Upload app.py

app.py

@@ -168,7 +168,7 @@ le = sklearn.preprocessing.LabelEncoder()
 x5[categorical_columns5] = x5[categorical_columns5].apply(le.fit_transform)
 
 #Assign hyperparameters.
-y1_params = {'objective': 'binary:logistic', 'booster': 'gbtree', 'lambda': 0.5059844209148782, 'alpha': 0.0030156848979492556, 'max_depth': 2, 'eta': 4.546875002603483e-07, 'gamma': 1.1982641538268563e-08, 'grow_policy': 'lossguide', 'eval_metric': 'auc', 'verbosity': 0, 'seed': 31}
+y1_params = {'objective': 'binary:logistic', 'booster': 'gbtree', 'lambda': 7.449603842156669e-07, 'alpha': 0.00015124151733686574, 'max_depth': 1, 'eta': 5.271006919170454e-08, 'gamma': 4.6961597706814486e-08, 'grow_policy': 'lossguide', 'eval_metric': 'auc', 'verbosity': 0, 'seed': 31}
 y2_params = {'criterion': 'gini', 'max_features': None, 'max_depth': 5, 'n_estimators': 1700, 'min_samples_leaf': 2, 'min_samples_split': 2, 'random_state': 31}
 y3_params =  {'objective': 'binary:logistic', 'booster': 'gbtree', 'lambda': 3.540855010579091e-08, 'alpha': 4.005546508605542e-08, 'max_depth': 5, 'eta': 5.190362998186933e-08, 'gamma': 1.1458984717217304e-05, 'grow_policy': 'depthwise', 'eval_metric': 'auc', 'verbosity': 0, 'seed': 31}
 y4_params =  {'objective': 'binary', 'booster': 'gbtree', 'lambda': 9.081139728398413e-05, 'alpha': 2.6896480100715624e-06, 'max_depth': 3, 'eta': 1.1457645461556677e-08, 'gamma': 0.00043222206530621666, 'grow_policy': 'depthwise', 'eval_metric': 'auc', 'verbosity': 0, 'seed': 31}