I'm currently learning Naive Bayes, I kinda understood how useful is NB in solving text classification problem. Also I guess I have some idea when to use Bernoulli & Multinomial in the context of text classification.

For instance, we use Bernoulli when only the presence or absence of the word matters and frequencies are irrelevant and go with Multinomial when even the frequencies of the words are to be taken in to consideration. But my main question is, how to choose the right variant of NB in other non-text classification problems? For instance, in this mushroom classification [https://www.kaggle.com/datasets/uciml/mushroom-classification](https://www.kaggle.com/datasets/uciml/mushroom-classification) problem all the features are categorical? How should I transform this data for NB? Should I use get\_dummies …

bayes classification context datascience learning multinomial text text classification