Update app.py

app.py

@@ -99,13 +99,13 @@ unique_levels = ['Single', 'Multiple']
 
 #Assign hyperparameters.
 
-y1_xgb_params = {'objective': 'binary:logistic', 'booster': 'gbtree', 'lambda': 0.3506223423303318, 'alpha': 0.010119011691233883, 'max_depth': 9, 'eta': 0.965102554386191, 'gamma': 1.119572508228617e-08, 'grow_policy': 'depthwise'}
+y1_xgb_params = {'objective': 'binary:logistic', 'booster': 'gbtree', 'lambda': 0.5510926378114541, 'alpha': 2.0796731320109623e-05, 'max_depth': 8, 'eta': 0.7105161360756392, 'gamma': 0.0063548351925993354, 'grow_policy': 'lossguide'}
 
-y1_lgb_params = {'objective': 'binary', 'boosting_type': 'gbdt', 'lambda_l1': 8.520929262020913e-05, 'lambda_l2': 1.6655311531708117, 'num_leaves': 136, 'feature_fraction': 0.7509810402777333, 'bagging_fraction': 0.9102746088494693, 'bagging_freq': 1, 'min_child_samples': 55}
+y1_lgb_params = {'objective': 'binary', 'boosting_type': 'gbdt', 'lambda_l1': 0.00015201438108112603, 'lambda_l2': 1.2678336645891737e-05, 'num_leaves': 201, 'feature_fraction': 0.7349321144466956, 'bagging_fraction': 0.9213936471712818, 'bagging_freq': 2, 'min_child_samples': 27}
 
-y1_cb_params = {'objective': 'Logloss', 'colsample_bylevel': 0.062057010685389026, 'depth': 3, 'boosting_type': 'Plain', 'bootstrap_type': 'MVS'}
+y1_cb_params = {'objective': 'Logloss', 'colsample_bylevel': 0.08511055529750637, 'depth': 12, 'boosting_type': 'Plain', 'bootstrap_type': 'Bayesian', 'bagging_temperature': 3.571269095878882}
 
-y1_rf_params = {'criterion': 'gini', 'bootstrap': 'auto', 'max_features': 'auto', 'max_depth': 95, 'n_estimators': 10, 'min_samples_leaf': 1, 'min_samples_split': 2}
+y1_rf_params = {'criterion': 'entropy', 'bootstrap': 'auto', 'max_features': 'sqrt', 'max_depth': 90, 'n_estimators': 900, 'min_samples_leaf': 1, 'min_samples_split': 2}
 
 
 #Modeling for y1/AE.