[scikit-learn] Bootstrapping in sklearn

[Brown J.B.]

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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