https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.MultinomialNB.html

Multinomial Naive Bayes Scikit Reference

With a multinomial model, samples represent the frequencies where certain events have been generated by a multinomial. Typically used for document classification, where there are discrete features like word counts.

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The primary types of Naive Bayes classifiers are Bernoulli Naive Bayes, Multinomial Naive Bayes, and Gaussian Naive Bayes.

Types of Naive Bayes Classifiers

In the Bernoulli model, features are independent booleans/binary variables describing inputs. This model is popular for document classification tasks such as word presence, where binary term occurrence features are used rather than term frequencies.

Bernoulli Naive Bayes

 When dealing with continuous data, a typical assumption is that the continuous/real-valued features associated with each class are distributed according to a normal (or Gaussian) distribution. Statistics computed by segmenting the data by the class, and then computing the mean and variance of the continuous attributes in each class.

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