标准化 sklearn 中的回归器

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 回归和 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

你可以使用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)
...