[scikit-learn] Combine already fitted models

[Sebastian Raschka]

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 <rvernica at 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
> 
> 
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