diff --git a/assignments/assignment_1/assign_1_template.ipynb b/assignments/assignment_1/assign_1_template.ipynb
index ec77847..a114942 100644
--- a/assignments/assignment_1/assign_1_template.ipynb
+++ b/assignments/assignment_1/assign_1_template.ipynb
@@ -42,10 +42,88 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "4baedf75",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "scrolled": true
+   },
+   "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: openjdk version \"11.0.11\" 2021-04-20; OpenJDK Runtime Environment (build 11.0.11+9-Ubuntu-0ubuntu2.18.04); OpenJDK 64-Bit Server VM (build 11.0.11+9-Ubuntu-0ubuntu2.18.04, mixed mode, sharing)\n",
+      "  Starting server from /home/patrickh/Workspace/GWU_rml/assignments/assign_env/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar\n",
+      "  Ice root: /tmp/tmp7trwg68w\n",
+      "  JVM stdout: /tmp/tmp7trwg68w/h2o_patrickh_started_from_python.out\n",
+      "  JVM stderr: /tmp/tmp7trwg68w/h2o_patrickh_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>America/New_York</td></tr>\n",
+       "<tr><td>H2O_data_parsing_timezone:</td>\n",
+       "<td>UTC</td></tr>\n",
+       "<tr><td>H2O_cluster_version:</td>\n",
+       "<td>3.32.1.3</td></tr>\n",
+       "<tr><td>H2O_cluster_version_age:</td>\n",
+       "<td>3 days </td></tr>\n",
+       "<tr><td>H2O_cluster_name:</td>\n",
+       "<td>H2O_from_python_patrickh_7fgogb</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>6 Gb</td></tr>\n",
+       "<tr><td>H2O_cluster_total_cores:</td>\n",
+       "<td>24</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>{\"http\": null, \"https\": null}</td></tr>\n",
+       "<tr><td>H2O_internal_security:</td>\n",
+       "<td>False</td></tr>\n",
+       "<tr><td>H2O_API_Extensions:</td>\n",
+       "<td>Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4</td></tr>\n",
+       "<tr><td>Python_version:</td>\n",
+       "<td>3.6.9 final</td></tr></table></div>"
+      ],
+      "text/plain": [
+       "--------------------------  ------------------------------------------------------------------\n",
+       "H2O_cluster_uptime:         01 secs\n",
+       "H2O_cluster_timezone:       America/New_York\n",
+       "H2O_data_parsing_timezone:  UTC\n",
+       "H2O_cluster_version:        3.32.1.3\n",
+       "H2O_cluster_version_age:    3 days\n",
+       "H2O_cluster_name:           H2O_from_python_patrickh_7fgogb\n",
+       "H2O_cluster_total_nodes:    1\n",
+       "H2O_cluster_free_memory:    6 Gb\n",
+       "H2O_cluster_total_cores:    24\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:       {\"http\": null, \"https\": null}\n",
+       "H2O_internal_security:      False\n",
+       "H2O_API_Extensions:         Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4\n",
+       "Python_version:             3.6.9 final\n",
+       "--------------------------  ------------------------------------------------------------------"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import datetime                                               # for timestamp \n",
     "import h2o                                                    # base h2o package for python\n",
@@ -83,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "110ae3e4",
    "metadata": {},
    "outputs": [],
@@ -101,13 +179,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "36898e3f",
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = pd.read_csv('data/hmda_train_preprocessed.csv')\n",
-    "test = pd.read_csv('data/hmda_test_preprocessed.csv')"
+    "data = pd.read_csv('../data/hmda_train_preprocessed.csv')\n",
+    "test = pd.read_csv('../data/hmda_test_preprocessed.csv')"
    ]
   },
   {
@@ -120,7 +198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "374364cd",
    "metadata": {},
    "outputs": [],
@@ -148,10 +226,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "cbbaaf84",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x1080 with 12 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "_ = data[x_names + [y_name]].hist(bins=50, figsize=(15, 15))"
    ]
@@ -166,10 +257,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "e1a420a5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "corr = data[x_names + [y_name]].corr()\n",
     "_ = sns.heatmap(corr, \n",
@@ -195,10 +299,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "d82faabc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train data rows = 112253, columns = 23\n",
+      "Validation data rows = 48085, columns = 23\n"
+     ]
+    }
+   ],
    "source": [
     "np.random.seed(SEED) # preserve exact reproducibility for this cell\n",
     "\n",
@@ -235,7 +348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "e65a8102",
    "metadata": {},
    "outputs": [],
@@ -290,10 +403,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "46a30359",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Elastic net GLM training completed in 11.19 s.\n"
+     ]
+    }
+   ],
    "source": [
     "# start local timer\n",
     "glm_tic = time.time()\n",
@@ -320,10 +441,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "4a08a632",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation AUC: 0.7538.\n"
+     ]
+    }
+   ],
    "source": [
     "print('Validation AUC: %.4f.' % best_glm.auc(valid=True))"
    ]
@@ -338,7 +467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "a5a3699b",
    "metadata": {},
    "outputs": [],
@@ -368,7 +497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "02f6deef",
    "metadata": {},
    "outputs": [],
@@ -391,7 +520,7 @@
     "\n",
     "    \"\"\"\n",
     "\n",
-    "    # cartesian product of _cv_params\n",
+    "    # cartesian product of gs_params\n",
     "    keys, values = zip(*gs_params.items())\n",
     "    experiments = [dict(zip(keys, v)) for v in itertools.product(*values)]\n",
     "\n",
@@ -455,10 +584,184 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "ac9cb630",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Grid search run 1/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.9, 'colsample_bylevel': 0.9, 'eta': 0.5, 'max_depth': 7, 'reg_alpha': 0.0005, 'reg_lambda': 0.005, 'subsample': 0.3, 'min_child_weight': 5, 'gamma': 0.0}\n",
+      "Grid search new best score discovered at iteration 1/50: 0.7915.\n",
+      "---------- ----------\n",
+      "Grid search run 2/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.3, 'colsample_bylevel': 0.3, 'eta': 0.05, 'max_depth': 5, 'reg_alpha': 0.0005, 'reg_lambda': 0.0005, 'subsample': 0.5, 'min_child_weight': 1, 'gamma': 0.2, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 3/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.3, 'colsample_bylevel': 0.3, 'eta': 0.5, 'max_depth': 5, 'reg_alpha': 0.005, 'reg_lambda': 0.05, 'subsample': 0.3, 'min_child_weight': 10, 'gamma': 0.4, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 4/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.5, 'colsample_bylevel': 0.5, 'eta': 0.5, 'max_depth': 3, 'reg_alpha': 0.05, 'reg_lambda': 0.005, 'subsample': 0.9, 'min_child_weight': 1, 'gamma': 0.0, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 5/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.3, 'colsample_bylevel': 0.9, 'eta': 0.05, 'max_depth': 5, 'reg_alpha': 0.005, 'reg_lambda': 0.0005, 'subsample': 0.3, 'min_child_weight': 10, 'gamma': 0.0, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 6/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.7, 'colsample_bylevel': 0.5, 'eta': 0.5, 'max_depth': 5, 'reg_alpha': 0.005, 'reg_lambda': 0.05, 'subsample': 0.9, 'min_child_weight': 5, 'gamma': 0.3, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 7/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.5, 'colsample_bylevel': 0.5, 'eta': 0.5, 'max_depth': 3, 'reg_alpha': 0.005, 'reg_lambda': 0.0005, 'subsample': 0.9, 'min_child_weight': 1, 'gamma': 0.1, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 8/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.7, 'colsample_bylevel': 0.7, 'eta': 0.5, 'max_depth': 5, 'reg_alpha': 0.005, 'reg_lambda': 0.0005, 'subsample': 0.7, 'min_child_weight': 1, 'gamma': 0.3, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 9/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.7, 'colsample_bylevel': 0.3, 'eta': 0.005, 'max_depth': 7, 'reg_alpha': 0.005, 'reg_lambda': 0.005, 'subsample': 0.9, 'min_child_weight': 5, 'gamma': 0.1, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 10/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.3, 'colsample_bylevel': 0.5, 'eta': 0.05, 'max_depth': 3, 'reg_alpha': 0.005, 'reg_lambda': 0.05, 'subsample': 0.5, 'min_child_weight': 1, 'gamma': 0.3, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 11/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.5, 'colsample_bylevel': 0.3, 'eta': 0.05, 'max_depth': 7, 'reg_alpha': 0.05, 'reg_lambda': 0.05, 'subsample': 0.3, 'min_child_weight': 10, 'gamma': 0.3, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 12/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.5, 'colsample_bylevel': 0.3, 'eta': 0.5, 'max_depth': 5, 'reg_alpha': 0.0005, 'reg_lambda': 0.005, 'subsample': 0.3, 'min_child_weight': 5, 'gamma': 0.2, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 13/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.5, 'colsample_bylevel': 0.3, 'eta': 0.05, 'max_depth': 5, 'reg_alpha': 0.005, 'reg_lambda': 0.005, 'subsample': 0.7, 'min_child_weight': 5, 'gamma': 0.3, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 14/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.3, 'colsample_bylevel': 0.9, 'eta': 0.005, 'max_depth': 5, 'reg_alpha': 0.05, 'reg_lambda': 0.005, 'subsample': 0.5, 'min_child_weight': 10, 'gamma': 0.3, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 15/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.7, 'colsample_bylevel': 0.3, 'eta': 0.05, 'max_depth': 7, 'reg_alpha': 0.005, 'reg_lambda': 0.005, 'subsample': 0.5, 'min_child_weight': 5, 'gamma': 0.2, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 16/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.3, 'colsample_bylevel': 0.7, 'eta': 0.5, 'max_depth': 3, 'reg_alpha': 0.0005, 'reg_lambda': 0.0005, 'subsample': 0.7, 'min_child_weight': 10, 'gamma': 0.1, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 17/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.3, 'colsample_bylevel': 0.5, 'eta': 0.005, 'max_depth': 5, 'reg_alpha': 0.05, 'reg_lambda': 0.005, 'subsample': 0.5, 'min_child_weight': 5, 'gamma': 0.2, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 18/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.5, 'colsample_bylevel': 0.7, 'eta': 0.05, 'max_depth': 3, 'reg_alpha': 0.005, 'reg_lambda': 0.0005, 'subsample': 0.5, 'min_child_weight': 5, 'gamma': 0.3, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 19/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.5, 'colsample_bylevel': 0.9, 'eta': 0.05, 'max_depth': 7, 'reg_alpha': 0.005, 'reg_lambda': 0.0005, 'subsample': 0.3, 'min_child_weight': 5, 'gamma': 0.2, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 20/50:\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.9, 'colsample_bylevel': 0.3, 'eta': 0.05, 'max_depth': 3, 'reg_alpha': 0.05, 'reg_lambda': 0.0005, 'subsample': 0.7, 'min_child_weight': 10, 'gamma': 0.4, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 21/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.3, 'colsample_bylevel': 0.3, 'eta': 0.5, 'max_depth': 3, 'reg_alpha': 0.005, 'reg_lambda': 0.005, 'subsample': 0.7, 'min_child_weight': 1, 'gamma': 0.1, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 22/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.9, 'colsample_bylevel': 0.9, 'eta': 0.05, 'max_depth': 3, 'reg_alpha': 0.05, 'reg_lambda': 0.05, 'subsample': 0.7, 'min_child_weight': 5, 'gamma': 0.4, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "Grid search new best score discovered at iteration 22/50: 0.7915.\n",
+      "---------- ----------\n",
+      "Grid search run 23/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.9, 'colsample_bylevel': 0.3, 'eta': 0.5, 'max_depth': 5, 'reg_alpha': 0.0005, 'reg_lambda': 0.0005, 'subsample': 0.7, 'min_child_weight': 10, 'gamma': 0.1, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 24/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.7, 'colsample_bylevel': 0.7, 'eta': 0.05, 'max_depth': 5, 'reg_alpha': 0.005, 'reg_lambda': 0.005, 'subsample': 0.7, 'min_child_weight': 5, 'gamma': 0.0, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 25/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.9, 'colsample_bylevel': 0.7, 'eta': 0.005, 'max_depth': 3, 'reg_alpha': 0.0005, 'reg_lambda': 0.05, 'subsample': 0.3, 'min_child_weight': 1, 'gamma': 0.0, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 26/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.5, 'colsample_bylevel': 0.9, 'eta': 0.05, 'max_depth': 7, 'reg_alpha': 0.05, 'reg_lambda': 0.05, 'subsample': 0.9, 'min_child_weight': 5, 'gamma': 0.1, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 27/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.9, 'colsample_bylevel': 0.7, 'eta': 0.005, 'max_depth': 3, 'reg_alpha': 0.005, 'reg_lambda': 0.05, 'subsample': 0.3, 'min_child_weight': 5, 'gamma': 0.3, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 28/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.5, 'colsample_bylevel': 0.9, 'eta': 0.05, 'max_depth': 3, 'reg_alpha': 0.005, 'reg_lambda': 0.05, 'subsample': 0.7, 'min_child_weight': 10, 'gamma': 0.3, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 29/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.7, 'colsample_bylevel': 0.7, 'eta': 0.005, 'max_depth': 7, 'reg_alpha': 0.0005, 'reg_lambda': 0.005, 'subsample': 0.7, 'min_child_weight': 10, 'gamma': 0.3, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 30/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.9, 'colsample_bylevel': 0.3, 'eta': 0.005, 'max_depth': 5, 'reg_alpha': 0.005, 'reg_lambda': 0.05, 'subsample': 0.9, 'min_child_weight': 5, 'gamma': 0.1, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 31/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.9, 'colsample_bylevel': 0.3, 'eta': 0.05, 'max_depth': 7, 'reg_alpha': 0.005, 'reg_lambda': 0.05, 'subsample': 0.3, 'min_child_weight': 5, 'gamma': 0.1, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 32/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.9, 'colsample_bylevel': 0.5, 'eta': 0.5, 'max_depth': 7, 'reg_alpha': 0.05, 'reg_lambda': 0.005, 'subsample': 0.7, 'min_child_weight': 5, 'gamma': 0.0, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 33/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.9, 'colsample_bylevel': 0.7, 'eta': 0.05, 'max_depth': 3, 'reg_alpha': 0.0005, 'reg_lambda': 0.005, 'subsample': 0.5, 'min_child_weight': 5, 'gamma': 0.0, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 34/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.5, 'colsample_bylevel': 0.3, 'eta': 0.5, 'max_depth': 5, 'reg_alpha': 0.05, 'reg_lambda': 0.0005, 'subsample': 0.7, 'min_child_weight': 1, 'gamma': 0.1, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 35/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.7, 'colsample_bylevel': 0.3, 'eta': 0.5, 'max_depth': 5, 'reg_alpha': 0.05, 'reg_lambda': 0.005, 'subsample': 0.3, 'min_child_weight': 10, 'gamma': 0.1, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 36/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.9, 'colsample_bylevel': 0.9, 'eta': 0.5, 'max_depth': 7, 'reg_alpha': 0.0005, 'reg_lambda': 0.005, 'subsample': 0.7, 'min_child_weight': 10, 'gamma': 0.4, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "Grid search new best score discovered at iteration 36/50: 0.7919.\n",
+      "---------- ----------\n",
+      "Grid search run 37/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.9, 'colsample_bylevel': 0.9, 'eta': 0.05, 'max_depth': 5, 'reg_alpha': 0.05, 'reg_lambda': 0.005, 'subsample': 0.7, 'min_child_weight': 5, 'gamma': 0.0, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 38/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.7, 'colsample_bylevel': 0.3, 'eta': 0.5, 'max_depth': 5, 'reg_alpha': 0.05, 'reg_lambda': 0.05, 'subsample': 0.5, 'min_child_weight': 5, 'gamma': 0.3, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 39/50:\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.9, 'colsample_bylevel': 0.5, 'eta': 0.05, 'max_depth': 3, 'reg_alpha': 0.0005, 'reg_lambda': 0.0005, 'subsample': 0.3, 'min_child_weight': 5, 'gamma': 0.1, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 40/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.9, 'colsample_bylevel': 0.7, 'eta': 0.5, 'max_depth': 3, 'reg_alpha': 0.0005, 'reg_lambda': 0.05, 'subsample': 0.3, 'min_child_weight': 1, 'gamma': 0.3, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 41/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.5, 'colsample_bylevel': 0.9, 'eta': 0.5, 'max_depth': 7, 'reg_alpha': 0.0005, 'reg_lambda': 0.05, 'subsample': 0.3, 'min_child_weight': 10, 'gamma': 0.4, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 42/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.5, 'colsample_bylevel': 0.9, 'eta': 0.05, 'max_depth': 3, 'reg_alpha': 0.005, 'reg_lambda': 0.005, 'subsample': 0.7, 'min_child_weight': 5, 'gamma': 0.1, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 43/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.7, 'colsample_bylevel': 0.3, 'eta': 0.5, 'max_depth': 7, 'reg_alpha': 0.05, 'reg_lambda': 0.0005, 'subsample': 0.5, 'min_child_weight': 5, 'gamma': 0.2, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 44/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.7, 'colsample_bylevel': 0.3, 'eta': 0.005, 'max_depth': 3, 'reg_alpha': 0.005, 'reg_lambda': 0.05, 'subsample': 0.5, 'min_child_weight': 10, 'gamma': 0.4, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 45/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.7, 'colsample_bylevel': 0.7, 'eta': 0.05, 'max_depth': 3, 'reg_alpha': 0.0005, 'reg_lambda': 0.005, 'subsample': 0.7, 'min_child_weight': 10, 'gamma': 0.2, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 46/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.5, 'colsample_bylevel': 0.9, 'eta': 0.5, 'max_depth': 3, 'reg_alpha': 0.005, 'reg_lambda': 0.005, 'subsample': 0.9, 'min_child_weight': 5, 'gamma': 0.4, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 47/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.9, 'colsample_bylevel': 0.9, 'eta': 0.005, 'max_depth': 7, 'reg_alpha': 0.005, 'reg_lambda': 0.05, 'subsample': 0.7, 'min_child_weight': 10, 'gamma': 0.2, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 48/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.9, 'colsample_bylevel': 0.7, 'eta': 0.5, 'max_depth': 7, 'reg_alpha': 0.05, 'reg_lambda': 0.0005, 'subsample': 0.7, 'min_child_weight': 10, 'gamma': 0.2, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "Grid search new best score discovered at iteration 48/50: 0.7919.\n",
+      "---------- ----------\n",
+      "Grid search run 49/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.3, 'colsample_bylevel': 0.3, 'eta': 0.005, 'max_depth': 3, 'reg_alpha': 0.005, 'reg_lambda': 0.005, 'subsample': 0.7, 'min_child_weight': 5, 'gamma': 0.4, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Grid search run 50/50:\n",
+      "Training with parameters: {'booster': 'gbtree', 'eval_metric': 'auc', 'nthread': 4, 'objective': 'binary:logistic', 'seed': 12345, 'colsample_bytree': 0.7, 'colsample_bylevel': 0.9, 'eta': 0.005, 'max_depth': 5, 'reg_alpha': 0.005, 'reg_lambda': 0.05, 'subsample': 0.7, 'min_child_weight': 5, 'gamma': 0.0, 'monotone_constraints': (1, 1, 1, -1, 1, 1, -1, -1, -1, 1)}\n",
+      "---------- ----------\n",
+      "Monotonic GBM training completed in 335.38 s.\n"
+     ]
+    }
+   ],
    "source": [
     "# dictionary of hyperparameter value lists for grid search\n",
     "gs_params = {'colsample_bytree': [0.3, 0.5, 0.7, 0.9],\n",
@@ -500,10 +803,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "3ce4a042",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation AUC: 0.7919.\n"
+     ]
+    }
+   ],
    "source": [
     "print('Validation AUC: %.4f.' % best_mxgb.best_score)"
    ]
@@ -518,10 +829,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "daf86e7d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/patrickh/Workspace/GWU_rml/assignments/assign_env/lib/python3.6/site-packages/xgboost/core.py:104: UserWarning: ntree_limit is deprecated, use `iteration_range` or model slicing instead.\n",
+      "  UserWarning\n"
+     ]
+    }
+   ],
    "source": [
     "dtest = xgb.DMatrix(test[x_names])\n",
     "best_mxgb_submit = pd.DataFrame(best_mxgb.predict(dtest, ntree_limit=best_mxgb.best_ntree_limit), columns=['phat'])\n",
@@ -547,8 +867,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "bb7070a9",
+   "execution_count": 16,
+   "id": "9da92af1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -568,7 +888,7 @@
     "\n",
     "    \"\"\"\n",
     "\n",
-    "    # cartesian product of _cv_params\n",
+    "    # cartesian product of gs_params\n",
     "    keys, values = zip(*gs_params.items())\n",
     "    experiments = [dict(zip(keys, v)) for v in itertools.product(*values)]\n",
     "\n",
@@ -627,12 +947,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "f8e7edc7",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Grid search run 1/10:\n",
+      "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 512, 'max_interaction_bins': 16, 'interactions': 5, 'outer_bags': 4, 'inner_bags': 0, 'learning_rate': 0.05, 'validation_size': 0.25, 'min_samples_leaf': 1, 'max_leaves': 3}\n",
+      "Grid search new best score discovered at iteration 1/10: 0.8217.\n",
+      "---------- ----------\n",
+      "Grid search run 2/10:\n",
+      "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 128, 'max_interaction_bins': 32, 'interactions': 5, 'outer_bags': 8, 'inner_bags': 0, 'learning_rate': 0.001, 'validation_size': 0.25, 'min_samples_leaf': 2, 'max_leaves': 5}\n",
+      "---------- ----------\n",
+      "Grid search run 3/10:\n",
+      "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 512, 'max_interaction_bins': 16, 'interactions': 5, 'outer_bags': 4, 'inner_bags': 0, 'learning_rate': 0.001, 'validation_size': 0.5, 'min_samples_leaf': 1, 'max_leaves': 3}\n",
+      "---------- ----------\n",
+      "Grid search run 4/10:\n",
+      "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 128, 'max_interaction_bins': 64, 'interactions': 5, 'outer_bags': 4, 'inner_bags': 4, 'learning_rate': 0.05, 'validation_size': 0.5, 'min_samples_leaf': 1, 'max_leaves': 5}\n",
+      "---------- ----------\n",
+      "Grid search run 5/10:\n",
+      "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 512, 'max_interaction_bins': 64, 'interactions': 15, 'outer_bags': 4, 'inner_bags': 0, 'learning_rate': 0.05, 'validation_size': 0.1, 'min_samples_leaf': 10, 'max_leaves': 3}\n",
+      "Grid search new best score discovered at iteration 5/10: 0.8247.\n",
+      "---------- ----------\n",
+      "Grid search run 6/10:\n",
+      "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 256, 'max_interaction_bins': 16, 'interactions': 15, 'outer_bags': 12, 'inner_bags': 4, 'learning_rate': 0.01, 'validation_size': 0.1, 'min_samples_leaf': 2, 'max_leaves': 5}\n",
+      "---------- ----------\n",
+      "Grid search run 7/10:\n",
+      "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 512, 'max_interaction_bins': 32, 'interactions': 15, 'outer_bags': 4, 'inner_bags': 4, 'learning_rate': 0.05, 'validation_size': 0.25, 'min_samples_leaf': 10, 'max_leaves': 1}\n",
+      "---------- ----------\n",
+      "Grid search run 8/10:\n",
+      "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 256, 'max_interaction_bins': 16, 'interactions': 15, 'outer_bags': 8, 'inner_bags': 4, 'learning_rate': 0.001, 'validation_size': 0.5, 'min_samples_leaf': 5, 'max_leaves': 3}\n",
+      "---------- ----------\n",
+      "Grid search run 9/10:\n",
+      "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 256, 'max_interaction_bins': 16, 'interactions': 10, 'outer_bags': 8, 'inner_bags': 0, 'learning_rate': 0.05, 'validation_size': 0.5, 'min_samples_leaf': 5, 'max_leaves': 1}\n",
+      "---------- ----------\n",
+      "Grid search run 10/10:\n",
+      "Training with parameters: {'n_jobs': 4, 'early_stopping_rounds': 100, 'random_state': 12345, 'max_bins': 512, 'max_interaction_bins': 64, 'interactions': 5, 'outer_bags': 4, 'inner_bags': 0, 'learning_rate': 0.001, 'validation_size': 0.25, 'min_samples_leaf': 2, 'max_leaves': 3}\n",
+      "---------- ----------\n",
+      "EBM training completed in 325.48 s.\n"
+     ]
+    }
+   ],
    "source": [
     "# dictionary of hyperparameter value lists for grid search\n",
     "gs_params = {'max_bins': [128, 256, 512],\n",
@@ -667,10 +1027,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "cd8c96fb",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation AUC: 0.8247.\n"
+     ]
+    }
+   ],
    "source": [
     "best_ebm_perf = ROC(best_ebm.predict_proba).explain_perf(valid[x_names], valid[y_name])\n",
     "print('Validation AUC: %.4f.' % best_ebm_perf._internal_obj['overall']['auc'])"
@@ -686,7 +1054,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "ee80d09a",
    "metadata": {},
    "outputs": [],
@@ -706,10 +1074,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "e978d190",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All tasks completed in 675.61 s.\n"
+     ]
+    }
+   ],
    "source": [
     "toc = time.time() - tic\n",
     "print('All tasks completed in %.2f s.' % (toc))"
@@ -725,10 +1101,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "436bc4fc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "H2O session _sid_a55a closed.\n"
+     ]
+    }
+   ],
    "source": [
     "h2o.cluster().shutdown(prompt=False)"
    ]
diff --git a/assignments/assignment_1/ph_best_glm.csv b/assignments/assignment_1/ph_best_glm.csv
new file mode 100644
index 0000000..d067ceb
--- /dev/null
+++ b/assignments/assignment_1/ph_best_glm.csv
@@ -0,0 +1,19832 @@
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diff --git a/assignments/data/hmda_test_preprocessed.zip b/assignments/data/hmda_test_preprocessed.zip
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+++ b/assignments/tex/assignment_1.tex
@@ -0,0 +1,93 @@
+% Copyright Patrick Hall 2021
+
+\documentclass[fleqn]{article}
+\renewcommand\refname{}
+\title{Responsible Machine Learning\\\Large{Assignment 1}\\\Large{10 points}}
+\author{\copyright Patrick Hall 2021}
+
+\usepackage{graphicx}
+\usepackage{fullpage}
+\usepackage{pdfpages}
+\usepackage{amsmath}
+\usepackage{amssymb}
+\usepackage{mathtools}
+\usepackage{MnSymbol}
+\usepackage{enumerate}
+\usepackage{setspace}
+\usepackage[colorlinks, breaklinks=true]{hyperref} 
+\usepackage{float}
+\usepackage{caption}
+\usepackage{subcaption}
+\usepackage{multicol}
+\usepackage{color}
+\usepackage{listings}
+\usepackage{csvsimple}
+\usepackage{algorithm}
+\usepackage{algorithmic}
+\usepackage{verbatim}
+\usepackage{mdframed}
+\usepackage{changepage}
+\usepackage[top=1in, bottom=1in, left=1in, right=1in]{geometry}
+
+\begin{document}
+
+\maketitle
+
+\noindent In Assignment 1, you will work with your group to train interpretable machine learning (ML) models following the instructions below. A \href{https://nbviewer.jupyter.org/github/jphall663/GWU_rml/blob/master/assignments/assignment_1/assign_1_template.ipynb}{template} has been provided as an example of how to train and compare a few different interpretable models. For those of you who use Python virtual environments, a basic \href{https://github.com/jphall663/GWU_rml/blob/master/assignments/requirements.txt}{\texttt{requirements.txt}} file is also available for the template.\\
+
+\noindent Please let me know immediately if you find typos or mistakes in this assignment or related materials. 
+
+\section{Download Data.}
+
+Download Home Mortgage Disclosure Act (HMDA) data as zip files from \href{https://github.com/jphall663/GWU_rml/tree/master/assignments/data}{this folder} in the class repository. The folder includes two data files:
+
+\begin{itemize}
+	\item \texttt{hmda\_train\_preprocessed.zip} -- Zipped CSV HMDA \textit{labeled} training data.
+	\item \texttt{hmda\_test\_preprocessed.zip} -- Zipped CSV HMDA \textit{unlabeled} test data.
+\end{itemize}
+
+\noindent Later you will score the unlabeled test data with your models and submit these scores as part of your assignment deliverable. See cell 3 in the template.
+
+\section{Load and Explore Data.}
+
+Load the data into modeling software. Training data contains 160338 rows and 23 columns. Test data contains 19831 rows and 22 columns. The features to use for Assignment 1 are as follows:
+
+\begin{itemize}\small
+\item \texttt{high\_priced}: Binary target, whether (1) or not (0) the annual percentage rate (APR) charged for a mortgage is 150 basis points (1.5\%) or more above a survey-based estimate of similar mortgages.
+\item \texttt{conforming}: Binary numeric input, whether the mortgage conforms to normal standards (1), or whether the loan is different (0), e.g., jumbo, HELOC, reverse mortgage, etc.
+\item \texttt{debt\_to\_income\_ratio\_std}: Numeric input, standardized debt-to-income ratio for mortgage applicants. 
+\item \texttt{debt\_to\_income\_ratio\_missing}: Binary numeric input, missing marker (1) for \texttt{debt\_to\_income\_ratio\_std}.
+\item \texttt{income\_std}: Numeric input, standardized income for mortgage applicants. 
+\item \texttt{loan\_amount\_std}: Numeric input, standardized amount of the mortgage for applicants. 
+\item \texttt{intro\_rate\_period\_std}: Numeric input, standardized introductory rate period for mortgage applicants.
+\item \texttt{loan\_to\_value\_ratio\_std}: Numeric input, ratio of the mortgage size to the value of the property for mortgage applicants. 
+\item \texttt{no\_intro\_rate\_period\_std}: Binary numeric input, whether or not a mortgage does not include an introductory rate period.
+\item \texttt{property\_value\_std}: Numeric input, value of the mortgaged property. 
+\item \texttt{term\_360}: Binary numeric input, whether the mortgage is a standard 360 month mortgage (1) or a different type of mortgage (0).
+\end{itemize}
+
+\noindent See cell 4 in the template for modeling roles.\\
+
+\noindent This data contains no major quality issues, so no preprocessing is required. Please familiarize yourself with the data using basic exploration techniques -- see cells 5--6 in the template. You may optionally try to improve your model with feature engineering or other preprocessing approaches.\\
+
+\noindent Training data should be used to create training and validation partitions. Test data will only be used to evaluate your models by the instructor. See cell 7 in the template.
+
+\section{Train Interpretable Models}
+
+Train at least two types of interpretable models, ensuring best practices like reproducibility, validation-based early-stopping, and grid search are used. (Scikit-learn does not necessarily make applying such best practices easy.) You are encouraged to try packages like \href{https://docs.h2o.ai/h2o/latest-stable/h2o-docs/downloading.html}{\texttt{h2o}}, \href{https://github.com/interpretml/interpret}{\texttt{interpret}}, and \href{https://xgboost.readthedocs.io/en/latest/install.html}{\texttt{XGBoost}}, but you may use any standard modeling approach, as long as it is interpretable and you will be able to apply explanation, discrimination testing and remediation, and model debugging approaches in coming weeks.\\
+
+\noindent The template contains examples for elastic net logistic regression using \texttt{h2o} (see cells 8--10), monotonic gradient boosting machines (GBM) using \texttt{XGBoost} (see cells 12--14), and explainable boosting machines (EBM) using \texttt{interpret} (see cells 16--18). 
+
+\section{Submit Code Results}
+
+Your deliverable for this assignment has two parts. Each part is worth 5 points. 
+
+\begin{itemize}
+	\item You must check in your code to a public GitHub repository by the deadline below. Code should be available as a commented script, Jupyter notebook, R markdown or other polished and professional format. 
+	\item You must create submission files with output probabilities for each row of the test data. The submission file should have one column named \texttt{phat}. Each model should have a separate submission file named using a \texttt{<group\_indentifier>\_<model\_type>.csv} convention, similar to the \href{}{example submission file}. 
+\end{itemize}
+
+\noindent Your deliverables are due Sunday, May 30\textsuperscript{th}, at 11:59:59 PM ET. Please send an email to \href{mailto:jphall@gwu.edu}{\texttt{jphall@gwu.edu}} by that deadline with the link to your group's GitHub page and with your zipped submission files. 
+
+\end{document}
+
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-% Copyright Patrick Hall 2021
-
-\documentclass[fleqn]{article}
-\renewcommand\refname{}
-\title{Responsible Machine Learning\\\Large{Assignment 1}}
-\author{\copyright Patrick Hall 2021}
-
-\usepackage{graphicx}
-\usepackage{fullpage}
-\usepackage{pdfpages}
-\usepackage{amsmath}
-\usepackage{amssymb}
-\usepackage{mathtools}
-\usepackage{MnSymbol}
-\usepackage{enumerate}
-\usepackage{setspace}
-\usepackage[colorlinks, breaklinks=true]{hyperref} 
-\usepackage{float}
-\usepackage{caption}
-\usepackage{subcaption}
-\usepackage{multicol}
-\usepackage{color}
-\usepackage{listings}
-\usepackage{csvsimple}
-\usepackage{algorithm}
-\usepackage{algorithmic}
-\usepackage{verbatim}
-\usepackage{mdframed}
-\usepackage{changepage}
-\usepackage[top=1in, bottom=1in, left=1in, right=1in]{geometry}
-
-\begin{document}
-
-\maketitle
-
-In Assignment 1, you will work with your group to train interpretable machine learning (ML) models. 
-
-
-
-
-\end{document}
\ No newline at end of file