model = RandomForestClassifier(n_estimators=1000) if model_name == 'gbm': model = GradientBoostingClassifier() if model_name == 'xgb': model = XGBRegressor() ## Train and Predict if (model_name == 'linearregression' or model_name == 'xgb'): model.fit(X_train, Y_train) Predict = model.predict(X_test) elif (model_name == 'svmlin'): model.fit(X_train, Y_train) Predict = model.decision_function(X_test) elif (model_name == 'coxregression'): if data_name == 'maggic': model.fit(Train_All, duration_col='days_to_fu', event_col='death_all') Predict = model.predict_partial_hazard(X_test) elif (data_name == 'heart_trans' or 'heart_wait'): model.fit(Train_All, duration_col="'Survival'", event_col="'Censor'") Predict = model.predict_partial_hazard(X_test) else: model.fit(X_train, Y_train)