python - Pipeline 中的项目何时调用 fit_transform()，何时调用 transform()？ (scikit-learn、管道)

Question

我正在尝试拟合一个使用 Pipeline 组合在一起的模型:

from sklearn import cross_validation
from sklearn.linear_model import LogisticRegression
from sklearn.grid_search import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler

cross_validation_object = cross_validation.StratifiedKFold(Y, n_folds = 10)
scaler = MinMaxScaler(feature_range = [0,1])
logistic_fit = LogisticRegression()

pipeline_object = Pipeline([('scaler', scaler),('model', logistic_fit)])

tuned_parameters = [{'model__C': [0.01,0.1,1,10],
                    'model__penalty': ['l1','l2']}]

grid_search_object = GridSearchCV(pipeline_object, tuned_parameters, cv = cross_validation_object, scoring = 'accuracy')

grid_search_object.fit(X_train,Y_train) 

我的问题:best_estimator 是否会根据训练数据中的值缩放测试数据？例如，如果我调用:

grid_search_object.best_estimator_.predict(X_test)

它不会尝试使缩放器适合 X_test 数据，对吗？它只会使用原始参数对其进行转换。

谢谢!

Answer 1

预测方法永远不适合任何数据。在这种情况下，正如您所描述的那样，best_estimator_ 管道将根据它在训练集上学到的缩放比例进行缩放。