Sounds good, Sebastian. Thank you! Raga
On Fri, Jul 21, 2017 at 2:52 PM, 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 >
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