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. ...
When training my algorithm, if I can get some i.e. data my future test data that has no labels can it improve my algorithm's efficiency, is there any mathematical proof for it? PS: I think ...