Re: [scikit-learn] Bootstrapping in sklearn
Thanks, Olivier. We will try adding examples to show how it can be used in conjunction with sklearn to generate confidence intervals on linear model parameters, as well as prediction intervals for other classes of models. On Thu, Sep 20, 2018, 11:55 AM Olivier Grisel wrote: > I believe it would fit in sklearn-contrib even if it's more for > statistical inference rather than machine learning style prediction. > > Others might disagree. > > Anyways, joining efforts to improve documentation, CI, testing and so on > is always a good thing for your future users. > > -- > 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
Re: [scikit-learn] Bootstrapping in sklearn
I believe it would fit in sklearn-contrib even if it's more for statistical inference rather than machine learning style prediction. Others might disagree. Anyways, joining efforts to improve documentation, CI, testing and so on is always a good thing for your future users. -- Olivier ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
Re: [scikit-learn] Bootstrapping in sklearn
Olivier, I got in touch with Constantine from the scikits-bootstrap package and he's interested in merging the two projects. If we were to get some documentation together, do you feel that there is potential for inclusion as an sklearn-contrib package? I believe we would have most of the other requirements (testing, continuous integration, etc.), but is there anything else that you feel is missing? Thanks, Daniel On Tue, Sep 18, 2018 at 2:42 AM Olivier Grisel 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
Re: [scikit-learn] Bootstrapping in sklearn
Hello! > Any help would certainly be welcome, no matter how slow. I appreciate the > interest. > That sound interest! If you need help, let me know! I would be happy to help Regards! Emmanuel ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
Re: [scikit-learn] Bootstrapping in sklearn
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 : > >> 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 >> 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 > ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
Re: [scikit-learn] Bootstrapping in sklearn
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 : > 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 > 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
Re: [scikit-learn] Bootstrapping in sklearn
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 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
Re: [scikit-learn] Bootstrapping in sklearn
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
