1lZn/dYGVm6qY+WmelZsqmPFpjqWb6xn+rJN7TYIEC5wK4zXIRSlEhQmE/QqKeDIQRXxgrpeTBhSqdNR5Q2n0BfpgkTCGFRZzKDKYk4c06/dd62tzmtbd7B8Yx3LN9ZRs62RxpZWGptbaWgOp6c2Nreyqa6R55fVtLvgbWjvEo4cVEFZUWqXi9UKUwl6FRe03YG1pIA+ZW1/C9S8JHtBoS+yjxIJY3BlCYMrSzhpbP89dr9pWwPz17weX7UsWb+NHU0tNLX4zo1FU0vYYLR0dnCCcOC6qryIqoq2V+/SAsoKU5QUJinbeXV0kuKCJMl4w71EApJmJBJGQSLBgF5FeppbD6LQF3mD9Ssv4tTDqzj18Krddufu1De2sGV7E5vrGtlS38SW7Y1srm9i07YGNmyNr20NrFxZx/rXG2jo4u25sw3qVczwviUM71PK8L7hNTIevK4qL9LxiTyi0Bc5SJnF22QXpRjae/d3X4WwkWhobt15G4zM22LsaGqhpdVpdafV2fl/Q3Mra7fsYNXmel6tqef5ZZt4aM7qdrfOKC1MMqJvKaP6lTGyf/g7ql8Zo/uXMbCXNgiHGoW+SJ4wM4oLQlNO3314ulpjcyurt2xn5aY6Xq2pZ8XGelZuquOV9Vt5YuH6nddFAJQUJBnVv4zR/Us5fGAFJx/Wn+OG99bxhoOYztMXkS5raXXW1m5n+cY6VmwMZy+t2BT/31SHO5QXpXjLmL689bD+vHVcFWOryrQ38AbQefoist8lE8awPqUM61PKKePaH5OorW/iuaUbeWbJRp59ZSN/ezk8VmNgryImDKnkqMEVHDkoPOJzVL9SXdyWIwp9EdkvKksLuOCYwVxwzGAAXt1UzzNLNjBjeQ0L127l6cUbdj7esyiVYGxVOaWFyZ1XRRvh+oaEGX1KCxnYq5jBlcUMrAx/B8X32ljsGzXviMgbIv14z4VrwzOel2zYRmNza9tV0QAOLe5srm9kXe0O6htb2v3G4MpiPnb6WN41abhOM82ie++IyCHN3dnaEG6psa52B6u3bOd3s6uZtXIzg3oVc91pY7hi8giFf6TQF5G84+48t3QT3/7bK8xYUcOAiiI+ctpY3jN5RI9/zKZCX0Ty2vSlm/j244t5flkNw/uW8PuPnkxVRVGui5UzXQ19HRERkUPSlLH9eODaKdx3zWQ2bG3gY7+cTeNeXpHckyj0ReSQdsq4Kr75zonMXLGZ/3h4fq6Lc9DTKZsicsi7ZOIQFqx5nR88tZTxQ3rx3hNH5rpIBy3V9EUkL3z2vCM4/YgqbvnjfGYsr8l1cQ5aCn0RyQvJhPHtK97EiL6lfOyXs1mzZXuui3RQUuiLSN6oLCng7g8cz46mVq69bxbbsy7uEoW+iOSZwwZUcMe7j2P+mte56XdzaWrRGT2ZFPoiknfOHj+Qz5x7BNNeXMNZtz/Fg7NWKfwjhb6I5KWPnT6WH185icqSAj7327mcefuTPDhT4a8rckUkr7k7Tyxczx1/e4V5q2sZ3reEG844jIsnDqG0MH/OWtdtGEREMrg7f18Uwn9udS2FqQRTxvTjjCOqOPPIgYzoV5rrIu4Thb6ISAfcnenLNvG3Bet5ctF6lm2sA2BMVRlnHjGAD0wZdUhuABT6IiJdsGJjHX9ftJ4nFq7nn8tq6FNWwO8+ehLD+hxawa8bromIdMGo/mVcffJo7rvmRKZ9/GS2N7bwgR/PYNO2hlwX7YBQ6IuIREcO6sW9V53A6i3bufqnM9nW0JzrIu13Cn0RkQyTRvXlrve+mflrXue6+2bT0JxfV/Uq9EVEspx11ED+6x3H8uySjXz6wRdpbT24jn3ui/w5SVVEZD965/HD2LStgW88upB+ZYXceskEzCzXxdpnCn0RkU585LSxbKpr5O6nlzGgVzHXn3FYrou0z9S8IyKyGzeffyQXHD2I7zz+Sl6c0aPQFxHZjUTC+PS5R9DQ3Mp9z6/MdXH2mUJfRGQPDhtQzllHDuDn01eyo+nQPptHoS8i0gUfOmUMNXWN/P5fq3NdlH2i0BcR6YK3jOnLMUMrueeZZYf0KZwKfRGRLjAzPnTKaJZtrOOJhetzXZy9ptAXEemitx0zmKG9S7j7mWW5LspeU+iLiHRRQTLB1SePYsbyGl5ctSXXxdkrCn0RkW549wnDqShK8aNDtLav0BcR6YaK4gLec+IIHpm3llU19bkuTrcp9EVEuumqk0eRMOMn/1iR66J0m0JfRKSbBleWcPHEIfx65qvUbm/KdXG6RaEvIrIXPnTKaOoaW7h/xqu5Lkq3KPRFRPbChCGVnHxYP3709DL+/NJaDrbnjXdGoS8ispe++LbxVJYUcN0v/sVF332Wx19+7aAPf4W+iMheGj+kF3+98VRuf9dEtu5o5pqfzeLtdz3HU4s3HLThbwdbwSZNmuSzZs3KdTFERLqlqaWV3/+rmu88voTVW7bz5hG9uexNQznjyAEM61N6wIdvZrPdfdIeu1Poi4jsP43NrTw4axU/fnY5yzfWAXDkoArOPHIAZx01gOOG9yGZ2P+PXVToi4jk2NIN23ji5fU8vvA1Zq7YTEur07eskJsvOJLLJw3fr8PqaujrGbkiIgfI2KpyxlaV8+FTx1C7vYmnF2/giYXrGVxZnLMyKfRFRN4AlSUFXDxxCBdPHJLTcujsHRGRHkShLyLSgyj0RUR6kC6Fvpmdb2aLzGyJmd3cwfenmtm/zKzZzN6Z9d2VZvZKfF25vwouIiLdt8fQN7MkcCdwATAemGpm47M6exW4CvhVVr99gVuAE4HJwC1m1mffiy0iInujKzX9ycASd1/m7o3AA8ClmR24+wp3nwu0ZvV7HvCYu9e4+2bgMeD8/VBuERHZC10J/aHAqoz31fGzruhSv2Z2rZnNMrNZGzZs6OJPi4hId3Ul9Du6Xrirl/F2qV93v9vdJ7n7pKqqqi7+tIiIdFdXLs6qBjKvFx4GrOni71cDp2f1++Tuepg9e/ZGM1vZxd/vSH9gYw4+z/dhvxHD0LDzcxga9v7pZ09Gdqkrd9/ti7BhWAaMBgqBF4EJnXT7U+CdGe/7AsuBPvG1HOi7p2HuywuYlYvP833Y+T5+PXXY+T5++TLs/fnaY/OOuzcDNwB/AV4GHnT3+WZ2m5ldAmBmJ5hZNfAu4IdmNj/2WwN8BZgZX7fFz0REJAe6dO8dd38EeCTrsy9n/D+T0HTTUb/3AvfuQxlFRGQ/yccrcu/O0ef5Puw3Yhgadn4OQ8PeP/3sFwfd/fRFROTAyceavoiIdOZAHyk+UC/C+f63Z7z/DPBfwBxgHdACNALb48vJODIOPEw4fdSBDRmfp4ANQAMwGKgD/gQsiN3Oj921xO5ejcMcRTg99U/AZbHbI2O3bwFeB14BlgLfJpwJle7egduBrxIOmN8E3Bo/fzGOmwO/iMP8Uyzn68DyjLJvB+YCC4EVhFNrPwBMB7bFbpKxvF8FvhfHYx2wNk6T3rG7a7PGYVR8vz5Oi58Dn4vlHAb8Mf5OA7ApdvN9oLmD+TYt/v8i8AJwa3yfHucfZ3T/x/hZa5wXLwG/Acrj53/OmN4TMqbPwDjeLUB9nDYvAP8iXCn+bWA1oeLzXCz7HKAmdr8J+Fssw7Px+7mxnwbgmdjtmji9vwc8DtwT+xkUP98I7CAcEzsc2AbcGD97J2GZGAEcQVgeWwgnTPwqjtN9HSybf4rvvxO7uSS+vz72vzKOy0vx+28Ad8V5uDYOuzJjfjTH7hfE6dQM1MbfmgPMj+9XAInY378R1q+G+Htb49858TeXx///ljGMyox1NXOePxqHMTf2swZ4E7A4YxjrgIuBSzLmaXMc7hzCerUceH8c3uT4/uQ4Hx6I3ayJ4zIpdrcuDucV4npCWH6uJqxfm2IZ5sXuPXb7IvApwvJzB2FdqAeejsPx+FsLgRmx/3sypvmC+LvHZMzXl4EfEG5psyGOV/o1/g07e+cg1gD8m5n1z/hsu7sfR5hwjwNfcPcSdy+J3080s8OzfqcOqDSzI+L7cwgze6u7ryWE82PA2wiBV2FmhYSV49fAl939OHdfkfGbUwlBcYWZWSzP6+4+jrDilwNfyxqXqwkbgbcTFup02QYQFoo6wsYjfY3EOcCW9A+Y2USgiHCLjOsJF8Y1EhaYlbQdtP844UyqJelpFst3OyHEro+fX0JYiK/IKKfHcpxOCPqJ8fPf07ZCjQX+QAjjbeyqFTjRzKYQVpgxQEHWMC7LmK/HElaAHYSN0h1xvH5BWOkHAu8jTO/PE0KZWIbFwPfdvRQ4khDUY4GPEDYUq4BTCedGL4jLzjRCMC5297NjOScSTkU+FngtTtMqQiXj2VjGBkLQTIjz/CHCsnNhnNZfiGWFsHwsJgTx+e7+KiHAv0VYho8Cfhan1dFmll5+z8kYPwjLQwPwdQB3v5MQYjMyxuWXwBnA/bGfSsL8vyy+b4rjNIWwjL9G2Fg8Q9v6dDJhXlYB34rj9xVgobsXuXsxcApwTuy+GfhsXC/Opi3038+uhgFHAW+O0/dswvL/KHF9A0oJ6/OHCevmo3Ge/idh+b3e3ccSKhm3xnlyF6F9fDVho/pk/G4jYR0poS2YJ8ZXFW2tHz8jhPtiwnJ9RpwffyYse+fE6XULYeP9OFBMqLCdRVi25gKnEeb3WNquWaojnFP/X7Gc58RxHUFYhgF+Hadf+rWgg2m3Vw7l0G8mzNQbu9HP43R8JtELwJfi/1OBzbStXEa4yAxC+DwK7O5uoUnCSnINITDPJKyYWwDcvSWW+YOEhTk9jNeB59x9e9bvLSGsFNBWk0+X8/mM7j5DCMNhwI8ItdmvAJ+Nwys0swmE029v6qTs04GhZlZOqAmtZtfQvxv4JKH2Ukm4fmNH7P7hS1uKAAAK50lEQVQz7l4dh3cl8PcOhtFKqP19E7gPWESo5aY1EzY2N5rZmPjb9fG7GwkrxRLCStgQf+t0wvS+mBBu/eK02Dl8d1/p7p+I5b+Qtj2RDwNlhI0xQG/CCrs13hxwOGFlnmFmY+P/nyNsjF8ETvJwT6qfZ0yji+N49CUsS7j7HHd/hjCvBxA2NIvdfWnsbzBtyxlxukBY3i6M/0+N40ecR+OA3wHjMiotrwNnmtlZwOWEYBxCCMIRhHX+S/G30hZmDKOStg1E2jsIe1w/Aj5EmPbNhL3c9PRNj187cZoRp09H62oFUOfuDfF3NhI2lv2At7l7Q1xnPkpYr0oz+m0mLKPpW7vcHcfxaUIN+nbCxswJFZqbgXPd/UnC/G4BHgQudPd6QsCnn2M4gbDx20qYZoXx703AFe6+nrA3/P8Ie0CXEvZqp8f+64ER7v5aLPMzwICMG04mCXs3a4EvxnFZ7OEeZQfUoRz6EO7++V4zq+zgu7cQgmOOmaVX/muAt5hZ9hOJ7wAuMrNiwhZ/LKHGBKEm9GPCilBF2Gh8mlBTeDdwm5k9lPFbg4A/u/tiwoJ2HmHh2cndXyesMIcRFuSC+PfyDsblJcKuLrG79Ip7LOGiubQJhAXpj8Db3X0hMItwId3a2M1cwsL7BHBb1nCMUEOZRtjbeIoQnDVm9uaM7u4k1KxPIgTTAGA2cHT8mz1+yTgP5pjZHMIex58J8+cRQq35mMzJQ2iCuSq+5mRMt7WEGvEXCDV5CNM4PbxUfF8Rf6Mj/yBseF4m1MYvIuz6N5vZiDg+i4B/EgKjhrBxeYnQ5DKdsAKXEkIj3d9bCHs7awk1wDWE6d2YNfziOIwLgGFmNiB+/i3CfCk2sxuBXvHzBwh7jMWEef7P+PnbCU0Gm+O4/0/8vIUQJr8gbHgvItQanbD3VhvLf0TGsOcQNu5FsXzpYaSlNzZ3xW6mEALylMx5mxHw2f02ESofIzvoZilho7XDzDaa2SuEDeKWuBwBuyxTmdOyihDyxHF9nLB8LCEsk0cQ1pvvEQI/vV5PICwHmdM3/RzDcsLyPT1OiyLgOKDV440lzWyiuy+LZfhTHFZ6Dx1CiKeXjZMIy3xJnHYJwjx6J2EjMjmWJXMv7t1Z07aE/eSQDv24IPwc+EQHXz8PfCvuGp0Ru19NWOl/kvU79xNmyI2EXboFhAUVwowcQ1joiwih+GL8Pt28c1nGzw0hLEjEv8fT8b2KLH6ebmaZ0sm4vEa4mjlFaEYZFIfxSFZ3RljQniNs3DKHAaHmscPdR8Rd8PR1FiXAdYSNQF9CU9ZUQlNKehzStcIEYQUbTAjW9MZkd+PXkrmbGss4JPZ7GWF6D8665fYvCGHwQdo2mEVxo3F1fP/h+PethHkxlTBvz9qlIGZ3mtmLZjaTEABJYFhcfpzQ/PUPwso5gBBo0+P7NxE2dNcS9mouIRwn2UBoikj3dxIhgBfH7gYT5kVH0+X5OG6/J1zQiLv/hLBH10zYc3kofj6X0BY/lfbzfCpt4XwncLKZjY7vy4GN7v4PQpina+4XA7Xu3po5bMIGalT8vl2TnJkNJATts7EiQ5xGAM9kNUEsZVdXEGrAtYRmpTuyvm8k1HTPj+NRQdi760h6mTrFzOYS9m4XZAT5VMJyvpaw4U4vt/WEDcblWb/V0fRtIeztpUN/OmG9P4G2UL+fsKFIEZanh+lY5rIxkxDsJxGWv8doOw64iFBZy5TdvJPdArDXDunQj+4ghFxZF7tPt51nd/888O/x88czv/BwFfE0wu73TMLMLGDX6deLUEu5x8xWEGo3R9O+JouZ9SI0GywlhPp2Qk1vVifjMp+w4N0fhz2eXXfB5xMW5MuBE8zsC8CbCRuwnaPCrtJt+l8j7AV8ltAk9Z+EXeDPEvZojFDTOY6wN3R0/P0NhKad+YQNXPb4deSthGC6hVArKyI0IaQ9TAjO9MYOoCFjw9EQm1SIZR0A3EOoCZ9B2CXfuXfi7tcTNgZVcThGOB60JpZjMiGgT4rDXEVYHqbEz54l1Pgr4zS8Kw7zvIz+jiG00/5fHM54wrzaycyOjcNOH1f4EBnNLO6+hhCQlxLCP33DwmmEmnx6nhfE8b6asAH6dCzXTYTASgAD4x5aibv/Kw57FDAmLptXZAx7YxzGF8k4ThS9O06T5fGq+wSh6SX72Ngu4jDHASVxmKMI7dfZy7e7+5Pufguh+TEF9Daziozfylymnont/98HppjZcWbWjzCPLyKE8yVx/F6J0+sC4Doze2/8yfm0rb/p6fsM8SA3YX6+RFgOigjLSGboX07Y420mrGPzCWGeKXPZKCY0o02J3f0jY7jpA79viEM+9GMgP0hb7XZP3S8jNEOcmvXVfxAOsBxP+xpa0szS7YgJwgo7nbDVvijrN04Dqt19pLuPcvfhhBldSVgZ0w+luZ1wn6J0W7UTzob4PuGAU/a4zAQa3X0eoY13cfw/0/8QQntALNdVhLM2bu90YrS3g7CX8TlCTfutcTjDCWdB7Lzi2t1fJuzSvoPQxFRKqHV+08yGxGHeR+fPTphCWBH+lxDQ99M+/LYTNja/ZffzNUXYOzoa+JS7DyLU8pywkp2R0W0pbQe6bya0ha8nHEw7l7BMXERoymmNy1XvOB1ei+W7ldBufC9hGaiK438RYS9plbs/G4ffi4ymCDM7gXDGUKO7jyTs7awiNLOMjE+nK4jdDiIEbdq9hFuYpOf54DjenwF+HufR/FiOwYSD9U/G/tIbinT55wDXuPsQQoimCBu1ewlNZw1Z03gqYT6eQAi37xKasgrIuBFjvBXLaR30eytQH9eHwYTl7NqMbvoRKkppx8Uy1AD/Z2aFcZ25i7CM1Wd0u4nQJHYToamkltA+P5yw115AqOEboTnsfODrZvZxwsYgaWZnx3H/BuEkhmmEdajG3VvicpAgVGxaAOIeTW2cXo/EprPvASkzOzGjfJWE5a2ecAzrDto2ytPjcG9j1w3tAXXIh350O+0XHGjfpj8n67sPkrVVdve/E3Zzn6D97mWCUAN/lNDMc4+H2040seutJ84CBplZdfpFCJO1wBAzayIs0FPjb2YOfyah5nY2IUzSUoRwSjc3bSAcOGrH3efE336YsNFKP9BmZHa3nXH3F2J/2QeTfkeokWe6jrZjDJcR2k7TG4h/I9TokoQVK3N6JAgr1Gra5ttKQs24PKMsDxA2QNnzNVMB8JC7V7v7t+NnTxMC9e2EM3Y+amZ1tJ0+2UQ4IHk/YY/lPkLojYrDynygwzxCReAOQm36qljOW+P3JYQVdyRtZ4Wk+3uVsHF7ihD+txL2MJvj+NUQQqiQsOKfS6hZlhBO2/06YHGaPQ981szSzTFDic0/GX5L2BNNAD8khPREQtvwKYSa/UOEPYMvxXWihND8tjQegP9J1m+WECpB9xAOmP+VEKbpZtWjzKzBzLbH74/J6j89zEwP0H75LiQ0lSyITTbjCSH4NsJ83EYIzbMJwZ9tOqECdxPhbKLH4ud3EdaH/ybMi3NiGZviZ1WE9fxLhD376wiVqx/HMmWeJNFEqJyUxDyZTzgTq4SwTBAP2O4gVL4ej+PxKUJGjAa+G5vw5sXuN2Ytt9my2/RP6qS7btMVuQe5eJD4Rx7ufyQisk/ypaafl8xsHqHm/ddcl0VE8oNq+iIiPYhq+iIiPYhCX0SkB1Hoi4j0IAp9EZEeRKEvItKDKPRFRHqQ/w8kF/smjw95IgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.yscale(\"logit\")\n",
+    "plt.plot(df[\"addr_state\"], df[\"mean_bad_loan\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
+     ]
+    }
+   ],
+   "source": [
+    "te_addr_state = mean_target_encoding(train, \"addr_state\", prior_count = 30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grouped_data = te_addr_state[[\"addr_state\", \"te_addr_state\"]].group_by([\"addr_state\"])\n",
+    "df = grouped_data.count(na = \"ignore\").mean(na = \"ignore\").get_frame().as_data_frame()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.sort_values(by = \"mean_te_addr_state\", ascending = 0)\n",
+    "df = df.reset_index(drop = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7febdfbc6e80>]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(df[\"addr_state\"], df[\"mean_te_addr_state\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train = train.merge(te_addr_state, all_x = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_trans.remove(\"addr_state\")\n",
+    "x_trans.append(\"te_addr_state\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "train[\"te_addr_state\"].hist()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Separating Typical from Extreme Debt to Income Ratio\n",
+    "\n",
+    "Looking at the occurance of bad loans on a logit scale reveal that the chance of a bad loan roughly increases linearly in debt-to-income from 5% to 30% and is highly volatile outside of that range due to small numbers of observations. To reflect this finding in the modeling, we will replace the original `dti` measure with two derived measures:\n",
+    "\n",
+    "\\begin{eqnarray}\n",
+    "dti\\_core & = & \\max(5, \\min(dti, 30)) \\\\\n",
+    "dti\\_diff & = & dti - dti\\_core\n",
+    "\\end{eqnarray}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th style=\"text-align: right;\">  Probs</th><th style=\"text-align: right;\">  dtiQuantiles</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td style=\"text-align: right;\">   0   </td><td style=\"text-align: right;\">          0   </td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.05</td><td style=\"text-align: right;\">          3.79</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.25</td><td style=\"text-align: right;\">         10.23</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.5 </td><td style=\"text-align: right;\">         15.62</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.75</td><td style=\"text-align: right;\">         21.26</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.95</td><td style=\"text-align: right;\">         29.02</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   1   </td><td style=\"text-align: right;\">         39.99</td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train[\"dti\"].quantile([0, 0.05, 0.25, 0.5, 0.75, 0.95, 1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "breaks = np.linspace(0, 40, 41).tolist()\n",
+    "train[\"dti_cat\"] = train[\"dti\"].cut(breaks = breaks)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = mean_target(train, \"dti_cat\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7febdfaebf98>]"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Hq+ZJF0wTjTCartU0e+mtccW+uhY8viDTR2VRlO0YUHknp0t5J90WGmub1XRNkr4QceQLBLln2QZeLa3gqoUTeODiWaSZMINDdAiP9PtaoLXNuIk7rSibwpx0qtz9j/Q9vgBt/mCnvjtg/ubokvSFiJMWr5/bn1vN+9sOcecZU/nemVOlnJMAoum0ud2Yrjm1KJuiHAdl1Yf7fd/2Zmtdb+SaXNOXpC9EHNQ2e7n56ZWsr6jnF5fMlpWzCSQ/ipr+tqomxuU7yUpPoygnnUPutn5n+7jbm631UtOX8o4QycHrD7K9ys2GygY2Gh9bjPLAI9ccy+LZo0yOUESKpv/O9oNuphdlA1CU48Af1NS2eNvvB/Sksb2tci9TNk3aHD3qpK+UWgz8N2AFntRa/6rL63cBXwf8wCHgZq11ufHaDcC9xqG/0Fo/MwSxC5EwyqrdPPXxbjZUNrDtoBtfINQ3IduRxuwxudywqJiLjhnLnHG5JkcqehJqxdBz0vf6g+w81MSXZ4Q2pCnMDiX6qkZP30m/hw6bAA77MBjpK6WswMPAWUAFsFIptVxrvTnisDXAAq11i1LqNuA3wBVKqQLgp8ACQAOlxrn9L4ETYhhYtrqCn7y+EatFMXd8HrecPInZY3OYMzaXCQUZUrcfBgoy7b2Wd/bUNOMP6vaRfmGOA4DqxjZmjen9Pd3tHTaH543chUCZ1noXgFLqReBioD3pa63fjzj+c+Ba4/E5wDta61rj3HeAxcALgwtdCHO1egP8dPlGXl5VwfETC/h/V82jyEgIYnhxZfXefydy5g7Q/nfc37TN8AYqXcs7NqsFq0Ul/I3cscC+iM8rgOP7OP4W4B99nDs22gCFSERl1U3c/txqtle7ueP0KXzvzKky9XIYK8hMp7S85+LD9io3Voti0shMAEZmhcs7fU/b7K28A8aWid7EbsPQ0++nPTZ7VUpdS6iUc+pAzlVKXQhcOGXKlChDEsIcf1tbyT3LNuCwWXn6poWcOm2k2SGJQXJl2qlt7rn/zraDbkpcGe0L6OxpFlyZdqrcfY/03R4/Vosiw9594Z2Zu2dFOzSpAMZHfD4O2N/1IKXUmcBPgIu01m0DOVdr/YbW+tbcXLnRJRKTxxfgnmXrufPFtcwek8tb3/2SJPwk4crq6L/T1fYqN9NHZXd6rjDHQXV/I31PqNlaT/d0nHZLwtf0VwJTlVITgUrgSuDqyAOUUvOAx4DFWuvqiJfeBn6plArv4XY2cM+gohYiRoJBzaGmNirrW9nf/uGhsr6Vzfsbqaxv5bbTJvODs6ZJOSeJdKzKbevUdK3VG6C8toVL5nWuSBflpFPdz0g/1IKh59bJTps1sZO+1tqvlLqDUAK3Aku01puUUvcDq7TWy4H/ArKAV4yfbHu11hdprWuVUg8Q+sEBcH/4pq4QicLjC/CT1zeyfF1l+3TLsKz0NMbmOZk+KpsHL53NadMLTYpSxIor0+i/0+RlSsRfb1l1E1rTPnMnrDA7nS0HGvt8z0aPv9tN3DCnieWdqOfpa63fAt7q8tx9EY/P7OPcJcCSIwlQiFg70NDKrX8uZeP+Bq5aOIEZo3MYm+dgTJ6TMXlOUza6EPFV0EsrhvBuWdO6lHeKchwccrcRCOpe9z1we3xkp/f8b8dhsyb2PH0hktXqvXV889lSWtr8PH7dAs6aWWR2SMIEI7J6Tvrbq9zY0ywUF2R0er4wx0FQh1o3FPYyTbex1U/JiIweX3PYrNS39L8ZeyxIUVKkrFdLK7jysc9x2qy8fvtJkvBTWLj/Tm1Tl5H+QTdTRmZ1u39TlN3/tM1GT981/USfvSNE0vAHgvzizc388JV1HFucz99uP6l94Y1ITTarhRxHGjXNnZN4TzN3ILoFWm6Pv9tq3DCnfRjU9BNdVaOHN9bt59w5oxmb5+z/BJGSGlp9fOeFNXy0/RA3LCrm3gtmYpNZOILQZiqR5Z2GVh8HGjw9DgjCSb+6l776/kCQprbeb+Q6bFbZI3ewDrnb+MXftzA2zylJX3SjteaD7Yd44I3N7Ktr4f9+ZQ5XLZxgdlgigRRk2juVd3YYN3Gnj8rqduyILDtK9T7Sb2oL99LvY8qm3MgdnGJX6IbJnpoWkyMRiSQY1LyzpYr/ea+MDZUNjM1z8tzXT2DhxAKzQxMJpiDTTnlE/mifudPDSD/NasGV2ftc/Y5ma71M2bRbpLwzWNkOG65MO3tre26aJFJLIKh5a8MBHn6/jK0H3UwoyODXl83h0nnjsKdJOUd058qys3pvR/+d7QfdZNqtvVYOinLSe72R2xDuu+PsfaTvD2p8gWDcy4tJk/QhNNrfc1hG+qnMHwjyt7X7efiDMnYdambyyEx+f8UxXHj0GFlBK/rkykynrsXX3n9nW5WbaaOye22NXZTT+wbp7R02eynvhPv4tPoCkvQHo9iVyYrdstg3VW2sbOB7L62lrLqJo0Zl8/DV81k8e1Svi2eEiFSQaScQ1DS0+sjLsLHtoJtzZvW+y1lRTjrrKxp6fK2/8k446Xu8gbgv/kuqpD+hIIO/rq2kzR8gPa17ZzuRnAJBzaMf7uT372zHlWXn0WuP5eyZRX3uXypEV672BVpt+IOauhZfn1N5C7MdoWMDwW6/RYbbKuf2Ud4BczZHT6qkXzIiA61hX20rUwq733EXyWdfbQt3vbyWlXvqOH/OaB68dDZ5Gfb+TxSii8j+O+FafU9z9MOKchxoDYebvIzK7bwqt31/3D7m6QOmTNtMqqQ/oSC0ycHe2mZJ+klOa83rayq572+bAPjd147h0nljZWtCccQ6Om16OdAQqtX3PdLv2Cu3W9I3RvpZvc3ekZH+0CgJT9uUm7lJrb7Fy09e38jfNxxgYUkBD33tGMYX9NzjRIhohcs7h5u9bK9y48q0MzK7943P+1qV6/b4yUpP6/V+UvuNXBPm6idV0i/ItJOVnsbeWkn6ycjt8fHm+gP84X+3U9Pk5UeLp/PNUybLjVoxJPIzOvrvbKty99uaoyjHGOn3sCo31Hen9/TaUd6RpD8oSikmFGRQXiNz9ZOF1ppV5XW8tHIff19/gFZfgBmjc3jqhuOYPVZ2WRNDx57W0X9n+0E3ly8Y3+fxrqx0LAqqexjpN7b6ep2jD1LeGVIlIzLYesBtdhiiD1qHpsVl2NN6XShV7fawbHUlL6/ax65DzWTarVwybwyXLxjPvPF5UrsXMeHKSmdDZQPN3kC/I32rRTEyO73X8k5v0zUBHLbQv3sp7wyBCQWZvLO5qs/NDYR5Wr0Bbly6gi+M9RT2NAtZ6WlkplvJtKeRlZ6GxaIoLa8jENQcV5LPbadO5vyjR5NhT7p/riLBFGTaWbuvHui5505XRTmOHpuuNXp8jOqlzz7ISH9Ilbgy8AU0++tb5eZegvEFgtz+/GpW7Knl9tMn40iz0uT109zmp7ktgNsTetzqC/D1L03kawvGM3mkzMIS8eMyFmgBTI2i3XZhtoPK+tZuzzd6+p7j75Ca/tCZYMzg2VvbIkk/gWitufu1Dby3tZpfXDKba08oNjskIboJz+AZk+uIaqVsYU46ayL69YT1V94Jj/TNSPpJ14yk2BWaq18u3TYTyq/+sZXXVlfw/TOnScIXCSs8V7/rnri9Kcp2UNPsxevvWGSltQ7dyO3jh4bNaiHNokwp7yRd0h+d48CeZpEZPAnk8Y928thHu7h+UTHfPWOK2eEI0avwqtzpUe6kFp62eaipo67f7A0Q1PS6gUqY02al1Rv/FblJl/QtFsX4fKeM9BPEa6UV/PKtrZw/ZzQ/vXCWzLoRCS1c3ol2+8yeFmiFV+P2tlVimMOkLROTLulDqMSzR0b6pntvaxU/em09J01x8bsrjpHZVCLhzRidQ67TxoKS/KiOLzRG+pFz9d399N0Jc9gsciN3qBS7Mvh8Vw1aaxlZDkK4r/iRKC2v5dvPrWbm6Bweu26BdD0Vw8K0omzW/fTsqI/vaa/c9l76UZV3JOkPieKCDFq8AQ43efvsnZHqWr0B9ta2sK+2hYq6FvbVtRqPW9lX10Jzm5+R2ekU5TgoynEwKsfBqFyH8Xk6Wod2CKpv9dHY6qO+xUt9i4+GVh+f7aphdK6TpTcdR1Z6Uv4zE4KCDDtpFnVE5R2nzYrHL0l/SBSPCM/gaZak34tdh5q49JFP27d1g9Cvm+PzMxiX72RBST7ZjjSqG9uocrext6aFFbtrOx3flcNmIc9pD/16XJzP/RfPZkSWXH+RvCztq3I7Rvod5Z2+06tDRvpDp9iYn19e08KCEtkAuycP/n0LgaDmv6+cy4SCDMYXZODKtPdbDmv1Bqhq9FDV6MFiUeQ6beQ5beQ4be2dA4VIJYVdtk3sKO/0M9K3W6lr9sY0tp4kZdIfl5+BRSHTNnvxwbZq3t1azT3nHsXFc8cO6Fyn3UrJiExKjN+mhEh1RdnpnWYLdpR3+q/p75fZO0PDnmZhTJ6Tcmmx3I0vEOSBNzdT4srgxpNKzA5HiGGvKMdBlbvz7J30NEu/kxecNpmyOaSKXRkyV78Hf/6snJ2Hmrn3/Jkyo0aIIVCUk059i699+mWjp++2ymHpsjhraBW7MqW800VNUxt/+N/tnDJtJGfMKDQ7HCGSQqExbfOQMW2zsbXvvjthTptVeu8MpeKCDOpafO03VQQ89M52WrwB7rtghqxfEGKIhPfKrTZKPKFds/of6Tvt5izOSt6kH+62KSUeADbtb+CFFXu5flExUwqjW2IuhOhfRysGY6Tv8UdV3nHarPiDGl8gviWeJE76odkl0o4h1PXv/jc2k+e08b0zppkdjhBJpWv/HXdr3/vjhjlM2kglaZP+hIi5+qnuHxsP8sXuWn5w9nRyM/ofgQghopefYcNmVREjfV+/q3EhYnP0OC/QStqkn5mexois9JS/mevxBXjw71s4alQ2Vy2cYHY4QiQdpRSF2Y72pmuh8k50N3JBRvpDqkSmbfLER7uorG/lvgtnSpdLIWKkKCedKrcHjy+A1x+M6kaulHdiYIIrg71RLtBq9Pja98ZMFgcaWnnkg52cO3sUJ04eYXY4QiStohwH1Y1tHS0YopyyCeDxxfdGblK2YQgrcWWybHUlHl+gz74wTW1+TvnN+1x7fDE/PGd6HCMcGg2tPvYZ3TL31rawr66FfbWtbD3YSEBr/vO8GWaHKERSK8xO55Oywx3N1qKYvdM+0o9zTT+pk3542ua+2pY+d7b/58aD1Lf4+PNne/j26ZPJsA/sstz/xmbcHh8PXDI7Lk3HtNas2VfPSyv28fbmUOyRcp02xhc4mT8hn0vmjZUN4oWIscIcB40ef/sMnujm6ZuzOXpSJ/3wDJ49NX0n/WWrK8hOT6PR42fZ6soBbdy9o8rNkk92A1BR18rj1x8b1Z37I1HX7GXZmkpeWrmX7VVNZNitLJ41iqNGZzM+P9Qpc3xBBrlRjDKEEEMnPG1zZ3UT0H+zNTDvRm5SJ/0SV0df/d7sr2/ls1013HnGVN7dUs3ST3Zz9cIJUe8Y9dhHu3DYLNxz7gweeHMzVz7+OU/ftHDI+vgHg5rPdtXw4sp9vL3xIN5AkLnj8/jVV+ZwwTFjZIMSIRJAeIP0HUbSj3ZxFkh5Z0jlZdjIdqT1eTP3r2sr0Rq+Mm8cEwoyuOvldfy77DCnThvZ7/vvr2/lr2tCvxnccGIJE1wZ3PaXUi5/9FOeveX4QZdVqt0ebliyki0HGsl12rj6+Alccdx4ZozOGdT7CiGGVnikXxZO+tHM3rGH5tHI7J0hpJSixJXJnl6mbWqtWba6kuNK8pngyuD8o0czIiudpUa5pj9P/jt03Ne/NBGA06cX8tzXT6Cuxcdlf/qUrQcbjzj2w01tXP3EF5TXNPPQ5cfwxX+ewc8umiUJX4gEVJQdSvo7BlDecdjMqeknddIHY9pmL+WdDZUNlFU38ZX54wBIT7Ny7QkT+GDbIXYeaurzfeuavbywYi8XHTOGcfkdI/pji/N55VuLsCjF1x79jJV7agccc01TG9c88QUVdS0sufE4Ljt2nOxKJUQCy3GmYU+zcMjdhtWiyLD3///VKUk/NooLMqioa8XfQ1OjZasrsadZOG/O6Pbnrjm+GLvVwjPr/DHhAAAP60lEQVSf7unzfZ/5bA+tvgDfPHVyt9emFWXz6m2LGJGVzrVPfsG7W6qijreu2cs1T37BnppmltxwHCdMckV9rhDCHEqp9rp+jiMtqi62NquFNIuS8s5QK3Fl4g9q9td7Oj3v9QdZvm4/Z80s6jTbZWR2OhceM4ZXSyt63QS8xevnmU/3cOaMQqaP6nlW0Lj8DF751iKmj8rm1mdL+dnyTRxoaO0z1oYWH9c+9QW7DjfzxPULOHGKLKgSYrgIl3gGMnvPacJGKkmf9CcYc/XLazuXeD7cfojaZi+Xze++R+xNJ5XQ4g3wyqp9Pb7nSyv3Udfi47bTuo/yI7my0nn+Gydw+bHj+Mvn5Zz6mw+4968bqKjrfo+hodXHdUu+YEdVE49ddyynRHEjWQiROMI3c6PpuxPmsMd/y8SkT/rhBVpdb+a+vqaCEVl2vjS1e3KdPTaXhSUFPP3pnm6tGXyBIE98tIuFJQUcW1zQ79fPSk/jV5cdzfs/PI2vLhjHSyv3cdp/fcDdr61v7/Xv9vi4YckKthxo5E/Xzuf06bKrlRDDTWF7eWdgI32p6Q+xomwH6WmWTjdzG1p8/O/mai48Zgw2a8+X4OaTS6ioa+WdzZ3r8cvX7md/g4dvnTZpQHGML8jgl5fO4cP/OJ1rjp/AsjWVnP7QB/zg5XXcuHQlGysbePjq+Zwxo2jg36QQwnTtI/0Bl3ck6Q8pi0UxoSCj00j/zQ378QaCXGbM2unJWTNHMTbP2Wn6ZjCoefTDnUwvyj7i0fiYPCc/v3g2H//odG48sYS/b9jP2n31/PGqeZw9a9QRvacQwnzhG7nRTNcMc9gssiI3FopdmZ22TVy2upJpRVnMGtP7nHerRXHDicX88q2tbNrfwKwxuby3tZod1U384Yq5g95jtjDHwf+5YCa3nTaZ2mYv0/poEyGESHyF2eGafvQjfYeUd2Kj2JVBeW0zWmv2HG6mtLyOr8wf12/ivmLBBJw2K09/sgetNY98UMa4fCcXHD26z/MGYkRWuiR8IZJA0ZHU9O2S9GOi2JWBxxek2t3GsjWVKAWXzO0+a6er3Awblx07lr+t28/bmw6yem893/jSJNJ6uQ8ghEhdY/KcZDvSKBkRffsVp01m78RE+ybph5t5fU0FJ08ZwahcR1Tn3njiRLz+IHe+uJaCTDtfWzA+lqEKIYapDHsaK/7zTC46ZkzU50jSj5Fio/HZa6sr2FfbyqXz+h/lh00pzOLUaSNp8we56cSS9h7YQgjRldNuHdD9Poc9/ouzUuJG7th8J1aL4rXVlWTYrZwzwFkyd545lTZ/gOsXlcQmQCFESpJ5+jFis1oYm+ckENQsnj2KzAH2oJ8/IZ8Xb11EboZsTiKEGDpmTNlMiaQPHStz+5qbL4QQ8eS0WQkENb4eGkLGSkqUdwDmjs9jf32rdK0UQiQMR8SWib11BxhqKZP07zprGt89YyrWKLdBFEKIWGvfHN0bGND8/sFImaSvlMJmlYQvhEgcZmyOnjI1fSGESDSS9IUQIoU4jPJOPDttStIXQgiTONJkpC+EECkjfCO3zRe/KZuS9IUQwiRS0xdCiBTSnvSlpi+EEMnPYQ+lYBnpCyFECgiP9OPZdE2SvhBCmMQh5R0hhEgdNquFNIvC45ekL4QQKcFpi+9GKpL0hRDCRA57fLdMjCrpK6UWK6W2KaXKlFJ39/D6KUqp1Uopv1Lqq11eu0EptcP4uGGoAhdCiGQQ792z+k36Sikr8DBwLjATuEopNbPLYXuBG4Hnu5xbAPwUOB5YCPxUKZU/+LCFECI5hMo7CZT0CSXrMq31Lq21F3gRuDjyAK31Hq31eqBrYeoc4B2tda3Wug54B1g8BHELIURSSMTyzlhgX8TnFcZz0YjqXKXUrUqpVUqpVYcOHYryrYUQYvhzxnmf3GiSfk87j+go3z+qc7XWj2utF2itF4wcOTLKtxZCiOHPYbPSlmBJvwIYH/H5OGB/lO8/mHOFECLpOW2JV95ZCUxVSk1UStmBK4HlUb7/28DZSql84wbu2cZzQgghSMCkr7X2A3cQStZbgJe11puUUvcrpS4CUEodp5SqAC4HHlNKbTLOrQUeIPSDYyVwv/GcEEIIjBu5cVycFdXG6Frrt4C3ujx3X8TjlYRKNz2duwRYMogYhRAiaSXcPH0hhBCxEy7vaB3t/JjBkaQvhBAmctqtBIIaXyDBkn4UrRjSlVIvGa9/oZQqMZ63K6WWKqU2KKXWKaVOG7LohRBimEtPC6XheHXajLb3TjStGG4B6rTWU4DfA782nv8GgNZ6DnAW8JBSSn7DEEIIOjZH98SpFUO0ybffVgzG588Yj18FzlBKKUI/JN4F0FpXA/XAgsEGLoQQySDem6NHm/SjaafQfowxzbMBcAHrgIuVUmlKqYnAsXResCWEECkr3kk/qimbRNdOobdjlgAzgFVAOfAp4O/2BZS6ELhwypQpUYYkhBDDn8Me3y0Tox3pR9NOof0YpVQakAvUaq39Wuvva63naq0vBvKAHV2/gNb6Da31rbm5uQP9HoQQYthK1PJONK0YlgPhTVK+CryntdZKqQylVCaAUuoswK+13jwEsQshxLAXTvrxWqAV7Ypcv1Iq3IrBCiwJt2IAVmmtlwNPAc8qpcqAWkI/GAAKgbeVUkGgErhuqL8JIYQYrhztST8+rRiirelH04rBQ6j3Ttfz9gDTjzxEIYRIXu3lnQSr6QshhIgBhz2UhhOtpi+EECIG4l3Tl6QvhBAmckh5RwghUofNasFmVYlZ3hlE0zWbUuoZo+naFqXUPUMTvhBCDH8OmzVus3cG0mVzME3XLgfSjaZrxwLfDP9AEEKIVOeI45aJAxnpD6bpmgYyjZW6TsALNA4qciGESBLx3D1rIEl/ME3XXgWagQPAXuC3sleuEEKEOG3WhLyRO5imawuBADAGmAj8QCk1qdOJSl2olHq8oaFhACEJIcTw57AnZnnniJuuAVcD/9Ra+4ye+p/Qpae+NFwTQqQqp82SkEn/iJuuESrpfFmFZAInAFsHF7oQQiSHhKzpGzX6cNO1LcDL4aZrSqmLjMOeAlxG07W7gPC0zoeBLGAjoR8eS7XW64foexBCiGHNaY9f0o+64RoMqulaU0/PCyGEAEdaYtb0hRBCxIDDbqXVm2CLs4QQQsRGQtb0hRBCxIbTWJEbmvcSW5L0hRDCZE67lUBQ4wtI0hdCiKTniOPm6JL0hRDCZOGNVNoSKekPoq3yNUqptREfQaXU3KH7FoQQYnhz2OK3ZWJUSX8wbZW11s9predqrecC1wF7tNZrh+obEEKI4c6ZgOWdwbRVjnQV8MKRBiuEEMnIYY/flonRJv3BtFWOdAWS9IUQopNEHOkPpq1y6EWljgdatNYbe/wC0lpZCJGiwkk/Hgu0ok36g2mrHHYlfYzypbWyECJVOdvLO7FvxRBt0h9MW2WUUhZCDddeHHzIQgiRXOI50o+qy6bW2q+UCrdVtgJLwm2VgVVa6+WE2io/a7RVriX0gyHsFKBCa71raMMXQojhLz2OUzajbq18pG2Vjdc+ILRxihBCiC4SsaYvhBAiRtrbMCTQlE0hhBAxYrNasFlVQk3ZFEIIEUMOW3x2z5KkL4QQCSBeG6lI0hdCiAQQ2hw99vP0B7QxuhBCiNj48lGFjMvPiPnXkaQvhBAJ4KcXzorL15HyjhBCpJCYb6JivHa0UuozpdQmpdQGpZRjaMIXQggxEDHfRMVovvYX4Fta61nAaYBvSKIXQggxIPHYROVsYL3Weh2A1rpGax37eUlCCCG6iccmKtMArZR6Wym1Win1o8GFLIQQ4khFO3tnMJuopAEnA8cBLcC7SqlSrfW7nU5W6kLgwilTpkQZkhBCiIGKxyYqFcCHWuvDWusWQp0653f9ArKJihBCxF48NlF5GzhaKZVh/DA4Fdg8+NCFEEIMlDI2t+r/QKXOA/5AxyYqD0ZuomJMw3wWmIexiUp40xSl1LXAPYTKPW9prXut6yulDgHlg/ieRgCHB3F+rEhcAyNxDYzENTDJGFex1npkfwdFnfSHC6XUKq31ArPj6EriGhiJa2AkroFJ5bhkRa4QQqQQSfpCCJFCkjHpP252AL2QuAZG4hoYiWtgUjaupKvpCyGE6F0yjvSFEEL0ImmSfn9dQM2ilNpjdBZdq5RaZWIcS5RS1UqpjRHPFSil3lFK7TD+zE+QuH6mlKo0rtlaY7pwvOMar5R6Xym1xegOe6fxvKnXrI+4TL1mSimHUmqFUmqdEdfPjecnGl13dxhdeO0JEtfTSqndEddrbjzjiojPqpRao5R60/g89tdLaz3sPwitHdgJTALswDpgptlxGbHtAUYkQBynEFoJvTHiud8AdxuP7wZ+nSBx/Qz4ocnXazQw33icDWwn1GHW1GvWR1ymXjNCbViyjMc24AvgBOBlQmt2AB4FbkuQuJ4GvmrmvzEjpruA54E3jc9jfr2SZaQfTRfQlKa1/ojQorlIkZ1RnwEuiWtQ9BqX6bTWB7TWq43HbmALoaaCpl6zPuIylQ5pMj61GR8a+DKhrrtgzvXqLS7TKaXGAecDTxqfK+JwvZIl6UfTBdQsGviXUqpUKXWr2cF0UaS1PgChZAIUmhxPpDuUUuuN8k/cy06RjA2B5hEaJSbMNesSF5h8zYxSxVqgGniH0G/f9TrUdRdM+n/ZNS6tdfh6PWhcr98rpdLjHRehDgc/AsK7obuIw/VKlqQfTRdQs5yktZ5PaAOa25VSp5gd0DDwJ2AyMBc4ADxkViBKqSzgNeB7WutGs+Loqoe4TL9mWuuA1nouoYaMC4EZPR0W36i6x6WUmk2oLcxRhLr/FgA/jmdMSqkLgGqtdWnk0z0cOuTXK1mSfjRdQE2htd5v/FkNvE7oP0OiqFJKjQYw/qw2OR4AtNZVxn/UIPAEJl0zpZSNUGJ9Tmu9zHja9GvWU1yJcs2MWOqBDwjVzvOMRotg8v/LiLgWG2UyrbVuA5YS/+t1EnCRUmoPoXL0lwmN/GN+vZIl6UfTBTTulFKZSqns8GNCu4ht7PusuIrsjHoD8DcTY2kXTqqGSzHhmhn11aeALVrr30W8ZOo16y0us6+ZUmqkUirPeOwEziR0v+F9Ql13wZzr1VNcWyN+cCtCdfO4Xi+t9T1a63Fa6xJC+eo9rfU1xON6mX33eqg+gPMIzWTYCfzE7HiMmCYRmkm0DthkZlzAC4R+7fcR+s3oFkI1xHeBHcafBQkS17PABmA9oSQ72oS4Tib0q/V6YK3xcZ7Z16yPuEy9ZsDRwBrj628E7jOenwSsAMqAV4D0BInrPeN6bSS0h3dWvP+NRcR4Gh2zd2J+vWRFrhBCpJBkKe8IIYSIgiR9IYRIIZL0hRAihUjSF0KIFCJJXwghUogkfSGESCGS9IUQIoVI0hdCiBTy/wEl1KP7BOaEdwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.yscale(\"logit\")\n",
+    "plt.plot(df[\"dti_cat\"].index, df[\"mean_bad_loan\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dti_cat</th>\n",
+       "      <th>nrow</th>\n",
+       "      <th>mean_bad_loan</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>(29.0,30.0]</td>\n",
+       "      <td>2329</td>\n",
+       "      <td>0.280378</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>(30.0,31.0]</td>\n",
+       "      <td>1496</td>\n",
+       "      <td>0.293449</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>(31.0,32.0]</td>\n",
+       "      <td>1282</td>\n",
+       "      <td>0.287051</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>(32.0,33.0]</td>\n",
+       "      <td>1051</td>\n",
+       "      <td>0.308278</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>(33.0,34.0]</td>\n",
+       "      <td>1025</td>\n",
+       "      <td>0.290732</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>(34.0,35.0]</td>\n",
+       "      <td>750</td>\n",
+       "      <td>0.318667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>(35.0,36.0]</td>\n",
+       "      <td>75</td>\n",
+       "      <td>0.160000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>(36.0,37.0]</td>\n",
+       "      <td>66</td>\n",
+       "      <td>0.227273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>(37.0,38.0]</td>\n",
+       "      <td>65</td>\n",
+       "      <td>0.123077</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>(38.0,39.0]</td>\n",
+       "      <td>59</td>\n",
+       "      <td>0.322034</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>(39.0,40.0]</td>\n",
+       "      <td>47</td>\n",
+       "      <td>0.063830</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        dti_cat  nrow  mean_bad_loan\n",
+       "30  (29.0,30.0]  2329       0.280378\n",
+       "31  (30.0,31.0]  1496       0.293449\n",
+       "32  (31.0,32.0]  1282       0.287051\n",
+       "33  (32.0,33.0]  1051       0.308278\n",
+       "34  (33.0,34.0]  1025       0.290732\n",
+       "35  (34.0,35.0]   750       0.318667\n",
+       "36  (35.0,36.0]    75       0.160000\n",
+       "37  (36.0,37.0]    66       0.227273\n",
+       "38  (37.0,38.0]    65       0.123077\n",
+       "39  (38.0,39.0]    59       0.322034\n",
+       "40  (39.0,40.0]    47       0.063830"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[30:41]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_trans.remove(\"dti\")\n",
+    "x_trans.append(\"dti_core\")\n",
+    "x_trans.append(\"dti_delta\")\n",
+    "\n",
+    "train[\"dti_core\"] = h2o.H2OFrame.ifelse(train[\"dti\"] <= 5, 5, train[\"dti\"])\n",
+    "train[\"dti_core\"] = h2o.H2OFrame.ifelse(train[\"dti_core\"] <= 30, train[\"dti_core\"], 30)\n",
+    "\n",
+    "train[\"dti_delta\"] = train[\"dti\"] - train[\"dti_core\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Separating Typical from Extreme Number of Delinquencies in the Past 2 Years\n",
+    "\n",
+    "The chance of a bad loan seems to max out at 3 delinquent payments in the past two years. To reflect this finding in the modeling, we will replace the original `delinq_2yrs` measure with two derived measures:\n",
+    "\n",
+    "\\begin{eqnarray}\n",
+    "delinq\\_2yrs\\_core & = & \\min(delinq\\_2yrs, 3) \\\\\n",
+    "delinq\\_2yrs\\_diff & = & delinq\\_2yrs - delinq\\_2yrs\\_core\n",
+    "\\end{eqnarray}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th style=\"text-align: right;\">  Probs</th><th style=\"text-align: right;\">  delinq_2yrsQuantiles</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td style=\"text-align: right;\">   0   </td><td style=\"text-align: right;\">                     0</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.05</td><td style=\"text-align: right;\">                     0</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.25</td><td style=\"text-align: right;\">                     0</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.5 </td><td style=\"text-align: right;\">                     0</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.75</td><td style=\"text-align: right;\">                     0</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.95</td><td style=\"text-align: right;\">                     1</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   1   </td><td style=\"text-align: right;\">                    29</td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train[\"delinq_2yrs\"].quantile([0, 0.05, 0.25, 0.5, 0.75, 0.95, 1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "breaks = np.linspace(0, 5, 6).tolist()\n",
+    "train[\"delinq_2yrs_cat\"] = train[\"delinq_2yrs\"].cut(breaks = breaks)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>delinq_2yrs_cat</th>\n",
+       "      <th>nrow</th>\n",
+       "      <th>mean_bad_loan</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.181037</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>(0.0,1.0]</td>\n",
+       "      <td>17158</td>\n",
+       "      <td>0.189299</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>(1.0,2.0]</td>\n",
+       "      <td>4635</td>\n",
+       "      <td>0.201510</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>(2.0,3.0]</td>\n",
+       "      <td>1488</td>\n",
+       "      <td>0.221774</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>(3.0,4.0]</td>\n",
+       "      <td>579</td>\n",
+       "      <td>0.215889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>(4.0,5.0]</td>\n",
+       "      <td>310</td>\n",
+       "      <td>0.216129</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  delinq_2yrs_cat   nrow  mean_bad_loan\n",
+       "0             NaN      0       0.181037\n",
+       "1       (0.0,1.0]  17158       0.189299\n",
+       "2       (1.0,2.0]   4635       0.201510\n",
+       "3       (2.0,3.0]   1488       0.221774\n",
+       "4       (3.0,4.0]    579       0.215889\n",
+       "5       (4.0,5.0]    310       0.216129"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_target(train, \"delinq_2yrs_cat\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_trans.remove(\"delinq_2yrs\")\n",
+    "x_trans.append(\"delinq_2yrs_core\")\n",
+    "x_trans.append(\"delinq_2yrs_delta\")\n",
+    "\n",
+    "train[\"delinq_2yrs_core\"] = h2o.H2OFrame.ifelse(train[\"delinq_2yrs\"] <= 3, train[\"delinq_2yrs\"], 3)\n",
+    "\n",
+    "train[\"delinq_2yrs_delta\"] = train[\"delinq_2yrs\"] - train[\"delinq_2yrs_core\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Separating Typical from Extreme Revolving Credit Line Utilized\n",
+    "\n",
+    "The relationship between credit line utilized is somewhat interesting. There appears to be a higher rate for a bad loan when 0% of the credit lines are utilized, then it drops down slightly and roughly increases linearly in credit line utilized up to 100%. To reflect this finding in the modeling, we will replace the original `revol_util` measure with three derived measures:\n",
+    "\n",
+    "\\begin{eqnarray}\n",
+    "revol\\_util\\_0 & = & I(revol\\_util == 0) \\\\\n",
+    "revol\\_util\\_core & = & \\max(5, \\min(revol\\_util, 30)) \\\\\n",
+    "revol\\_util\\_diff & = & revol\\_util - revol\\_util\\_core\n",
+    "\\end{eqnarray}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th style=\"text-align: right;\">  Probs</th><th style=\"text-align: right;\">  revol_utilQuantiles</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td style=\"text-align: right;\">   0   </td><td style=\"text-align: right;\">                  0  </td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.05</td><td style=\"text-align: right;\">                  8.7</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.25</td><td style=\"text-align: right;\">                 35.6</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.5 </td><td style=\"text-align: right;\">                 55.8</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.75</td><td style=\"text-align: right;\">                 74.2</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.95</td><td style=\"text-align: right;\">                 92.5</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   1   </td><td style=\"text-align: right;\">                150.7</td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train[\"revol_util\"].quantile([0, 0.05, 0.25, 0.5, 0.75, 0.95, 1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "breaks = np.linspace(0, 120, 25).tolist()\n",
+    "train[\"revol_util_cat\"] = train[\"revol_util\"].cut(breaks = breaks)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = mean_target(train, \"revol_util_cat\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7febe3b0ee80>]"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.yscale(\"logit\")\n",
+    "plt.plot(df[\"revol_util_cat\"].index, df[\"mean_bad_loan\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>revol_util_cat</th>\n",
+       "      <th>nrow</th>\n",
+       "      <th>mean_bad_loan</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>(95.0,100.0]</td>\n",
+       "      <td>4316</td>\n",
+       "      <td>0.255097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>(100.0,105.0]</td>\n",
+       "      <td>238</td>\n",
+       "      <td>0.260504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>(105.0,110.0]</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>(110.0,115.0]</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>(115.0,120.0]</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   revol_util_cat  nrow  mean_bad_loan\n",
+       "20   (95.0,100.0]  4316       0.255097\n",
+       "21  (100.0,105.0]   238       0.260504\n",
+       "22  (105.0,110.0]    35       0.200000\n",
+       "23  (110.0,115.0]     9       0.333333\n",
+       "24  (115.0,120.0]     3       0.000000"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[20:25]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_trans.remove(\"revol_util\")\n",
+    "x_trans.append(\"revol_util_0\")\n",
+    "x_trans.append(\"revol_util_core\")\n",
+    "x_trans.append(\"revol_util_delta\")\n",
+    "\n",
+    "train[\"revol_util_0\"] = train[\"revol_util\"] == 0\n",
+    "\n",
+    "train[\"revol_util_core\"] = h2o.H2OFrame.ifelse(train[\"revol_util\"] <= 100, train[\"revol_util\"], 100)\n",
+    "\n",
+    "train[\"revol_util_delta\"] = train[\"revol_util\"] - train[\"revol_util_core\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Separating Typical from Extreme Number of Credit Lines\n",
+    "\n",
+    "Looking at the occurance of bad loans on a logit scale reveal that the chance of a bad loan roughly decreases linearly in number of lines of credit up to about 50. To reflect this finding in the modeling, we will replace the original `total_acc` measure with two derived measures:\n",
+    "\n",
+    "\\begin{eqnarray}\n",
+    "total\\_acc\\_core & = & \\min(total\\_acc, 50) \\\\\n",
+    "total\\_acc\\_diff & = & total\\_acc - total\\_acc\\_core\n",
+    "\\end{eqnarray}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th style=\"text-align: right;\">  Probs</th><th style=\"text-align: right;\">  total_accQuantiles</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td style=\"text-align: right;\">   0   </td><td style=\"text-align: right;\">                   1</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.05</td><td style=\"text-align: right;\">                   8</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.25</td><td style=\"text-align: right;\">                  16</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.5 </td><td style=\"text-align: right;\">                  23</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.75</td><td style=\"text-align: right;\">                  31</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.95</td><td style=\"text-align: right;\">                  46</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   1   </td><td style=\"text-align: right;\">                 118</td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train[\"total_acc\"].quantile([0, 0.05, 0.25, 0.5, 0.75, 0.95, 1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "breaks = np.linspace(0, 60, 13).tolist()\n",
+    "train[\"total_acc_cat\"] = train[\"total_acc\"].cut(breaks = breaks)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = mean_target(train, \"total_acc_cat\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7febdfd6d208>]"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.yscale(\"logit\")\n",
+    "plt.plot(df[\"total_acc_cat\"].index, df[\"mean_bad_loan\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th style=\"text-align: right;\">  C1</th><th style=\"text-align: right;\">  Count</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td style=\"text-align: right;\">   0</td><td style=\"text-align: right;\"> 163958</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   1</td><td style=\"text-align: right;\">     29</td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(train[\"total_acc\"] == None).table()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>total_acc_cat</th>\n",
+       "      <th>nrow</th>\n",
+       "      <th>mean_bad_loan</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.153788</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>(0.0,5.0]</td>\n",
+       "      <td>2309</td>\n",
+       "      <td>0.226072</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>(5.0,10.0]</td>\n",
+       "      <td>12717</td>\n",
+       "      <td>0.210034</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  total_acc_cat   nrow  mean_bad_loan\n",
+       "0           NaN      0       0.153788\n",
+       "1     (0.0,5.0]   2309       0.226072\n",
+       "2    (5.0,10.0]  12717       0.210034"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[0:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>total_acc_cat</th>\n",
+       "      <th>nrow</th>\n",
+       "      <th>mean_bad_loan</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>(35.0,40.0]</td>\n",
+       "      <td>11251</td>\n",
+       "      <td>0.163719</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>(40.0,45.0]</td>\n",
+       "      <td>7174</td>\n",
+       "      <td>0.161137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>(45.0,50.0]</td>\n",
+       "      <td>4203</td>\n",
+       "      <td>0.157507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>(50.0,55.0]</td>\n",
+       "      <td>2350</td>\n",
+       "      <td>0.140426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>(55.0,60.0]</td>\n",
+       "      <td>1287</td>\n",
+       "      <td>0.165501</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   total_acc_cat   nrow  mean_bad_loan\n",
+       "8    (35.0,40.0]  11251       0.163719\n",
+       "9    (40.0,45.0]   7174       0.161137\n",
+       "10   (45.0,50.0]   4203       0.157507\n",
+       "11   (50.0,55.0]   2350       0.140426\n",
+       "12   (55.0,60.0]   1287       0.165501"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[8:13]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_trans.remove(\"total_acc\")\n",
+    "x_trans.append(\"total_acc_core\")\n",
+    "x_trans.append(\"total_acc_delta\")\n",
+    "\n",
+    "train[\"total_acc_core\"] = h2o.H2OFrame.ifelse(train[\"total_acc\"] <= 50, train[\"total_acc\"], 50)\n",
+    "\n",
+    "train[\"total_acc_delta\"] = train[\"total_acc\"] - train[\"total_acc_core\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Separating Typical from Extreme Longest Credit Length\n",
+    "\n",
+    "Looking at the occurance of bad loans on a logit scale reveal that the chance of a bad loan roughly decreases linearly in longest credit length from 3 to 20 years and is highly volatile outside of that range due to small numbers of observations. To reflect this finding in the modeling, we will replace the original `longest_credit_length` measure with two derived measures:\n",
+    "\n",
+    "\\begin{eqnarray}\n",
+    "longest\\_credit\\_length\\_core & = & \\max(3, \\min(longest\\_credit\\_length, 20)) \\\\\n",
+    "longest\\_credit\\_length\\_diff & = & longest\\_credit\\_length - longest\\_credit\\_length\\_core\n",
+    "\\end{eqnarray}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th style=\"text-align: right;\">  Probs</th><th style=\"text-align: right;\">  longest_credit_lengthQuantiles</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td style=\"text-align: right;\">   0   </td><td style=\"text-align: right;\">                               0</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.05</td><td style=\"text-align: right;\">                               6</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.25</td><td style=\"text-align: right;\">                              10</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.5 </td><td style=\"text-align: right;\">                              14</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.75</td><td style=\"text-align: right;\">                              18</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   0.95</td><td style=\"text-align: right;\">                              28</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">   1   </td><td style=\"text-align: right;\">                              65</td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train[\"longest_credit_length\"].quantile([0, 0.05, 0.25, 0.5, 0.75, 0.95, 1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "breaks = np.linspace(0, 25, 26).tolist()\n",
+    "train[\"longest_credit_length_cat\"] = train[\"longest_credit_length\"].cut(breaks = breaks)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = mean_target(train, \"longest_credit_length_cat\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7febe37b3860>]"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.yscale(\"logit\")\n",
+    "plt.plot(df[\"longest_credit_length_cat\"].index, df[\"mean_bad_loan\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>longest_credit_length_cat</th>\n",
+       "      <th>nrow</th>\n",
+       "      <th>mean_bad_loan</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.171094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>(0.0,1.0]</td>\n",
+       "      <td>67</td>\n",
+       "      <td>0.253731</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>(1.0,2.0]</td>\n",
+       "      <td>100</td>\n",
+       "      <td>0.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>(2.0,3.0]</td>\n",
+       "      <td>914</td>\n",
+       "      <td>0.205689</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  longest_credit_length_cat  nrow  mean_bad_loan\n",
+       "0                       NaN     0       0.171094\n",
+       "1                 (0.0,1.0]    67       0.253731\n",
+       "2                 (1.0,2.0]   100       0.300000\n",
+       "3                 (2.0,3.0]   914       0.205689"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[0:4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>longest_credit_length_cat</th>\n",
+       "      <th>nrow</th>\n",
+       "      <th>mean_bad_loan</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>(19.0,20.0]</td>\n",
+       "      <td>4682</td>\n",
+       "      <td>0.163392</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>(20.0,21.0]</td>\n",
+       "      <td>3892</td>\n",
+       "      <td>0.157760</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>(21.0,22.0]</td>\n",
+       "      <td>3350</td>\n",
+       "      <td>0.160000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>(22.0,23.0]</td>\n",
+       "      <td>3092</td>\n",
+       "      <td>0.167206</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>(23.0,24.0]</td>\n",
+       "      <td>2856</td>\n",
+       "      <td>0.176821</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>(24.0,25.0]</td>\n",
+       "      <td>2471</td>\n",
+       "      <td>0.164711</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   longest_credit_length_cat  nrow  mean_bad_loan\n",
+       "20               (19.0,20.0]  4682       0.163392\n",
+       "21               (20.0,21.0]  3892       0.157760\n",
+       "22               (21.0,22.0]  3350       0.160000\n",
+       "23               (22.0,23.0]  3092       0.167206\n",
+       "24               (23.0,24.0]  2856       0.176821\n",
+       "25               (24.0,25.0]  2471       0.164711"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[20:26]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_trans.remove(\"longest_credit_length\")\n",
+    "x_trans.append(\"longest_credit_length_core\")\n",
+    "x_trans.append(\"longest_credit_length_delta\")\n",
+    "\n",
+    "train[\"longest_credit_length_core\"] = h2o.H2OFrame.ifelse(train[\"longest_credit_length\"] <= 3,\n",
+    "                                                          3, train[\"longest_credit_length\"])\n",
+    "train[\"longest_credit_length_core\"] = h2o.H2OFrame.ifelse(train[\"longest_credit_length_core\"] <= 20,\n",
+    "                                                          train[\"longest_credit_length_core\"], 20)\n",
+    "\n",
+    "train[\"longest_credit_length_delta\"] = train[\"longest_credit_length\"] - train[\"longest_credit_length_core\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Converting Income Verification Status to a 0/1 Indicator\n",
+    "\n",
+    "Given that incomes are either verified or not verified, we will create a simplifed `verified` binary indicator that is 1 when income has been verified."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th>verification_status  </th><th style=\"text-align: right;\">  Count</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td>not verified         </td><td style=\"text-align: right;\">  59155</td></tr>\n",
+       "<tr><td>verified             </td><td style=\"text-align: right;\"> 104832</td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train[\"verification_status\"].table()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_trans.remove(\"verification_status\")\n",
+    "x_trans.append(\"verified\")\n",
+    "\n",
+    "train[\"verified\"] = train[\"verification_status\"] == \"verified\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th style=\"text-align: right;\">  verified</th><th style=\"text-align: right;\">  Count</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td style=\"text-align: right;\">         0</td><td style=\"text-align: right;\">  59155</td></tr>\n",
+       "<tr><td style=\"text-align: right;\">         1</td><td style=\"text-align: right;\"> 104832</td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train[\"verified\"].table()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train Models Using Transformed Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th>model_id                                           </th><th style=\"text-align: right;\">     auc</th><th style=\"text-align: right;\">  logloss</th><th style=\"text-align: right;\">  mean_per_class_error</th><th style=\"text-align: right;\">    rmse</th><th style=\"text-align: right;\">     mse</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td>StackedEnsemble_AllModels_AutoML_20190204_105352   </td><td style=\"text-align: right;\">0.684653</td><td style=\"text-align: right;\"> 0.444916</td><td style=\"text-align: right;\">              0.366459</td><td style=\"text-align: right;\">0.374001</td><td style=\"text-align: right;\">0.139877</td></tr>\n",
+       "<tr><td>StackedEnsemble_BestOfFamily_AutoML_20190204_105352</td><td style=\"text-align: right;\">0.684095</td><td style=\"text-align: right;\"> 0.445076</td><td style=\"text-align: right;\">              0.3681  </td><td style=\"text-align: right;\">0.374064</td><td style=\"text-align: right;\">0.139924</td></tr>\n",
+       "<tr><td>XGBoost_1_AutoML_20190204_105352                   </td><td style=\"text-align: right;\">0.682381</td><td style=\"text-align: right;\"> 0.444104</td><td style=\"text-align: right;\">              0.36856 </td><td style=\"text-align: right;\">0.373716</td><td style=\"text-align: right;\">0.139664</td></tr>\n",
+       "<tr><td>XGBoost_2_AutoML_20190204_105352                   </td><td style=\"text-align: right;\">0.681244</td><td style=\"text-align: right;\"> 0.444554</td><td style=\"text-align: right;\">              0.369625</td><td style=\"text-align: right;\">0.37401 </td><td style=\"text-align: right;\">0.139883</td></tr>\n",
+       "<tr><td>XGBoost_3_AutoML_20190204_105352                   </td><td style=\"text-align: right;\">0.676053</td><td style=\"text-align: right;\"> 0.446428</td><td style=\"text-align: right;\">              0.372664</td><td style=\"text-align: right;\">0.374848</td><td style=\"text-align: right;\">0.140511</td></tr>\n",
+       "<tr><td>GLM_grid_1_AutoML_20190204_105352_model_1          </td><td style=\"text-align: right;\">0.674157</td><td style=\"text-align: right;\"> 0.447619</td><td style=\"text-align: right;\">              0.373543</td><td style=\"text-align: right;\">0.374973</td><td style=\"text-align: right;\">0.140605</td></tr>\n",
+       "<tr><td>XRT_1_AutoML_20190204_105352                       </td><td style=\"text-align: right;\">0.666562</td><td style=\"text-align: right;\"> 0.449967</td><td style=\"text-align: right;\">              0.379013</td><td style=\"text-align: right;\">0.376207</td><td style=\"text-align: right;\">0.141532</td></tr>\n",
+       "<tr><td>DRF_1_AutoML_20190204_105352                       </td><td style=\"text-align: right;\">0.662709</td><td style=\"text-align: right;\"> 0.45256 </td><td style=\"text-align: right;\">              0.382445</td><td style=\"text-align: right;\">0.377047</td><td style=\"text-align: right;\">0.142164</td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(aml.leaderboard)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['emp_length', 'loan_amnt_core', 'loan_amnt_delta', 'term_60months', 'emp_length_missing', 'home_ownership_3cat', 'annual_inc_core', 'annual_inc_delta', 'te_purpose', 'te_addr_state', 'dti_core', 'dti_delta', 'delinq_2yrs_core', 'delinq_2yrs_delta', 'revol_util_0', 'revol_util_core', 'revol_util_delta', 'total_acc_core', 'total_acc_delta', 'longest_credit_length_core', 'longest_credit_length_delta', 'verified']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(x_trans)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AutoML progress: |████████████████████████████████████████████████████████| 100%\n"
+     ]
+    }
+   ],
+   "source": [
+    "aml_trans = H2OAutoML(max_models = 6, exclude_algos = ['DeepLearning'])\n",
+    "aml_trans.train(x = x_trans, y = y, training_frame = train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead>\n",
+       "<tr><th>model_id                                           </th><th style=\"text-align: right;\">     auc</th><th style=\"text-align: right;\">  logloss</th><th style=\"text-align: right;\">  mean_per_class_error</th><th style=\"text-align: right;\">    rmse</th><th style=\"text-align: right;\">     mse</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "<tr><td>StackedEnsemble_AllModels_AutoML_20190204_110108   </td><td style=\"text-align: right;\">0.68491 </td><td style=\"text-align: right;\"> 0.444785</td><td style=\"text-align: right;\">              0.364736</td><td style=\"text-align: right;\">0.373931</td><td style=\"text-align: right;\">0.139825</td></tr>\n",
+       "<tr><td>StackedEnsemble_BestOfFamily_AutoML_20190204_110108</td><td style=\"text-align: right;\">0.684419</td><td style=\"text-align: right;\"> 0.444887</td><td style=\"text-align: right;\">              0.364876</td><td style=\"text-align: right;\">0.373962</td><td style=\"text-align: right;\">0.139848</td></tr>\n",
+       "<tr><td>XGBoost_1_AutoML_20190204_110108                   </td><td style=\"text-align: right;\">0.684239</td><td style=\"text-align: right;\"> 0.443425</td><td style=\"text-align: right;\">              0.365317</td><td style=\"text-align: right;\">0.37343 </td><td style=\"text-align: right;\">0.13945 </td></tr>\n",
+       "<tr><td>XGBoost_2_AutoML_20190204_110108                   </td><td style=\"text-align: right;\">0.681666</td><td style=\"text-align: right;\"> 0.444487</td><td style=\"text-align: right;\">              0.367792</td><td style=\"text-align: right;\">0.374001</td><td style=\"text-align: right;\">0.139877</td></tr>\n",
+       "<tr><td>GLM_grid_1_AutoML_20190204_110108_model_1          </td><td style=\"text-align: right;\">0.67722 </td><td style=\"text-align: right;\"> 0.445658</td><td style=\"text-align: right;\">              0.371839</td><td style=\"text-align: right;\">0.374414</td><td style=\"text-align: right;\">0.140186</td></tr>\n",
+       "<tr><td>XGBoost_3_AutoML_20190204_110108                   </td><td style=\"text-align: right;\">0.676057</td><td style=\"text-align: right;\"> 0.446528</td><td style=\"text-align: right;\">              0.372181</td><td style=\"text-align: right;\">0.374933</td><td style=\"text-align: right;\">0.140575</td></tr>\n",
+       "<tr><td>DRF_1_AutoML_20190204_110108                       </td><td style=\"text-align: right;\">0.667149</td><td style=\"text-align: right;\"> 0.450572</td><td style=\"text-align: right;\">              0.378345</td><td style=\"text-align: right;\">0.376559</td><td style=\"text-align: right;\">0.141797</td></tr>\n",
+       "<tr><td>XRT_1_AutoML_20190204_110108                       </td><td style=\"text-align: right;\">0.666489</td><td style=\"text-align: right;\"> 0.450549</td><td style=\"text-align: right;\">              0.379019</td><td style=\"text-align: right;\">0.376585</td><td style=\"text-align: right;\">0.141816</td></tr>\n",
+       "</tbody>\n",
+       "</table>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(aml_trans.leaderboard)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_model_trans = h2o.get_model(aml_trans.leaderboard[2,'model_id'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model Details\n",
+      "=============\n",
+      "H2OXGBoostEstimator :  XGBoost\n",
+      "Model Key:  XGBoost_1_AutoML_20190204_105352\n",
+      "\n",
+      "\n",
+      "ModelMetricsBinomial: xgboost\n",
+      "** Reported on train data. **\n",
+      "\n",
+      "MSE: 0.1375855946755337\n",
+      "RMSE: 0.3709253222355326\n",
+      "LogLoss: 0.43801446987930465\n",
+      "Mean Per-Class Error: 0.3559811560155455\n",
+      "AUC: 0.6993402202868698\n",
+      "pr_auc: 0.3503558980432989\n",
+      "Gini: 0.39868044057373964\n",
+      "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.19257715782698462: \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>90524.0</td>\n",
+       "<td>43447.0</td>\n",
+       "<td>0.3243</td>\n",
+       "<td> (43447.0/133971.0)</td></tr>\n",
+       "<tr><td>1</td>\n",
+       "<td>11663.0</td>\n",
+       "<td>18353.0</td>\n",
+       "<td>0.3886</td>\n",
+       "<td> (11663.0/30016.0)</td></tr>\n",
+       "<tr><td>Total</td>\n",
+       "<td>102187.0</td>\n",
+       "<td>61800.0</td>\n",
+       "<td>0.3361</td>\n",
+       "<td> (55110.0/163987.0)</td></tr></table></div>"
+      ],
+      "text/plain": [
+       "       0       1      Error    Rate\n",
+       "-----  ------  -----  -------  ------------------\n",
+       "0      90524   43447  0.3243   (43447.0/133971.0)\n",
+       "1      11663   18353  0.3886   (11663.0/30016.0)\n",
+       "Total  102187  61800  0.3361   (55110.0/163987.0)"
+      ]
+     },
+     "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>0.1925772</td>\n",
+       "<td>0.3997778</td>\n",
+       "<td>234.0</td></tr>\n",
+       "<tr><td>max f2</td>\n",
+       "<td>0.1268430</td>\n",
+       "<td>0.5601718</td>\n",
+       "<td>309.0</td></tr>\n",
+       "<tr><td>max f0point5</td>\n",
+       "<td>0.2817136</td>\n",
+       "<td>0.3730883</td>\n",
+       "<td>151.0</td></tr>\n",
+       "<tr><td>max accuracy</td>\n",
+       "<td>0.4535213</td>\n",
+       "<td>0.8193271</td>\n",
+       "<td>52.0</td></tr>\n",
+       "<tr><td>max precision</td>\n",
+       "<td>0.7562991</td>\n",
+       "<td>1.0</td>\n",
+       "<td>0.0</td></tr>\n",
+       "<tr><td>max recall</td>\n",
+       "<td>0.0424902</td>\n",
+       "<td>1.0</td>\n",
+       "<td>397.0</td></tr>\n",
+       "<tr><td>max specificity</td>\n",
+       "<td>0.7562991</td>\n",
+       "<td>1.0</td>\n",
+       "<td>0.0</td></tr>\n",
+       "<tr><td>max absolute_mcc</td>\n",
+       "<td>0.2184510</td>\n",
+       "<td>0.2332565</td>\n",
+       "<td>208.0</td></tr>\n",
+       "<tr><td>max min_per_class_accuracy</td>\n",
+       "<td>0.1846351</td>\n",
+       "<td>0.6429238</td>\n",
+       "<td>242.0</td></tr>\n",
+       "<tr><td>max mean_per_class_accuracy</td>\n",
+       "<td>0.1865885</td>\n",
+       "<td>0.6440188</td>\n",
+       "<td>240.0</td></tr></table></div>"
+      ],
+      "text/plain": [
+       "metric                       threshold    value     idx\n",
+       "---------------------------  -----------  --------  -----\n",
+       "max f1                       0.192577     0.399778  234\n",
+       "max f2                       0.126843     0.560172  309\n",
+       "max f0point5                 0.281714     0.373088  151\n",
+       "max accuracy                 0.453521     0.819327  52\n",
+       "max precision                0.756299     1         0\n",
+       "max recall                   0.0424902    1         397\n",
+       "max specificity              0.756299     1         0\n",
+       "max absolute_mcc             0.218451     0.233257  208\n",
+       "max min_per_class_accuracy   0.184635     0.642924  242\n",
+       "max mean_per_class_accuracy  0.186588     0.644019  240"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gains/Lift Table: Avg response rate: 18.30 %, avg score: 18.36 %\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>score</b></td>\n",
+       "<td><b>cumulative_response_rate</b></td>\n",
+       "<td><b>cumulative_score</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>0.0100008</td>\n",
+       "<td>0.4635638</td>\n",
+       "<td>3.2779917</td>\n",
+       "<td>3.2779917</td>\n",
+       "<td>0.6</td>\n",
+       "<td>0.5120752</td>\n",
+       "<td>0.6</td>\n",
+       "<td>0.5120752</td>\n",
+       "<td>0.0327825</td>\n",
+       "<td>0.0327825</td>\n",
+       "<td>227.7991738</td>\n",
+       "<td>227.7991738</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>2</td>\n",
+       "<td>0.0200016</td>\n",
+       "<td>0.4273828</td>\n",
+       "<td>2.8149421</td>\n",
+       "<td>3.0464669</td>\n",
+       "<td>0.5152439</td>\n",
+       "<td>0.4435705</td>\n",
+       "<td>0.5576220</td>\n",
+       "<td>0.4778229</td>\n",
+       "<td>0.0281517</td>\n",
+       "<td>0.0609342</td>\n",
+       "<td>181.4942092</td>\n",
+       "<td>204.6466915</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>3</td>\n",
+       "<td>0.0300024</td>\n",
+       "<td>0.4031179</td>\n",
+       "<td>2.4351748</td>\n",
+       "<td>2.8427029</td>\n",
+       "<td>0.4457317</td>\n",
+       "<td>0.4147789</td>\n",
+       "<td>0.5203252</td>\n",
+       "<td>0.4568082</td>\n",
+       "<td>0.0243537</td>\n",
+       "<td>0.0852878</td>\n",
+       "<td>143.5174756</td>\n",
+       "<td>184.2702862</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>4</td>\n",
+       "<td>0.0400032</td>\n",
+       "<td>0.3839370</td>\n",
+       "<td>2.3718802</td>\n",
+       "<td>2.7249972</td>\n",
+       "<td>0.4341463</td>\n",
+       "<td>0.3933012</td>\n",
+       "<td>0.4987805</td>\n",
+       "<td>0.4409315</td>\n",
+       "<td>0.0237207</td>\n",
+       "<td>0.1090085</td>\n",
+       "<td>137.1880200</td>\n",
+       "<td>172.4997197</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>5</td>\n",
+       "<td>0.0500040</td>\n",
+       "<td>0.3683249</td>\n",
+       "<td>2.2253033</td>\n",
+       "<td>2.6250584</td>\n",
+       "<td>0.4073171</td>\n",
+       "<td>0.3759864</td>\n",
+       "<td>0.4804878</td>\n",
+       "<td>0.4279424</td>\n",
+       "<td>0.0222548</td>\n",
+       "<td>0.1312633</td>\n",
+       "<td>122.5303334</td>\n",
+       "<td>162.5058424</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>6</td>\n",
+       "<td>0.1000018</td>\n",
+       "<td>0.3130251</td>\n",
+       "<td>2.0050163</td>\n",
+       "<td>2.3150563</td>\n",
+       "<td>0.3669960</td>\n",
+       "<td>0.3380940</td>\n",
+       "<td>0.4237454</td>\n",
+       "<td>0.3830209</td>\n",
+       "<td>0.1002465</td>\n",
+       "<td>0.2315099</td>\n",
+       "<td>100.5016290</td>\n",
+       "<td>131.5056262</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>7</td>\n",
+       "<td>0.1499997</td>\n",
+       "<td>0.2773508</td>\n",
+       "<td>1.7511408</td>\n",
+       "<td>2.1270921</td>\n",
+       "<td>0.3205269</td>\n",
+       "<td>0.2941653</td>\n",
+       "<td>0.3893406</td>\n",
+       "<td>0.3534036</td>\n",
+       "<td>0.0875533</td>\n",
+       "<td>0.3190632</td>\n",
+       "<td>75.1140848</td>\n",
+       "<td>112.7092099</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>8</td>\n",
+       "<td>0.2000037</td>\n",
+       "<td>0.2518945</td>\n",
+       "<td>1.5170706</td>\n",
+       "<td>1.9745774</td>\n",
+       "<td>0.2776829</td>\n",
+       "<td>0.2640652</td>\n",
+       "<td>0.3614245</td>\n",
+       "<td>0.3310676</td>\n",
+       "<td>0.0758595</td>\n",
+       "<td>0.3949227</td>\n",
+       "<td>51.7070566</td>\n",
+       "<td>97.4577416</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>9</td>\n",
+       "<td>0.2999994</td>\n",
+       "<td>0.2144557</td>\n",
+       "<td>1.2990293</td>\n",
+       "<td>1.7494039</td>\n",
+       "<td>0.2377729</td>\n",
+       "<td>0.2320772</td>\n",
+       "<td>0.3202090</td>\n",
+       "<td>0.2980722</td>\n",
+       "<td>0.1298974</td>\n",
+       "<td>0.5248201</td>\n",
+       "<td>29.9029331</td>\n",
+       "<td>74.9403876</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>10</td>\n",
+       "<td>0.4000012</td>\n",
+       "<td>0.1861287</td>\n",
+       "<td>1.1047239</td>\n",
+       "<td>1.5882314</td>\n",
+       "<td>0.2022075</td>\n",
+       "<td>0.1996984</td>\n",
+       "<td>0.2907081</td>\n",
+       "<td>0.2734784</td>\n",
+       "<td>0.1104744</td>\n",
+       "<td>0.6352945</td>\n",
+       "<td>10.4723926</td>\n",
+       "<td>58.8231431</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>11</td>\n",
+       "<td>0.5000030</td>\n",
+       "<td>0.1629951</td>\n",
+       "<td>0.9244900</td>\n",
+       "<td>1.4554815</td>\n",
+       "<td>0.1692176</td>\n",
+       "<td>0.1743251</td>\n",
+       "<td>0.2664097</td>\n",
+       "<td>0.2536475</td>\n",
+       "<td>0.0924507</td>\n",
+       "<td>0.7277452</td>\n",
+       "<td>-7.5509983</td>\n",
+       "<td>45.5481530</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>12</td>\n",
+       "<td>0.5999988</td>\n",
+       "<td>0.1431221</td>\n",
+       "<td>0.7999409</td>\n",
+       "<td>1.3462292</td>\n",
+       "<td>0.1464203</td>\n",
+       "<td>0.1528475</td>\n",
+       "<td>0.2464123</td>\n",
+       "<td>0.2368482</td>\n",
+       "<td>0.0799907</td>\n",
+       "<td>0.8077359</td>\n",
+       "<td>-20.0059137</td>\n",
+       "<td>34.6229193</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>13</td>\n",
+       "<td>0.7000006</td>\n",
+       "<td>0.1244567</td>\n",
+       "<td>0.6829566</td>\n",
+       "<td>1.2514743</td>\n",
+       "<td>0.1250076</td>\n",
+       "<td>0.1337421</td>\n",
+       "<td>0.2290685</td>\n",
+       "<td>0.2221185</td>\n",
+       "<td>0.0682969</td>\n",
+       "<td>0.8760328</td>\n",
+       "<td>-31.7043411</td>\n",
+       "<td>25.1474313</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>14</td>\n",
+       "<td>0.7999963</td>\n",
+       "<td>0.1063103</td>\n",
+       "<td>0.5443996</td>\n",
+       "<td>1.1630933</td>\n",
+       "<td>0.0996463</td>\n",
+       "<td>0.1154926</td>\n",
+       "<td>0.2128913</td>\n",
+       "<td>0.2087907</td>\n",
+       "<td>0.0544376</td>\n",
+       "<td>0.9304704</td>\n",
+       "<td>-45.5600429</td>\n",
+       "<td>16.3093339</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>15</td>\n",
+       "<td>0.8999982</td>\n",
+       "<td>0.0856335</td>\n",
+       "<td>0.4307624</td>\n",
+       "<td>1.0817216</td>\n",
+       "<td>0.0788463</td>\n",
+       "<td>0.0963852</td>\n",
+       "<td>0.1979971</td>\n",
+       "<td>0.1963010</td>\n",
+       "<td>0.0430770</td>\n",
+       "<td>0.9735474</td>\n",
+       "<td>-56.9237625</td>\n",
+       "<td>8.1721578</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>16</td>\n",
+       "<td>1.0</td>\n",
+       "<td>0.0316745</td>\n",
+       "<td>0.2645207</td>\n",
+       "<td>1.0</td>\n",
+       "<td>0.0484176</td>\n",
+       "<td>0.0694961</td>\n",
+       "<td>0.1830389</td>\n",
+       "<td>0.1836203</td>\n",
+       "<td>0.0264526</td>\n",
+       "<td>1.0</td>\n",
+       "<td>-73.5479253</td>\n",
+       "<td>0.0</td></tr></table></div>"
+      ],
+      "text/plain": [
+       "    group    cumulative_data_fraction    lower_threshold    lift      cumulative_lift    response_rate    score      cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain      cumulative_gain\n",
+       "--  -------  --------------------------  -----------------  --------  -----------------  ---------------  ---------  --------------------------  ------------------  --------------  -------------------------  --------  -----------------\n",
+       "    1        0.0100008                   0.463564           3.27799   3.27799            0.6              0.512075   0.6                         0.512075            0.0327825       0.0327825                  227.799   227.799\n",
+       "    2        0.0200016                   0.427383           2.81494   3.04647            0.515244         0.443571   0.557622                    0.477823            0.0281517       0.0609342                  181.494   204.647\n",
+       "    3        0.0300024                   0.403118           2.43517   2.8427             0.445732         0.414779   0.520325                    0.456808            0.0243537       0.0852878                  143.517   184.27\n",
+       "    4        0.0400032                   0.383937           2.37188   2.725              0.434146         0.393301   0.49878                     0.440931            0.0237207       0.109009                   137.188   172.5\n",
+       "    5        0.050004                    0.368325           2.2253    2.62506            0.407317         0.375986   0.480488                    0.427942            0.0222548       0.131263                   122.53    162.506\n",
+       "    6        0.100002                    0.313025           2.00502   2.31506            0.366996         0.338094   0.423745                    0.383021            0.100247        0.23151                    100.502   131.506\n",
+       "    7        0.15                        0.277351           1.75114   2.12709            0.320527         0.294165   0.389341                    0.353404            0.0875533       0.319063                   75.1141   112.709\n",
+       "    8        0.200004                    0.251894           1.51707   1.97458            0.277683         0.264065   0.361424                    0.331068            0.0758595       0.394923                   51.7071   97.4577\n",
+       "    9        0.299999                    0.214456           1.29903   1.7494             0.237773         0.232077   0.320209                    0.298072            0.129897        0.52482                    29.9029   74.9404\n",
+       "    10       0.400001                    0.186129           1.10472   1.58823            0.202207         0.199698   0.290708                    0.273478            0.110474        0.635295                   10.4724   58.8231\n",
+       "    11       0.500003                    0.162995           0.92449   1.45548            0.169218         0.174325   0.26641                     0.253647            0.0924507       0.727745                   -7.551    45.5482\n",
+       "    12       0.599999                    0.143122           0.799941  1.34623            0.14642          0.152847   0.246412                    0.236848            0.0799907       0.807736                   -20.0059  34.6229\n",
+       "    13       0.700001                    0.124457           0.682957  1.25147            0.125008         0.133742   0.229068                    0.222118            0.0682969       0.876033                   -31.7043  25.1474\n",
+       "    14       0.799996                    0.10631            0.5444    1.16309            0.0996463        0.115493   0.212891                    0.208791            0.0544376       0.93047                    -45.56    16.3093\n",
+       "    15       0.899998                    0.0856335          0.430762  1.08172            0.0788463        0.0963852  0.197997                    0.196301            0.043077        0.973547                   -56.9238  8.17216\n",
+       "    16       1                           0.0316745          0.264521  1                  0.0484176        0.0694961  0.183039                    0.18362             0.0264526       1                          -73.5479  0"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "ModelMetricsBinomial: xgboost\n",
+      "** Reported on cross-validation data. **\n",
+      "\n",
+      "MSE: 0.13966377536338925\n",
+      "RMSE: 0.37371616952359615\n",
+      "LogLoss: 0.44410403240512103\n",
+      "Mean Per-Class Error: 0.36744047321629403\n",
+      "AUC: 0.6823805451020182\n",
+      "pr_auc: 0.32363072597274334\n",
+      "Gini: 0.36476109020403635\n",
+      "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.19381296035337148: \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>90483.0</td>\n",
+       "<td>43488.0</td>\n",
+       "<td>0.3246</td>\n",
+       "<td> (43488.0/133971.0)</td></tr>\n",
+       "<tr><td>1</td>\n",
+       "<td>12382.0</td>\n",
+       "<td>17634.0</td>\n",
+       "<td>0.4125</td>\n",
+       "<td> (12382.0/30016.0)</td></tr>\n",
+       "<tr><td>Total</td>\n",
+       "<td>102865.0</td>\n",
+       "<td>61122.0</td>\n",
+       "<td>0.3407</td>\n",
+       "<td> (55870.0/163987.0)</td></tr></table></div>"
+      ],
+      "text/plain": [
+       "       0       1      Error    Rate\n",
+       "-----  ------  -----  -------  ------------------\n",
+       "0      90483   43488  0.3246   (43488.0/133971.0)\n",
+       "1      12382   17634  0.4125   (12382.0/30016.0)\n",
+       "Total  102865  61122  0.3407   (55870.0/163987.0)"
+      ]
+     },
+     "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>0.1938130</td>\n",
+       "<td>0.3869736</td>\n",
+       "<td>230.0</td></tr>\n",
+       "<tr><td>max f2</td>\n",
+       "<td>0.1186643</td>\n",
+       "<td>0.5530505</td>\n",
+       "<td>319.0</td></tr>\n",
+       "<tr><td>max f0point5</td>\n",
+       "<td>0.2745510</td>\n",
+       "<td>0.3505269</td>\n",
+       "<td>155.0</td></tr>\n",
+       "<tr><td>max accuracy</td>\n",
+       "<td>0.4715899</td>\n",
+       "<td>0.8176197</td>\n",
+       "<td>42.0</td></tr>\n",
+       "<tr><td>max precision</td>\n",
+       "<td>0.7477095</td>\n",
+       "<td>1.0</td>\n",
+       "<td>0.0</td></tr>\n",
+       "<tr><td>max recall</td>\n",
+       "<td>0.0415521</td>\n",
+       "<td>1.0</td>\n",
+       "<td>396.0</td></tr>\n",
+       "<tr><td>max specificity</td>\n",
+       "<td>0.7477095</td>\n",
+       "<td>1.0</td>\n",
+       "<td>0.0</td></tr>\n",
+       "<tr><td>max absolute_mcc</td>\n",
+       "<td>0.2223240</td>\n",
+       "<td>0.2116015</td>\n",
+       "<td>201.0</td></tr>\n",
+       "<tr><td>max min_per_class_accuracy</td>\n",
+       "<td>0.1824965</td>\n",
+       "<td>0.6313904</td>\n",
+       "<td>243.0</td></tr>\n",
+       "<tr><td>max mean_per_class_accuracy</td>\n",
+       "<td>0.1824965</td>\n",
+       "<td>0.6325595</td>\n",
+       "<td>243.0</td></tr></table></div>"
+      ],
+      "text/plain": [
+       "metric                       threshold    value     idx\n",
+       "---------------------------  -----------  --------  -----\n",
+       "max f1                       0.193813     0.386974  230\n",
+       "max f2                       0.118664     0.553051  319\n",
+       "max f0point5                 0.274551     0.350527  155\n",
+       "max accuracy                 0.47159      0.81762   42\n",
+       "max precision                0.747709     1         0\n",
+       "max recall                   0.0415521    1         396\n",
+       "max specificity              0.747709     1         0\n",
+       "max absolute_mcc             0.222324     0.211602  201\n",
+       "max min_per_class_accuracy   0.182496     0.63139   243\n",
+       "max mean_per_class_accuracy  0.182496     0.63256   243"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gains/Lift Table: Avg response rate: 18.30 %, avg score: 18.36 %\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>score</b></td>\n",
+       "<td><b>cumulative_response_rate</b></td>\n",
+       "<td><b>cumulative_score</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>0.0100008</td>\n",
+       "<td>0.4660096</td>\n",
+       "<td>2.9015557</td>\n",
+       "<td>2.9015557</td>\n",
+       "<td>0.5310976</td>\n",
+       "<td>0.5133815</td>\n",
+       "<td>0.5310976</td>\n",
+       "<td>0.5133815</td>\n",
+       "<td>0.0290179</td>\n",
+       "<td>0.0290179</td>\n",
+       "<td>190.1555695</td>\n",
+       "<td>190.1555695</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>2</td>\n",
+       "<td>0.0200016</td>\n",
+       "<td>0.4279539</td>\n",
+       "<td>2.4218496</td>\n",
+       "<td>2.6617026</td>\n",
+       "<td>0.4432927</td>\n",
+       "<td>0.4450473</td>\n",
+       "<td>0.4871951</td>\n",
+       "<td>0.4792144</td>\n",
+       "<td>0.0242204</td>\n",
+       "<td>0.0532383</td>\n",
+       "<td>142.1849587</td>\n",
+       "<td>166.1702641</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>3</td>\n",
+       "<td>0.0300024</td>\n",
+       "<td>0.4027831</td>\n",
+       "<td>2.3552237</td>\n",
+       "<td>2.5595430</td>\n",
+       "<td>0.4310976</td>\n",
+       "<td>0.4144992</td>\n",
+       "<td>0.4684959</td>\n",
+       "<td>0.4576427</td>\n",
+       "<td>0.0235541</td>\n",
+       "<td>0.0767924</td>\n",
+       "<td>135.5223738</td>\n",
+       "<td>155.9543007</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>4</td>\n",
+       "<td>0.0400032</td>\n",
+       "<td>0.3834357</td>\n",
+       "<td>2.2652788</td>\n",
+       "<td>2.4859770</td>\n",
+       "<td>0.4146341</td>\n",
+       "<td>0.3927745</td>\n",
+       "<td>0.4550305</td>\n",
+       "<td>0.4414256</td>\n",
+       "<td>0.0226546</td>\n",
+       "<td>0.0994470</td>\n",
+       "<td>126.5278843</td>\n",
+       "<td>148.5976966</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>5</td>\n",
+       "<td>0.0500040</td>\n",
+       "<td>0.3672744</td>\n",
+       "<td>2.0687326</td>\n",
+       "<td>2.4025281</td>\n",
+       "<td>0.3786585</td>\n",
+       "<td>0.3754589</td>\n",
+       "<td>0.4397561</td>\n",
+       "<td>0.4282323</td>\n",
+       "<td>0.0206890</td>\n",
+       "<td>0.1201359</td>\n",
+       "<td>106.8732591</td>\n",
+       "<td>140.2528091</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>6</td>\n",
+       "<td>0.1000018</td>\n",
+       "<td>0.3118320</td>\n",
+       "<td>1.9157268</td>\n",
+       "<td>2.1591423</td>\n",
+       "<td>0.3506525</td>\n",
+       "<td>0.3373007</td>\n",
+       "<td>0.3952070</td>\n",
+       "<td>0.3827693</td>\n",
+       "<td>0.0957822</td>\n",
+       "<td>0.2159182</td>\n",
+       "<td>91.5726765</td>\n",
+       "<td>115.9142270</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>7</td>\n",
+       "<td>0.1499997</td>\n",
+       "<td>0.2767413</td>\n",
+       "<td>1.6645167</td>\n",
+       "<td>1.9942738</td>\n",
+       "<td>0.3046713</td>\n",
+       "<td>0.2933016</td>\n",
+       "<td>0.3650297</td>\n",
+       "<td>0.3529479</td>\n",
+       "<td>0.0832223</td>\n",
+       "<td>0.2991405</td>\n",
+       "<td>66.4516681</td>\n",
+       "<td>99.4273777</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>8</td>\n",
+       "<td>0.2000037</td>\n",
+       "<td>0.2513265</td>\n",
+       "<td>1.4697662</td>\n",
+       "<td>1.8631389</td>\n",
+       "<td>0.2690244</td>\n",
+       "<td>0.2634427</td>\n",
+       "<td>0.3410269</td>\n",
+       "<td>0.3305702</td>\n",
+       "<td>0.0734941</td>\n",
+       "<td>0.3726346</td>\n",
+       "<td>46.9766214</td>\n",
+       "<td>86.3138890</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>9</td>\n",
+       "<td>0.2999994</td>\n",
+       "<td>0.2143639</td>\n",
+       "<td>1.3110234</td>\n",
+       "<td>1.6791079</td>\n",
+       "<td>0.2399683</td>\n",
+       "<td>0.2318853</td>\n",
+       "<td>0.3073421</td>\n",
+       "<td>0.2976766</td>\n",
+       "<td>0.1310967</td>\n",
+       "<td>0.5037313</td>\n",
+       "<td>31.1023447</td>\n",
+       "<td>67.9107891</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>10</td>\n",
+       "<td>0.4000012</td>\n",
+       "<td>0.1862486</td>\n",
+       "<td>1.1193825</td>\n",
+       "<td>1.5391744</td>\n",
+       "<td>0.2048905</td>\n",
+       "<td>0.1998159</td>\n",
+       "<td>0.2817288</td>\n",
+       "<td>0.2732111</td>\n",
+       "<td>0.1119403</td>\n",
+       "<td>0.6156716</td>\n",
+       "<td>11.9382507</td>\n",
+       "<td>53.9174412</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>11</td>\n",
+       "<td>0.5000030</td>\n",
+       "<td>0.1634355</td>\n",
+       "<td>0.9581381</td>\n",
+       "<td>1.4229657</td>\n",
+       "<td>0.1753765</td>\n",
+       "<td>0.1745223</td>\n",
+       "<td>0.2604581</td>\n",
+       "<td>0.2534731</td>\n",
+       "<td>0.0958156</td>\n",
+       "<td>0.7114872</td>\n",
+       "<td>-4.1861878</td>\n",
+       "<td>42.2965736</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>12</td>\n",
+       "<td>0.5999988</td>\n",
+       "<td>0.1435154</td>\n",
+       "<td>0.7902789</td>\n",
+       "<td>1.3175222</td>\n",
+       "<td>0.1446518</td>\n",
+       "<td>0.1532284</td>\n",
+       "<td>0.2411578</td>\n",
+       "<td>0.2367663</td>\n",
+       "<td>0.0790245</td>\n",
+       "<td>0.7905117</td>\n",
+       "<td>-20.9721063</td>\n",
+       "<td>31.7522223</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>13</td>\n",
+       "<td>0.7000006</td>\n",
+       "<td>0.1247466</td>\n",
+       "<td>0.7362605</td>\n",
+       "<td>1.2344834</td>\n",
+       "<td>0.1347643</td>\n",
+       "<td>0.1340723</td>\n",
+       "<td>0.2259585</td>\n",
+       "<td>0.2220955</td>\n",
+       "<td>0.0736274</td>\n",
+       "<td>0.8641391</td>\n",
+       "<td>-26.3739482</td>\n",
+       "<td>23.4483390</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>14</td>\n",
+       "<td>0.7999963</td>\n",
+       "<td>0.1065095</td>\n",
+       "<td>0.5883780</td>\n",
+       "<td>1.1537233</td>\n",
+       "<td>0.1076961</td>\n",
+       "<td>0.1156723</td>\n",
+       "<td>0.2111762</td>\n",
+       "<td>0.2087931</td>\n",
+       "<td>0.0588353</td>\n",
+       "<td>0.9229744</td>\n",
+       "<td>-41.1622006</td>\n",
+       "<td>15.3723294</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>15</td>\n",
+       "<td>0.8999982</td>\n",
+       "<td>0.0856104</td>\n",
+       "<td>0.4694077</td>\n",
+       "<td>1.0776867</td>\n",
+       "<td>0.0859199</td>\n",
+       "<td>0.0965336</td>\n",
+       "<td>0.1972586</td>\n",
+       "<td>0.1963195</td>\n",
+       "<td>0.0469416</td>\n",
+       "<td>0.9699160</td>\n",
+       "<td>-53.0592276</td>\n",
+       "<td>7.7686685</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>16</td>\n",
+       "<td>1.0</td>\n",
+       "<td>0.0285809</td>\n",
+       "<td>0.3008340</td>\n",
+       "<td>1.0</td>\n",
+       "<td>0.0550643</td>\n",
+       "<td>0.0689028</td>\n",
+       "<td>0.1830389</td>\n",
+       "<td>0.1835776</td>\n",
+       "<td>0.0300840</td>\n",
+       "<td>1.0</td>\n",
+       "<td>-69.9165951</td>\n",
+       "<td>0.0</td></tr></table></div>"
+      ],
+      "text/plain": [
+       "    group    cumulative_data_fraction    lower_threshold    lift      cumulative_lift    response_rate    score      cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain      cumulative_gain\n",
+       "--  -------  --------------------------  -----------------  --------  -----------------  ---------------  ---------  --------------------------  ------------------  --------------  -------------------------  --------  -----------------\n",
+       "    1        0.0100008                   0.46601            2.90156   2.90156            0.531098         0.513382   0.531098                    0.513382            0.0290179       0.0290179                  190.156   190.156\n",
+       "    2        0.0200016                   0.427954           2.42185   2.6617             0.443293         0.445047   0.487195                    0.479214            0.0242204       0.0532383                  142.185   166.17\n",
+       "    3        0.0300024                   0.402783           2.35522   2.55954            0.431098         0.414499   0.468496                    0.457643            0.0235541       0.0767924                  135.522   155.954\n",
+       "    4        0.0400032                   0.383436           2.26528   2.48598            0.414634         0.392774   0.45503                     0.441426            0.0226546       0.099447                   126.528   148.598\n",
+       "    5        0.050004                    0.367274           2.06873   2.40253            0.378659         0.375459   0.439756                    0.428232            0.020689        0.120136                   106.873   140.253\n",
+       "    6        0.100002                    0.311832           1.91573   2.15914            0.350653         0.337301   0.395207                    0.382769            0.0957822       0.215918                   91.5727   115.914\n",
+       "    7        0.15                        0.276741           1.66452   1.99427            0.304671         0.293302   0.36503                     0.352948            0.0832223       0.29914                    66.4517   99.4274\n",
+       "    8        0.200004                    0.251327           1.46977   1.86314            0.269024         0.263443   0.341027                    0.33057             0.0734941       0.372635                   46.9766   86.3139\n",
+       "    9        0.299999                    0.214364           1.31102   1.67911            0.239968         0.231885   0.307342                    0.297677            0.131097        0.503731                   31.1023   67.9108\n",
+       "    10       0.400001                    0.186249           1.11938   1.53917            0.204891         0.199816   0.281729                    0.273211            0.11194         0.615672                   11.9383   53.9174\n",
+       "    11       0.500003                    0.163435           0.958138  1.42297            0.175377         0.174522   0.260458                    0.253473            0.0958156       0.711487                   -4.18619  42.2966\n",
+       "    12       0.599999                    0.143515           0.790279  1.31752            0.144652         0.153228   0.241158                    0.236766            0.0790245       0.790512                   -20.9721  31.7522\n",
+       "    13       0.700001                    0.124747           0.736261  1.23448            0.134764         0.134072   0.225958                    0.222095            0.0736274       0.864139                   -26.3739  23.4483\n",
+       "    14       0.799996                    0.106509           0.588378  1.15372            0.107696         0.115672   0.211176                    0.208793            0.0588353       0.922974                   -41.1622  15.3723\n",
+       "    15       0.899998                    0.0856104          0.469408  1.07769            0.0859199        0.0965336  0.197259                    0.19632             0.0469416       0.969916                   -53.0592  7.76867\n",
+       "    16       1                           0.0285809          0.300834  1                  0.0550643        0.0689028  0.183039                    0.183578            0.030084        1                          -69.9166  0"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Cross-Validation Metrics Summary: \n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
+       "<td><b>mean</b></td>\n",
+       "<td><b>sd</b></td>\n",
+       "<td><b>cv_1_valid</b></td>\n",
+       "<td><b>cv_2_valid</b></td>\n",
+       "<td><b>cv_3_valid</b></td>\n",
+       "<td><b>cv_4_valid</b></td>\n",
+       "<td><b>cv_5_valid</b></td></tr>\n",
+       "<tr><td>accuracy</td>\n",
+       "<td>0.6458257</td>\n",
+       "<td>0.0172431</td>\n",
+       "<td>0.6627234</td>\n",
+       "<td>0.6158302</td>\n",
+       "<td>0.6616764</td>\n",
+       "<td>0.6167637</td>\n",
+       "<td>0.6721346</td></tr>\n",
+       "<tr><td>auc</td>\n",
+       "<td>0.6824062</td>\n",
+       "<td>0.0018664</td>\n",
+       "<td>0.6840339</td>\n",
+       "<td>0.6812375</td>\n",
+       "<td>0.6860084</td>\n",
+       "<td>0.6781849</td>\n",
+       "<td>0.6825666</td></tr>\n",
+       "<tr><td>err</td>\n",
+       "<td>0.3541744</td>\n",
+       "<td>0.0172431</td>\n",
+       "<td>0.3372767</td>\n",
+       "<td>0.3841698</td>\n",
+       "<td>0.3383236</td>\n",
+       "<td>0.3832363</td>\n",
+       "<td>0.3278654</td></tr>\n",
+       "<tr><td>err_count</td>\n",
+       "<td>11616.0</td>\n",
+       "<td>565.55725</td>\n",
+       "<td>11062.0</td>\n",
+       "<td>12600.0</td>\n",
+       "<td>11096.0</td>\n",
+       "<td>12569.0</td>\n",
+       "<td>10753.0</td></tr>\n",
+       "<tr><td>f0point5</td>\n",
+       "<td>0.3188592</td>\n",
+       "<td>0.0062496</td>\n",
+       "<td>0.3199507</td>\n",
+       "<td>0.3087547</td>\n",
+       "<td>0.3263041</td>\n",
+       "<td>0.3089049</td>\n",
+       "<td>0.3303814</td></tr>\n",
+       "<tr><td>f1</td>\n",
+       "<td>0.3885291</td>\n",
+       "<td>0.0025436</td>\n",
+       "<td>0.3862628</td>\n",
+       "<td>0.3857853</td>\n",
+       "<td>0.3930642</td>\n",
+       "<td>0.3848074</td>\n",
+       "<td>0.3927260</td></tr>\n",
+       "<tr><td>f2</td>\n",
+       "<td>0.4979363</td>\n",
+       "<td>0.0085386</td>\n",
+       "<td>0.4872484</td>\n",
+       "<td>0.5140296</td>\n",
+       "<td>0.4941685</td>\n",
+       "<td>0.5101617</td>\n",
+       "<td>0.4840731</td></tr>\n",
+       "<tr><td>lift_top_group</td>\n",
+       "<td>2.9082687</td>\n",
+       "<td>0.1208559</td>\n",
+       "<td>2.9325216</td>\n",
+       "<td>3.053886</td>\n",
+       "<td>2.8383858</td>\n",
+       "<td>2.619124</td>\n",
+       "<td>3.0974264</td></tr>\n",
+       "<tr><td>logloss</td>\n",
+       "<td>0.4441041</td>\n",
+       "<td>0.0020101</td>\n",
+       "<td>0.4391639</td>\n",
+       "<td>0.4438323</td>\n",
+       "<td>0.4437065</td>\n",
+       "<td>0.447041</td>\n",
+       "<td>0.4467766</td></tr>\n",
+       "<tr><td>max_per_class_error</td>\n",
+       "<td>0.4051554</td>\n",
+       "<td>0.0090834</td>\n",
+       "<td>0.4099000</td>\n",
+       "<td>0.3941282</td>\n",
+       "<td>0.4035524</td>\n",
+       "<td>0.3911078</td>\n",
+       "<td>0.4270885</td></tr>\n",
+       "<tr><td>mcc</td>\n",
+       "<td>0.2119794</td>\n",
+       "<td>0.0040928</td>\n",
+       "<td>0.2138271</td>\n",
+       "<td>0.2071293</td>\n",
+       "<td>0.2183418</td>\n",
+       "<td>0.2034273</td>\n",
+       "<td>0.2171714</td></tr>\n",
+       "<tr><td>mean_per_class_accuracy</td>\n",
+       "<td>0.6335962</td>\n",
+       "<td>0.0014017</td>\n",
+       "<td>0.6343749</td>\n",
+       "<td>0.6331261</td>\n",
+       "<td>0.6364003</td>\n",
+       "<td>0.6302916</td>\n",
+       "<td>0.6337882</td></tr>\n",
+       "<tr><td>mean_per_class_error</td>\n",
+       "<td>0.3664038</td>\n",
+       "<td>0.0014017</td>\n",
+       "<td>0.3656251</td>\n",
+       "<td>0.3668738</td>\n",
+       "<td>0.3635997</td>\n",
+       "<td>0.3697084</td>\n",
+       "<td>0.3662119</td></tr>\n",
+       "<tr><td>mse</td>\n",
+       "<td>0.1396638</td>\n",
+       "<td>0.0008432</td>\n",
+       "<td>0.1375632</td>\n",
+       "<td>0.1394970</td>\n",
+       "<td>0.1396217</td>\n",
+       "<td>0.1408368</td>\n",
+       "<td>0.1408002</td></tr>\n",
+       "<tr><td>precision</td>\n",
+       "<td>0.2848917</td>\n",
+       "<td>0.0074842</td>\n",
+       "<td>0.2870928</td>\n",
+       "<td>0.2724831</td>\n",
+       "<td>0.2931147</td>\n",
+       "<td>0.2730051</td>\n",
+       "<td>0.2987627</td></tr>\n",
+       "<tr><td>r2</td>\n",
+       "<td>0.0660026</td>\n",
+       "<td>0.0016984</td>\n",
+       "<td>0.0674278</td>\n",
+       "<td>0.0657655</td>\n",
+       "<td>0.0688078</td>\n",
+       "<td>0.0616699</td>\n",
+       "<td>0.0663418</td></tr>\n",
+       "<tr><td>recall</td>\n",
+       "<td>0.6143061</td>\n",
+       "<td>0.0247767</td>\n",
+       "<td>0.5901</td>\n",
+       "<td>0.6603805</td>\n",
+       "<td>0.5964475</td>\n",
+       "<td>0.6516910</td>\n",
+       "<td>0.5729115</td></tr>\n",
+       "<tr><td>rmse</td>\n",
+       "<td>0.3737128</td>\n",
+       "<td>0.0011299</td>\n",
+       "<td>0.3708951</td>\n",
+       "<td>0.3734930</td>\n",
+       "<td>0.3736599</td>\n",
+       "<td>0.3752822</td>\n",
+       "<td>0.3752335</td></tr>\n",
+       "<tr><td>specificity</td>\n",
+       "<td>0.6528863</td>\n",
+       "<td>0.0266566</td>\n",
+       "<td>0.6786498</td>\n",
+       "<td>0.6058718</td>\n",
+       "<td>0.6763530</td>\n",
+       "<td>0.6088922</td>\n",
+       "<td>0.6946648</td></tr></table></div>"
+      ],
+      "text/plain": [
+       "                         mean       sd           cv_1_valid    cv_2_valid    cv_3_valid    cv_4_valid    cv_5_valid\n",
+       "-----------------------  ---------  -----------  ------------  ------------  ------------  ------------  ------------\n",
+       "accuracy                 0.645826   0.0172431    0.662723      0.61583       0.661676      0.616764      0.672135\n",
+       "auc                      0.682406   0.00186645   0.684034      0.681237      0.686008      0.678185      0.682567\n",
+       "err                      0.354174   0.0172431    0.337277      0.38417       0.338324      0.383236      0.327865\n",
+       "err_count                11616      565.557      11062         12600         11096         12569         10753\n",
+       "f0point5                 0.318859   0.00624955   0.319951      0.308755      0.326304      0.308905      0.330381\n",
+       "f1                       0.388529   0.00254357   0.386263      0.385785      0.393064      0.384807      0.392726\n",
+       "f2                       0.497936   0.00853857   0.487248      0.51403       0.494168      0.510162      0.484073\n",
+       "lift_top_group           2.90827    0.120856     2.93252       3.05389       2.83839       2.61912       3.09743\n",
+       "logloss                  0.444104   0.00201011   0.439164      0.443832      0.443707      0.447041      0.446777\n",
+       "max_per_class_error      0.405155   0.00908337   0.4099        0.394128      0.403552      0.391108      0.427088\n",
+       "mcc                      0.211979   0.00409284   0.213827      0.207129      0.218342      0.203427      0.217171\n",
+       "mean_per_class_accuracy  0.633596   0.00140169   0.634375      0.633126      0.6364        0.630292      0.633788\n",
+       "mean_per_class_error     0.366404   0.00140169   0.365625      0.366874      0.3636        0.369708      0.366212\n",
+       "mse                      0.139664   0.000843169  0.137563      0.139497      0.139622      0.140837      0.1408\n",
+       "precision                0.284892   0.00748419   0.287093      0.272483      0.293115      0.273005      0.298763\n",
+       "r2                       0.0660026  0.00169835   0.0674278     0.0657655     0.0688078     0.0616699     0.0663418\n",
+       "recall                   0.614306   0.0247767    0.5901        0.660381      0.596448      0.651691      0.572912\n",
+       "rmse                     0.373713   0.00112987   0.370895      0.373493      0.37366       0.375282      0.375234\n",
+       "specificity              0.652886   0.0266566    0.67865       0.605872      0.676353      0.608892      0.694665"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "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>number_of_trees</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_pr_auc</b></td>\n",
+       "<td><b>training_lift</b></td>\n",
+       "<td><b>training_classification_error</b></td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>2019-02-04 10:56:53</td>\n",
+       "<td>49.588 sec</td>\n",
+       "<td>0.0</td>\n",
+       "<td>0.5</td>\n",
+       "<td>0.6931472</td>\n",
+       "<td>0.5</td>\n",
+       "<td>0.0</td>\n",
+       "<td>1.0</td>\n",
+       "<td>0.8169611</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>2019-02-04 10:56:54</td>\n",
+       "<td>50.011 sec</td>\n",
+       "<td>5.0</td>\n",
+       "<td>0.4548462</td>\n",
+       "<td>0.6062185</td>\n",
+       "<td>0.6713416</td>\n",
+       "<td>0.3147873</td>\n",
+       "<td>2.6990429</td>\n",
+       "<td>0.3537110</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>2019-02-04 10:56:54</td>\n",
+       "<td>50.246 sec</td>\n",
+       "<td>10.0</td>\n",
+       "<td>0.4253391</td>\n",
+       "<td>0.5515536</td>\n",
+       "<td>0.6736622</td>\n",
+       "<td>0.3180533</td>\n",
+       "<td>2.8165560</td>\n",
+       "<td>0.3625348</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>2019-02-04 10:56:54</td>\n",
+       "<td>50.473 sec</td>\n",
+       "<td>15.0</td>\n",
+       "<td>0.4063605</td>\n",
+       "<td>0.5160345</td>\n",
+       "<td>0.6746698</td>\n",
+       "<td>0.3205805</td>\n",
+       "<td>2.9115496</td>\n",
+       "<td>0.3339228</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>2019-02-04 10:56:54</td>\n",
+       "<td>50.712 sec</td>\n",
+       "<td>20.0</td>\n",
+       "<td>0.3942844</td>\n",
+       "<td>0.4925073</td>\n",
+       "<td>0.6759822</td>\n",
+       "<td>0.3222165</td>\n",
+       "<td>2.9640002</td>\n",
+       "<td>0.3314775</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></tr>\n",
+       "<tr><td></td>\n",
+       "<td>2019-02-04 10:56:59</td>\n",
+       "<td>55.572 sec</td>\n",
+       "<td>105.0</td>\n",
+       "<td>0.3714918</td>\n",
+       "<td>0.4393966</td>\n",
+       "<td>0.6963451</td>\n",
+       "<td>0.3466050</td>\n",
+       "<td>3.2246911</td>\n",
+       "<td>0.3348924</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>2019-02-04 10:57:00</td>\n",
+       "<td>55.902 sec</td>\n",
+       "<td>110.0</td>\n",
+       "<td>0.3713399</td>\n",
+       "<td>0.4390259</td>\n",
+       "<td>0.6971581</td>\n",
+       "<td>0.3475160</td>\n",
+       "<td>3.2546727</td>\n",
+       "<td>0.3398989</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>2019-02-04 10:57:00</td>\n",
+       "<td>56.226 sec</td>\n",
+       "<td>115.0</td>\n",
+       "<td>0.3711854</td>\n",
+       "<td>0.4386487</td>\n",
+       "<td>0.6979635</td>\n",
+       "<td>0.3485002</td>\n",
+       "<td>3.2613353</td>\n",
+       "<td>0.3370999</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>2019-02-04 10:57:00</td>\n",
+       "<td>56.567 sec</td>\n",
+       "<td>120.0</td>\n",
+       "<td>0.3710452</td>\n",
+       "<td>0.4382927</td>\n",
+       "<td>0.6986839</td>\n",
+       "<td>0.3494183</td>\n",
+       "<td>3.2580040</td>\n",
+       "<td>0.3372523</td></tr>\n",
+       "<tr><td></td>\n",
+       "<td>2019-02-04 10:57:01</td>\n",
+       "<td>56.889 sec</td>\n",
+       "<td>124.0</td>\n",
+       "<td>0.3709253</td>\n",
+       "<td>0.4380145</td>\n",
+       "<td>0.6993402</td>\n",
+       "<td>0.3503559</td>\n",
+       "<td>3.2779917</td>\n",
+       "<td>0.3360632</td></tr></table></div>"
+      ],
+      "text/plain": [
+       "     timestamp            duration    number_of_trees    training_rmse        training_logloss     training_auc        training_pr_auc      training_lift       training_classification_error\n",
+       "---  -------------------  ----------  -----------------  -------------------  -------------------  ------------------  -------------------  ------------------  -------------------------------\n",
+       "     2019-02-04 10:56:53  49.588 sec  0.0                0.5                  0.6931471805599067   0.5                 0.0                  1.0                 0.8169611005750456\n",
+       "     2019-02-04 10:56:54  50.011 sec  5.0                0.4548461543643131   0.6062184625129501   0.6713416478216915  0.3147872798955476   2.6990429482385516  0.35371096489355863\n",
+       "     2019-02-04 10:56:54  50.246 sec  10.0               0.4253390773044219   0.551553580461646    0.6736622272143787  0.31805325502833104  2.816555972098245   0.36253483507839035\n",
+       "     2019-02-04 10:56:54  50.473 sec  15.0               0.4063604679546381   0.516034549835951    0.6746698495419139  0.32058050237542945  2.9115495719356184  0.33392281095452686\n",
+       "     2019-02-04 10:56:54  50.712 sec  20.0               0.3942843595588244   0.492507333499956    0.6759822181119092  0.3222164598122689   2.9640002245865795  0.33147749516729985\n",
+       "---  ---                  ---         ---                ---                  ---                  ---                 ---                  ---                 ---\n",
+       "     2019-02-04 10:56:59  55.572 sec  105.0              0.37149178554534024  0.4393965865355424   0.6963451012301393  0.3466049564194871   3.224691059077435   0.3348924000073176\n",
+       "     2019-02-04 10:57:00  55.902 sec  110.0              0.3713399179536629   0.4390259162984699   0.697158119785264   0.3475159669058915   3.254672690825056   0.33989889442455806\n",
+       "     2019-02-04 10:57:00  56.226 sec  115.0              0.3711853856087302   0.4386487453085832   0.6979634852922376  0.3485002230849438   3.2613352756578604  0.3370998920646149\n",
+       "     2019-02-04 10:57:00  56.567 sec  120.0              0.37104521842651933  0.4382927496245225   0.6986838961208134  0.3494182794020932   3.258003983241458   0.33725234317354424\n",
+       "     2019-02-04 10:57:01  56.889 sec  124.0              0.3709253222355326   0.43801446987930465  0.6993402202868698  0.3503558980432989   3.277991737739872   0.3360632245238952"
+      ]
+     },
+     "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>annual_inc</td>\n",
+       "<td>20075.4160156</td>\n",
+       "<td>1.0</td>\n",
+       "<td>0.1940653</td></tr>\n",
+       "<tr><td>term.36months</td>\n",
+       "<td>18496.7636719</td>\n",
+       "<td>0.9213639</td>\n",
+       "<td>0.1788047</td></tr>\n",
+       "<tr><td>revol_util</td>\n",
+       "<td>16078.5566406</td>\n",
+       "<td>0.8009078</td>\n",
+       "<td>0.1554284</td></tr>\n",
+       "<tr><td>dti</td>\n",
+       "<td>11124.3505859</td>\n",
+       "<td>0.5541280</td>\n",
+       "<td>0.1075370</td></tr>\n",
+       "<tr><td>loan_amnt</td>\n",
+       "<td>7898.3613281</td>\n",
+       "<td>0.3934345</td>\n",
+       "<td>0.0763520</td></tr>\n",
+       "<tr><td>---</td>\n",
+       "<td>---</td>\n",
+       "<td>---</td>\n",
+       "<td>---</td></tr>\n",
+       "<tr><td>addr_state.MT</td>\n",
+       "<td>6.9731202</td>\n",
+       "<td>0.0003473</td>\n",
+       "<td>0.0000674</td></tr>\n",
+       "<tr><td>addr_state.AL</td>\n",
+       "<td>6.9634399</td>\n",
+       "<td>0.0003469</td>\n",
+       "<td>0.0000673</td></tr>\n",
+       "<tr><td>purpose.educational</td>\n",
+       "<td>6.9432998</td>\n",
+       "<td>0.0003459</td>\n",
+       "<td>0.0000671</td></tr>\n",
+       "<tr><td>addr_state.SD</td>\n",
+       "<td>6.9125500</td>\n",
+       "<td>0.0003443</td>\n",
+       "<td>0.0000668</td></tr>\n",
+       "<tr><td>addr_state.NH</td>\n",
+       "<td>5.6122599</td>\n",
+       "<td>0.0002796</td>\n",
+       "<td>0.0000543</td></tr></table></div>"
+      ],
+      "text/plain": [
+       "variable             relative_importance    scaled_importance       percentage\n",
+       "-------------------  ---------------------  ----------------------  ----------------------\n",
+       "annual_inc           20075.416015625        1.0                     0.19406527466547135\n",
+       "term.36months        18496.763671875        0.9213639038652394      0.17880473907045863\n",
+       "revol_util           16078.556640625        0.8009077683924865      0.15542838605479758\n",
+       "dti                  11124.3505859375       0.5541280229151541      0.10753700696686397\n",
+       "loan_amnt            7898.361328125         0.3934345032739339      0.07635197494072928\n",
+       "---                  ---                    ---                     ---\n",
+       "addr_state.MT        6.973120212554932      0.0003473462371652795   6.740784291949791e-05\n",
+       "addr_state.AL        6.96343994140625       0.0003468640418702407   6.731426555712382e-05\n",
+       "purpose.educational  6.943299770355225      0.0003458608162815231   6.711957430769791e-05\n",
+       "addr_state.SD        6.91254997253418       0.00034432910217920454  6.682232178972248e-05\n",
+       "addr_state.NH        5.612259864807129      0.00027955883257607325  5.4252661629034175e-05"
+      ]
+     },
+     "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": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "best_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "best_model_trans.varimp_plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AUC (orig): train = 0.6993, xval = 0.6824\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"AUC (orig): train = {:.4f}, xval = {:.4f}\" \\\n",
+    "      .format(best_model.auc(train = True),\n",
+    "              best_model.auc(xval = True)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AUC (trans): train = 0.7009, xval = 0.6842\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"AUC (trans): train = {:.4f}, xval = {:.4f}\" \\\n",
+    "      .format(best_model_trans.auc(train = True),\n",
+    "              best_model_trans.auc(xval = True)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Logloss (orig): train = 0.4380, xval = 0.4441\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Logloss (orig): train = {:.4f}, xval = {:.4f}\" \\\n",
+    "      .format(best_model.logloss(train = True),\n",
+    "              best_model.logloss(xval = True)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Logloss (trans): train = 0.4372, xval = 0.4434\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Logloss (trans): train = {:.4f}, xval = {:.4f}\" \\\n",
+    "      .format(best_model_trans.logloss(train = True),\n",
+    "              best_model_trans.logloss(xval = True)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Shutdown H2O Cluster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "H2O session _sid_980c closed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "h2o.cluster().shutdown()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
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+}