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
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