For some reason I thought we had a "prefit" parameter.
I think we should.
On 10/01/2017 07:39 PM, Sebastian Raschka wrote:
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
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