Right now I'm splitting the dataset in half to get the predictions of the first layer. I can make this configurable.
You're right that the literature doesn't really support different classifiers working on different representations. I’ll take this out. On Wed, Dec 16, 2015 at 11:03 AM, Andreas Mueller <t3k...@gmail.com> wrote: > I think stacking would be a nice contribution. > Are you doing loo / cross validation to get the predictions of the first > level? > Otherwise this is basically "VotingClassifier" > > And in the "literature" version, all classifiers get the same data. We > need to think about how and if we want to support > passing different representations to the different classifiers. Or is that > just ``FeatureUnion``? > > > On 12/15/2015 10:22 PM, Dan Shiebler wrote: > > Hello, > > I have some code and tests written for a StackingClassifier that has an > sklearn-like interface and is compatible with sklearn classifiers. The > classifier contains methods to easily train classifiers on different > transformations of the same data and train a meta-classifier on the > classifier outputs. Where would be the best place for this code? I believe > this class would be a useful addition to sklearn.ensemble. > > Thanks, > Dan > > > ------------------------------------------------------------------------------ > > > > _______________________________________________ > Scikit-learn-general mailing > listScikit-learn-general@lists.sourceforge.nethttps://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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