@Scott: See https://github.com/scikit-learn/scikit-learn/pull/5794 for checking the VotingClassifier
Best, Sebastian > On Nov 11, 2015, at 5:16 PM, Sebastian Raschka <se.rasc...@gmail.com> wrote: > > Hi, Scott, > > I was also thinking about regression when I implemented the VotingClassifier > but ultimately decided against it. I think that it could potentially be > useful, but it is not as "generally applicable" as the VotingClassifier I'd > say. > > Implementation-wise, I think refactoring it into a VotingBase parent and a > VotingClassifier and a VotingRegressor could technically be pretty > straight-forward though. > > I am only a little concerned if averaging the results of different regressors > could potentially help with the predictive performance. > > Would be interested to hear what others think. Has anyone averaged regression > models in practice? > >> On a semi-related note, I notice that VotingClassifier.predict() doesn't >> check to see if it has been fitted. Is there a general way to check whether >> a classifier has been fitted yet? > > Good point! I will add a check for that. I think the general solution is the > check_estimators_unfitted > > in > > from sklearn.utils.estimator_checks import check_estimators_unfitted > > Best, > Sebastian > >> On Nov 10, 2015, at 1:09 PM, Scott Turner <srt19...@gmail.com >> <mailto:srt19...@gmail.com>> wrote: >> >> First of all, my thanks for all your hard work in providing Scikit-Learn. >> It's a joy to use. >> >> Now that VotingClassifier has dropped, are there plans to create the >> analogous ensemble classifier for regression, i.e., one that averages the >> results of a list of base classifiers? I have an implementation that I >> would be happy to submit if there's interest. (I'd suggest extending the >> classifier to include a meta-classifier option as well as (say) 'mean' and >> 'median'.) But maybe someone is already working on this? >> >> On a semi-related note, I notice that VotingClassifier.predict() doesn't >> check to see if it has been fitted. Is there a general way to check whether >> a classifier has been fitted yet? >> >> Finally, I've also implemented the CONFINE and CONVINE algorithms for >> estimating the confidence of individual predictions from this paper >> <http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0048723>. >> This isn't complicated -- it's a matter of doing k-nn on the training data >> and then taking local error / variance means for the neighbors of the >> prediction. It's nice in the sense that it can be applied to any estimator, >> so it provides a way for getting confidence estimates (or at least local >> error estimates) for estimators that don't provide that otherwise. If >> there's interest I'm happy to contribute that code as well. >> >> -- Scott Turner >> ------------------------------------------------------------------------------ >> _______________________________________________ >> Scikit-learn-general mailing list >> Scikit-learn-general@lists.sourceforge.net >> <mailto:Scikit-learn-general@lists.sourceforge.net> >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > > ------------------------------------------------------------------------------ > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
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