如何使用sklearn对管道中的标签进行预处理？

from sklearn.preprocessing import LabelEncoder

le = LabelEncoder()

le.fit(labels)

encoded_labels = le.transform(labels)

original_labels = le.inverse_transform(encoded_labels)

from sklearn.preprocessing import LabelEncoder

# 原始标签数据
labels = ['A', 'B', 'A', 'C', 'B']

# 创建LabelEncoder对象
le = LabelEncoder()

# 对标签进行训练
le.fit(labels)

# 编码标签
encoded_labels = le.transform(labels)
print("Encoded labels:", encoded_labels)

# 反向转换编码后的标签
original_labels = le.inverse_transform(encoded_labels)
print("Original labels:", original_labels)

Encoded labels: [0 1 0 2 1]
Original labels: ['A' 'B' 'A' 'C' 'B']