Hi
I was interested in the implementation of *stacking ensemble
meta-estimator*for the scikit-learn project, and as suggested by a
previous email on the
mailing list I've gone through the source-code of scikit-learn in general
and the source code of ensemble methods in specific.
I want to start working on it, but I'm not sure of the details of the
process you use in contributing scikit-learn:
shall I first provide an implementation as pull request and then I can
discuss it here, like how to enhance and modify to comply to the standards
of the project ?
Or shall I begin by discussing the various parts of the implementation - on
the mailing list or with maintainers of the project - prior to working on
it ?
I appreciate much your consideration
Respectfully
M.Yakoub
On Wed, Dec 11, 2013 at 12:25 AM, m yakoub <[email protected]> wrote:
> >> Yes, you should probably start by reading the source code of related
> >> ensemble models such as Adaboost and Bagging models (and maybe GBRT
> >> and forest models but they are more tree related hence less decoupled
> >> from the base estimator).
>
> Thanks a lot for the advice !
>
>
> On Tue, Dec 10, 2013 at 9:12 PM, Olivier Grisel
> <[email protected]>wrote:
>
>> 2013/12/10 magellane a <[email protected]>:
>> >
>> >>>We are still missing a stacking ensemble meta-estimator:
>> >>>
>> 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,
>>
>> Yes, you should probably start by reading the source code of related
>> ensemble models such as Adaboost and Bagging models (and maybe GBRT
>> and forest models but they are more tree related hence less decoupled
>> from the base estimator).
>>
>> --
>> Olivier
>> http://twitter.com/ogrisel - http://github.com/ogrisel
>>
>>
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>
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