[scikit-learn] Bootstrapping in sklearn

[Olivier Grisel]

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
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20180918/1c98e640/attachment.html>