python - 为什么 sklearn 的 LabelEncoder 应该只用于目标变量？

Question

我试图创建一个带有 LabelEncoder 的管道来转换分类值。

cat_variable = Pipeline(steps = [
    ('imputer',SimpleImputer(strategy = 'most_frequent')),
    ('lencoder',LabelEncoder())
])
                        
num_variable = SimpleImputer(strategy = 'mean')

preprocess = ColumnTransformer (transformers = [
    ('categorical',cat_variable,cat_columns),
    ('numerical',num_variable,num_columns)
])

odel = RandomForestRegressor(n_estimators = 100, random_state = 0)

final_pipe = Pipeline(steps = [
    ('preprocessor',preprocess),
    ('model',model)
])

scores = -1 * cross_val_score(final_pipe,X_train,y,cv = 5,scoring = 'neg_mean_absolute_error')

但这会引发 TypeError:

TypeError: fit_transform() takes 2 positional arguments but 3 were given

在进一步的引用中，我发现像 LabelEncoders 这样的转换器不应该与特征一起使用，而应该只用于预测目标。
From Documentation:

class sklearn.preprocessing.LabelEncoder

Encode target labels with value between 0 and n_classes-1.

This transformer should be used to encode target values, i.e. y, and not the input X.

我的问题是，为什么我们不能在特征变量上使用 LabelEncoder，还有其他变压器有这样的条件吗？

Answer 1

LabelEncoder可用于标准化标签或转换非数字标签。对于输入分类，您应该使用 OneHotEncoder .
区别:

le = preprocessing.LabelEncoder()
le.fit_transform([1, 2, 2, 6])
array([0, 0, 1, 2])

enc = OneHotEncoder(handle_unknown='ignore')
enc.fit_transform([[1], [2], [2], [6]]).toarray()
array([[1., 0., 0.],
       [0., 1., 0.],
       [0., 1., 0.],
       [0., 0., 1.]])