如何使用Scikit-learn创建同时具有数字和1-hot分类特征的训练数据集？

from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.datasets import make_classification

X, y = make_classification(n_samples=100, n_features=3, n_informative=2, n_classes=2, random_state=42)

numeric_features = [0, 1]  # 数字特征的索引
categorical_features = [2]  # 分类特征的索引

preprocessor = ColumnTransformer(
    transformers=[
        ('num', 'passthrough', numeric_features),  # 数字特征保持不变
        ('cat', OneHotEncoder(), categorical_features)  # 分类特征进行1-hot编码
    ])

X_transformed = preprocessor.fit_transform(X)