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
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