Is it true that Multinomial Naive Bayes requires equally by count training data for each class to get best performance?
For example, we forming classifier for three classes - Japan, China, Korea.
For Japan available 500 training data, China - 300, Korea - 100.
So to train Multinomial Naive Bayes we need to get only 100 data for each class inspite of we have 500 for Japan, 300 for China.
I know that we can train Naive Bayes with all available data, but in this case we got worse performance, is it true?