Python 3.x sklearn中回归函数的标准化

我正在与sklearn合作，我想知道StandardScaler（）是如何正确使用的。我建立了一个函数，允许在岭回归和套索回归之间切换，并获取alpha值、回归器X和预测变量Y。所有回归器都应该标准化

from sklearn.linear_model import Ridge, Lasso
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler() # Standardize regressors by removing the mean and scaling to unit variance

def do_penalized_regression(X, y, penalty, type):
    if type == "ridge":
        lm = Ridge(alpha = penalty, normalize=False)
    elif type == "lasso":
        lm = Lasso(alpha = penalty, normalize=False)
lm.scaler.fit(X,y)
return lm

这是要走的路还是我应该提前标准化回归器？

您可以使用：

---

非常感谢，经过一些非常好的迭代之后！

from sklearn.linear_model import Ridge, Lasso
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline


model = make_pipeline(StandardScaler(), lm)

model.fit(X, y)
...