I understand Multinomial Naive Bayes is a specific instance of Naive Bayes when the data distribution is assumed to be multinomial.
In the sklearn documentation for Multinomial Naive Bayes, it is stated:
Naive Bayes classifier for multinomial models
The multinomial Naive Bayes classifier is suitable for classification with discrete features
(e.g., word counts for text classification). The multinomial distribution normally requires
integer feature counts. However, in practice, fractional counts such as tf-idf may also work.
I observed this to be true when working on a text classification project, the Multinomial Naive Bayes classifier had the best results among the others I tried. Can someone explain why it is suited for discrete features?