类型错误：fit_transform() 需要 2 个位置参数，但给出了 3 个

我有熊猫数据框df。我想对连续和分类特征进行编码df使用不同的编码器。我觉得用起来很舒服make_column_transformer，但是下面显示的代码失败了LabelEncoder()，但可以很好地与OneHotEncoder(handle_unknown='ignore'))。错误信息是：

类型错误：fit_transform() 需要 2 个位置参数，但 3 个是 给定

我不清楚如何解决这个问题。

代码：

from sklearn.compose import make_column_transformer
from sklearn.preprocessing import RobustScaler, OneHotEncoder, LabelEncoder

continuous_features = ['COL1','COL2']       
categorical_features = ['COL3','COL4']

column_trans = make_column_transformer(
    (categorical_features,LabelEncoder()),
    (continuous_features, RobustScaler()))

X_enc = column_trans.fit_transform(df)

根据https://scikit-learn.org/stable/modules/ generated/sklearn.compose.make_column_transformer.html https://scikit-learn.org/stable/modules/generated/sklearn.compose.make_column_transformer.html.

make_column_transformer(
...     (StandardScaler(), ['numerical_column']),
...     (OneHotEncoder(), ['categorical_column']))

所以对于你的情况：

from sklearn.compose import make_column_transformer
from sklearn.preprocessing import RobustScaler, OneHotEncoder, LabelEncoder

continuous_features = ['COL1','COL2']       
categorical_features = ['COL3','COL4']

column_trans = make_column_transformer(
    (OneHotEncoder(), categorical_features),
    (RobustScaler(), continuous_features))

X_enc = column_trans.fit_transform(df)

如果你想使用LabelEncoder()，你只能通过一列，不能通过两列！

希望这可以帮助。