I am doing text classification but I am confused which Naive Bayes model I should use. What I understood by reading answers from couple of places that Gaussian Naive Bayes can be used if the attribute values are continuous, when attribute values are binary, binomial Naive Bayes can be used, for examples if we have words as features, we look into each sample to see if that word is present or not and thats how we get a matrix of S (sample) * V(vocubulary of words) dimension for text classification. Now, if we had actual word counts for creating S * V matrix, we would use multinomial Naive Bayes. My question is, if we use tf-idf (which has continuous/fraction value) for S * V matrix, which Naive Bayes Classification model should we use?
Am I getting conceptually wrong idea of data distribution?