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)