我试着做特征选择 RFECV 为了它和 LogisticRegression . 为此，我需要缩放数据，因为否则回归将不会收敛。然而，我认为如果我首先缩放完整的数据，它将是有偏差的（基本上是数据泄漏到测试集）。
这是我目前的代码：

from sklearn.feature_selection import RFECV
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import Pipeline
cv = StratifiedKFold(5)
scaler = MinMaxScaler()
reg = LogisticRegression(max_iter=1000, solver="newton-cg")
pipeline = Pipeline(steps=[("scale",scaler),("lr",reg)])
visualizer = RFECV(pipeline, cv=cv, scoring='f1_weighted')

但它给了我一个错误：

Traceback (most recent call last):
  File "<ipython-input-267-0073ead26d52>", line 1, in <module>
    visualizer.fit(x_6, y_6)        # Fit the data to the visualizer
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\feature_selection\_rfe.py", line 550, in fit
    scores = parallel(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\feature_selection\_rfe.py", line 551, in <genexpr>
    func(rfe, self.estimator, X, y, train, test, scorer)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\feature_selection\_rfe.py", line 33, in _rfe_single_fit
    return rfe._fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\feature_selection\_rfe.py", line 204, in _fit
    raise RuntimeError('The classifier does not expose '
RuntimeError: The classifier does not expose "coef_" or "feature_importances_" attributes

我甚至还没有把它和数据吻合。
我试着搜索，但找不到任何有用的东西。你知道什么会失败吗？