2014-06-11 15:54 GMT+02:00 Gavin Gray <[email protected]>: > I need to use Naive Bayes for mixed categorial and numerical data and was > thinking of implementing a flexible Naive Bayes algorithm similar to Weka's > instead of hacking my way around by converting the numerical to categorical > or similar. Is there a good reason I shouldn't do this? Is anyone else > interested in having this functionality? Or does anyone have any other > comments?
I've thought about such a FrankensteinNB but never really found it worthwhile. The API becomes complicated because you have to specify which features follow which event model (and what would the model attributes look like?). When dealing with mixed event models, I just switch to discriminative classifiers, i.e. LinearSVC or LogisticRegression. ------------------------------------------------------------------------------ HPCC Systems Open Source Big Data Platform from LexisNexis Risk Solutions Find What Matters Most in Your Big Data with HPCC Systems Open Source. Fast. Scalable. Simple. Ideal for Dirty Data. Leverages Graph Analysis for Fast Processing & Easy Data Exploration http://p.sf.net/sfu/hpccsystems _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
