Sklearn：有没有办法为管道定义特定的分数类型？

Question

I can do this:

model=linear_model.LogisticRegression(solver='lbfgs',max_iter=10000)
kfold = model_selection.KFold(n_splits=number_splits,shuffle=True, random_state=random_state)
scalar = StandardScaler()
pipeline = Pipeline([('transformer', scalar), ('estimator', model)])
results = model_selection.cross_validate(pipeline, X, y, cv=kfold, scoring=score_list,return_train_score=True)

where score_list can be something like ['accuracy','balanced_accuracy','precision','recall','f1'] .

I also can do this:

kfold = model_selection.KFold(n_splits=number_splits,shuffle=True, random_state=random_state)
scalar = StandardScaler()
pipeline = Pipeline([('transformer', scalar), ('estimator', model)])
for i, (train, test) in enumerate(kfold.split(X, y)):
    pipeline.fit(self.X[train], self.y[train])
    pipeline.score(self.X[test], self.y[test])

However, I am not able to change the score type for pipeline in the last line. How can I do that?

Answer 1

score method is always accuracy for classification and r2 score for regression. There is no parameter to change that. It comes from the Classifiermixin and RegressorMixin .

Instead, when we need other scoring options, we have to import it from sklearn.metrics like the following.

from sklearn.metrics import balanced_accuracy

y_pred=pipeline.score(self.X[test])
balanced_accuracy(self.y_test, y_pred)