如何在scikit学习回归中不标准化目标数据

我试图以csv格式预测铜矿企业数据的数据集中的未来利润数据.

我读了数据:

data = pd.read_csv('data.csv')

我拆分数据:

data_target = data[target].astype(float)
data_used = data.drop(['Periodo', 'utilidad_operativa_dolar'], axis=1)
x_train, x_test, y_train, y_test = train_test_split(data_used, data_target, test_size=0.4,random_state=33)

创建一个svr预测器:

clf_svr= svm.SVR(kernel='rbf')

标准化数据:

from sklearn.preprocessing import StandardScaler
scalerX = StandardScaler().fit(x_train)
scalery = StandardScaler().fit(y_train)

x_train = scalerX.transform(x_train)
y_train = scalery.transform(y_train)
x_test = scalerX.transform(x_test)
y_test = scalery.transform(y_test)

print np.max(x_train), np.min(x_train), np.mean(x_train), np.max(y_train), np.min(y_train), np.mean(y_train)

然后预测:

y_pred=clf.predict(x_test)

并且预测数据也是标准化的.我希望预测数据采用原始格式,我该怎么做？