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)
