>>We are still missing a stacking ensemble meta-estimator:
>>http://www.machine-learning.martinsewell.com/ensembles/stacking/Wolpert1992.pdf<http://www.machine-learning.martinsewell.com/ensembles/stacking/Wolpert1992.pdf>
>> (2748
citations)
I would be glad to work on this, beside the guidelines here (
http://scikit-learn.org/stable/developers/) are there any other guidelines
that I'm supposed to know or read before working on it,
Thanks a lot
Respectfully
Yakoub
On Mon, Dec 9, 2013 at 3:53 PM, Olivier Grisel <[email protected]>wrote:
> 2013/12/8 Gael Varoquaux <[email protected]>:
> > Hi Magellane,
> >
> >> I would like to provide an implementation for the Ensemble selection
> >> technique as described by the following paper : Ensemble selection from
> >> libraries of models by Rich Caruana ,Alexandru Niculescu-Mizil,Geoff
> >> Crew,Alex Ksikes (
> >> www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml04.icdm06long.pdf
> )
> >
> > This paper has 200 citations on Google scholar, which is somewhat on the
> > low end of what we include in scikit-learn.
> >
> > Do you believe that it is a major tool that is very useful in general?
> > Have you had a lot of success using it?
>
> There are at least 2 R packages used by kagglers that implement this
> ensemble method (and refinements):
>
>
> http://moderntoolmaking.blogspot.fr/2013/03/new-package-for-ensembling-r-models.html
>
> http://www.kaggle.com/forums/t/3661/medley-a-new-r-package-for-blending-regression-models
>
> There is also a Python project that works with scikit-learn:
>
> https://github.com/dclambert/pyensemble
>
> However in practice this method is likely to generate a large amount
> of models and predictions. Keeping it all in memory might not be
> efficient. On the other hand storing temporary datastructures (pickled
> scikit-learn models and prediction data) on the filesystem might lead
> to frameworkish code which we try to avoid in a library such as
> scikit-learn.
>
> --
> Olivier
> http://twitter.com/ogrisel - http://github.com/ogrisel
>
>
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