Upload app.py

app.py

@@ -74,7 +74,7 @@ y3_valid = x3_valid.pop('OUTCOME')
 
 y1_params =  {'criterion': 'entropy', 'max_depth': 4, 'n_estimators': 1400, 'min_samples_leaf': 3, 'min_samples_split': 10, 'random_state': 31}
 y2_params =  {'objective': 'binary', 'boosting_type': 'gbdt', 'lambda_l1': 0.6726024444744665, 'lambda_l2': 3.946412314107168e-08, 'num_leaves': 180, 'feature_fraction': 0.6838613576310666, 'bagging_fraction': 0.43284935253254003, 'bagging_freq': 5, 'min_child_samples': 83, 'metric': 'binary_logloss', 'verbosity': -1, 'random_state': 31}
-y3_params =  {'objective': 'Logloss', 'colsample_bylevel': 0.08960988134854374, 'depth': 5, 'boosting_type': 'Plain', 'bootstrap_type': 'Bernoulli', 'subsample': 0.15476628999955983, 'seed': 31}
+y3_params =  {'objective': 'Logloss', 'colsample_bylevel': 0.08960988134854374, 'depth': 5, 'boosting_type': 'Plain', 'bootstrap_type': 'Bernoulli', 'subsample': 0.15476628999955983, 'random_seed': 31}
 
 
 #Training models.