Thanks for the help, guys. Indeed it's easy enough to implement a class
for combining the classifiers in a model-specific way. Thanks for the note
on the oob-score!
On Thu, Jan 10, 2013 at 2:28 AM, Olivier Grisel <olivier.gri...@ensta.org>wrote:
> 2013/1/10 Gael Varoquaux <gael.varoqu...@normalesup.org>:
> > On Thu, Jan 10, 2013 at 03:57:23PM +1100, Juan Nunez-Iglesias wrote:
> >> More precisely, I think David wants a function that will take a set of
> RFs and
> >> return a new classifier object that does all the weighted averaging Andy
> >> suggested for you transparently. And the answer is no, sklearn doesn't
> have
> >> such a function. =)
> >
> > But it's fairly trivial to write an object that does that.
>
> I think such a new method would be interesting as it could also be of
> interest for linear models.
>
> >>> clf1 = Perceptron().fit(X_bigdata_1, y_bigdata_1)
>
> On another machine
>
> >>> clf2 = Perceptron().fit(X_bigdata_2, y_bigdata_2)
>
> Then averaging the models:
>
> >>> clf3 = clf2.combine(clf1, copy=True)
>
> A type error would be raised if clf1 is of a type that could not be
> combined with clf2 (for instance if combining a linear model with a
> RF).
>
> Naive bayes models could also probably be extended to support such a
> combine method if they would also store the `n_samples` of the
> training set encountered at fit time as a new fitted attribute.
>
> Off course the exact semantics of such a "combiner" method would be
> model specific but the global expectation would be that combining two
> compatible models trained on distinct shards of the data would result
> in a model that generalize better than the original models even if not
> always strictly equivalent to fit a unique model on the concatenated
> data.
>
> --
> Olivier
> http://twitter.com/ogrisel - http://github.com/ogrisel
>
>
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