Upload app.py

app.py

@@ -237,8 +237,8 @@ g3_y4_valid = g3_x4_valid.pop('OUTCOME')
 
 #Assign hyperparameters (g3).
 g3_y1_params =  {'objective': 'binary', 'boosting_type': 'gbdt', 'lambda_l1': 0.0002964237261979634, 'lambda_l2': 0.0003914277818587476, 'num_leaves': 13, 'feature_fraction': 0.4598669689672836, 'bagging_fraction': 0.5293813102688141, 'bagging_freq': 3, 'min_child_samples': 93, 'metric': 'binary_logloss', 'verbosity': -1, 'random_state': 31}
-g3_y2_params =  {'criterion': 'entropy', 'max_features': 'auto', 'max_depth': 53, 'n_estimators': 800, 'min_samples_leaf': 4, 'min_samples_split': 8, 'random_state': 31}
-g3_y3_params =  {'criterion': 'entropy', 'max_features': 'auto', 'max_depth': 44, 'n_estimators': 1000, 'min_samples_leaf': 2, 'min_samples_split': 7, 'random_state': 31}
+g3_y2_params =  {'criterion': 'entropy', 'max_depth': 53, 'n_estimators': 800, 'min_samples_leaf': 4, 'min_samples_split': 8, 'random_state': 31}
+g3_y3_params =  {'criterion': 'entropy', 'max_depth': 44, 'n_estimators': 1000, 'min_samples_leaf': 2, 'min_samples_split': 7, 'random_state': 31}
 g3_y4_params =  {'objective': 'binary', 'boosting_type': 'gbdt', 'lambda_l1': 1.9669754108753293e-06, 'lambda_l2': 0.0012741287968581684, 'num_leaves': 17, 'feature_fraction': 0.40138611969162713, 'bagging_fraction': 0.49839300388462815, 'bagging_freq': 4, 'min_child_samples': 91, 'metric': 'binary_logloss', 'verbosity': -1, 'random_state': 31}
 
 #Training models (g3).