管道中的fit vs fit_变换

在本页 https://www.kaggle.com/baghern/a-deep-dive-into-sklearn-pipelines

它叫 fit_transfrom 用于转换数据,如下所示:

from sklearn.pipeline import FeatureUnion

feats = FeatureUnion([('text', text), 
                      ('length', length),
                      ('words', words),
                      ('words_not_stopword', words_not_stopword),
                      ('avg_word_length', avg_word_length),
                      ('commas', commas)])

feature_processing = Pipeline([('feats', feats)])
feature_processing.fit_transform(X_train)

而在特征处理的训练中,它只使用 fit 然后 predict

from sklearn.ensemble import RandomForestClassifier

pipeline = Pipeline([
    ('features',feats),
    ('classifier', RandomForestClassifier(random_state = 42)),
])

pipeline.fit(X_train, y_train)

preds = pipeline.predict(X_test)
np.mean(preds == y_test)

问题是 适合 在上进行转换 X_train (如通过 transform ,因为我们没有打电话 fit_transform 这里)第二个病例?