Hi, Rares, > vc = VotingClassifier(...) > vc.estimators_ = [e1, e2, ...] > vc.le_ = ... > vc.predict(...) > > But I am not sure it is recommended to modify the "private" estimators_ and > le_ attributes.
I think that this may work if you don't call the fit method of the VotingClassifier after that due to https://github.com/scikit-learn/scikit-learn/blob/ef5cb84a/sklearn/ensemble/voting_classifier.py#L186 Also, I see that we have only added one check in predict(), "check_is_fitted(self, 'estimators_')", for checking that the VotingClassifier was fit, so your proposed method could/should work as a workaround ;) Best, Sebastian > On Oct 1, 2017, at 7:22 PM, Rares Vernica <rvern...@gmail.com> wrote: > > > > I am looking at VotingClassifier but it seems that it is expected that > > > the estimators are fitted when VotingClassifier.fit() is called. I don't > > > see how I can have already fitted classifiers combined under a > > > VotingClassifier. > > > > I think the opposite is true: The classifiers provided via an `estimators` > > argument upon initialization will be cloned and fitted if you call > > VotingClassifier's fit(). Based on your follow-up question, I think you > > meant "it is expected that the estimators are *not* fitted when > > VotingClassifier.fit() is called," right?! > > Yes, you are right. Sorry for the confusion. Thanks for the pointer! > > I am also exploring something like: > > vc = VotingClassifier(...) > vc.estimators_ = [e1, e2, ...] > vc.le_ = ... > vc.predict(...) > > But I am not sure it is recommended to modify the "private" estimators_ and > le_ attributes. > > -- > Rares > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
