I am running some experiments using word embedding features with Multinomial and Gaussian Naive Bayes in Scikit learn. As far as I know, Multinomial Naive Bayes works on features with distribution like word frequencies, it may work with tf-idf as well (according to Scikit learn documentation). On the other hand in Gaussian Naive Bayes the data distribution in features is assumed to be a normal distribution and the values can be continuous.

I was surprised to see Multinomial NB performed better than Gaussian NB in a multilabel textmining task with a OneVsAll classifier. I am not sure why. Is their anyone who can put an insight. Also, can tell me what NB should be used with Word Embedding features?

  • $\begingroup$ What do you mean by "performed better"? Maybe it just overfitted? $\endgroup$ – Tim Dec 5 '17 at 22:46
  • $\begingroup$ By performance I meant, avg F-measure on 5 fold CV. Also, why multinomial NB should work with this kind of data? $\endgroup$ – MiNdFrEaK Dec 5 '17 at 23:02

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