[scikit-learn] Bootstrapping in sklearn

[Daniel Saxton]

J.B.,

Any help would certainly be welcome, no matter how slow.  I appreciate the
interest.

Thanks,
Daniel

On Tue, Sep 18, 2018, 8:47 AM Brown J.B. via scikit-learn <
scikit-learn at python.org> wrote:

> Resampling is a very important interesting contribution which relates very
> closely to my primary research in applied ML for chemical development.
> I'd be very interested in contributing documentation and learning new
> things along the way, but I potentially would be perceived as slow because
> of juggling many projects and responsibilities.
> (I failed once before at timely reviewing of a PR for multi-metric
> optimization for 0.19.)
> If still acceptable, please let me know, and I'm happy to try to help.
>
> J.B.
>
>
> 2018年9月18日(火) 20:37 Daniel Saxton <daniel.saxton at gmail.com>:
>
>> Great, I went ahead and contacted Constantine.  Documentation was
>> actually the next thing that I wanted to work on, so hopefully he and I can
>> put something together.
>>
>> Thanks for the help.
>>
>> On Tue, Sep 18, 2018 at 2:42 AM Olivier Grisel <olivier.grisel at ensta.org>
>> wrote:
>>
>>> This looks like a very useful project.
>>>
>>> There is also scikits-bootstraps [1]. Personally I prefer the flat
>>> package namespace of resample (I am not a fan of the 'scikits' namespace
>>> package) but I still think it would be great to contact the author to know
>>> if he would be interested in joining efforts.
>>>
>>> What currently lacks from both projects is a good sphinx-based
>>> documentation that explains in a couple of paragraphs with examples what
>>> are the different non-parametric inference methods, what are the pros and
>>> cons for each of them (sample complexity, computation complexity, kinds of
>>> inference, bias, theoretical asymptotic results, practical discrepancies
>>> observed in the finite sample setting, assumptions made on the distribution
>>> of the data...) and ideally the doc would have reference to examples (using
>>> sphinx-gallery) that would highlight the behavior of the tools in both
>>> nominal and pathological cases.
>>>
>>> [1] https://github.com/cgevans/scikits-bootstrap
>>>
>>> --
>>> Olivier
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>>>
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