def predict(self): regr = linear_model.LinearRegression() regr.fit(self.x_train, self.y_train) train_result = regr.predict(self.x_train) test_result = regr.predict(self.x_test) BaseModel.export_prediction(test_result, 'LinearRegression') return (train_result, test_result)
def predict(self): svr_rbf = SVM.SVR(kernel='rbf', C=1e3, gamma=0.1) train_result = svr_rbf.fit(self.x_train, self.y_train).predict(self.x_train) test_result = svr_rbf.fit(self.x_train, self.y_train).predict(self.x_test) BaseModel.export_prediction(test_result, 'SVR_RBF_C1e3_Gamma01') return (train_result, test_result)
def predict(self): regr_rf = RFR(max_depth=17, random_state=9, n_estimators=50, n_jobs=-1) regr_rf.fit(self.x_train, self.y_train) train_result = regr_rf.predict(self.x_train) test_result = regr_rf.predict(self.x_test) export_filename = 'RandomForestReg' if self.drop_feature_names: export_filename += '_without_' + '_'.join(self.drop_feature_names) BaseModel.export_prediction(test_result, export_filename) return (train_result, test_result)