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@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.sax...@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.gri...@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 >>> _______________________________________________ >>> scikit-learn mailing list >>> scikit-learn@python.org >>> https://mail.python.org/mailman/listinfo/scikit-learn >>> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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