The right thing to do would probably be to write a scikit-learn-contrib package for them and see if they gather traction. If they perform well on eg kaggle competitions, we know that we need them in :).
Cheers, Gaël On Fri, Jul 21, 2017 at 07:09:03PM -0400, Sebastian Raschka wrote: > Maybe because they are genetic algorithms, which are -- for some reason -- > not very popular in the ML field in general :P. (People in bioinformatics > seem to use them a lot though.). Also, the name "Learning Classifier Systems" > is also a bit weird I'd must say: I remember that when Ryan introduced me to > those, I was like "ah yeah, sure, I know machine learning classifiers" ;) > > On Jul 21, 2017, at 3:01 PM, Stuart Reynolds <stu...@stuartreynolds.net> > > wrote: > > +1 > > LCS and its many many variants seem very practical and adaptable. I'm > > not sure why they haven't gotten traction. > > Overshadowed by GBM & random forests? > > On Fri, Jul 21, 2017 at 11:52 AM, Sebastian Raschka > > <se.rasc...@gmail.com> wrote: > >> Just to throw some additional ideas in here. Based on a conversation with > >> a colleague some time ago, I think learning classifier systems > >> (https://en.wikipedia.org/wiki/Learning_classifier_system) are > >> particularly useful when working with large, sparse binary vectors (like > >> from a one-hot encoding). I am really not into LCS's, and only know the > >> basics (read through the first chapters of the Intro to Learning > >> Classifier Systems draft; the print version will be out later this year). > >> Also, I saw an interesting poster on a Set Covering Machine algorithm > >> once, which they benchmarked against SVMs, random forests and the like for > >> categorical (genomics data). Looked promising. > >> Best, > >> Sebastian > >>> On Jul 21, 2017, at 2:37 PM, Raga Markely <raga.mark...@gmail.com> wrote: > >>> Thank you, Jacob. Appreciate it. > >>> Regarding 'perform better', I was referring to better accuracy, > >>> precision, recall, F1 score, etc. > >>> Thanks, > >>> Raga > >>> On Fri, Jul 21, 2017 at 2:27 PM, Jacob Schreiber > >>> <jmschreibe...@gmail.com> wrote: > >>> Traditionally tree based methods are very good when it comes to > >>> categorical variables and can handle them appropriately. There is a > >>> current WIP PR to add this support to sklearn. I'm not exactly sure what > >>> you mean that "perform better" though. Estimators that ignore the > >>> categorical aspect of these variables and treat them as discrete will > >>> likely perform worse than those that treat them appropriately. > >>> On Fri, Jul 21, 2017 at 8:11 AM, Raga Markely <raga.mark...@gmail.com> > >>> wrote: > >>> Hello, > >>> I am wondering if there are some classifiers that perform better for > >>> datasets with categorical features (converted into sparse input matrix > >>> with pd.get_dummies())? The data for the categorical features are nominal > >>> (order doesn't matter, e.g. country, occupation, etc). > >>> If you could provide me some references (papers, books, website, etc), > >>> that would be great. > >>> Thank you very much! > >>> Raga > >>> _______________________________________________ > >>> 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 > > _______________________________________________ > > 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 -- Gael Varoquaux Researcher, INRIA Parietal NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France Phone: ++ 33-1-69-08-79-68 http://gael-varoquaux.info http://twitter.com/GaelVaroquaux _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
