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
