Question

我正在使用sklearn，我想知道如何正确使用StandardScaler（）。我构建了一个函数，允许在Ridge和Lasso回归之间切换，并获取alpha值，回归量X和预测变量Y.所有回归量都应该标准化。

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量？

Answer 1

您可以使用sklearn.pipeline.make_pipeline：

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)
...

标准化sklearn中的回归量

1 个答案: