Hello,

While trying to understand the different distributions for which NaiveBayes can be used I am not being able to understand something which I will outline below with a dataset example:

If our independent variable has only two levels(like in the red box) we say it is a bernoulli model.

If it is like in the red circle it is gaussian and if the y has several levels(more than 2) we say it is a multi-nomial model??

I understand that for text classification if we use binary representation it is bernoulli model and if we use count representation it is multinomial,but I am getting confused in case of datasets such as the one shown above.

Can someone please clarify if my understanding is correct,and kindly rectify me if I am wrong.