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Naive Bayes for text comes in two main variants. Picking the right one between Multinomial and Bernoulli can quietly boost your accuracy.

The Multinomial model cares how many times each word appears. A word that shows up five times counts as stronger evidence than one that appears once.

The Bernoulli model only asks whether a word is present or absent. Repeats do not matter; each word is a simple yes-or-no signal.

Multinomial reads frequency, while Bernoulli reads presence. That single distinction shapes which model fits your text best.

Bernoulli also treats a missing word as meaningful evidence. The absence of free can itself nudge a message toward the ham class.

For longer documents where word counts carry real signal, reach for MultinomialNB. It is the usual default for topic and document sorting.

For short snippets like tweets or subject lines, presence matters more than counts. Here BernoulliNB often wins.

Bernoulli expects binary features, so it can binarize counts for you. Multinomial wants the raw counts straight from your vectorizer.

You may see GaussianNB too, but it expects continuous numbers, not word counts. Skip it for plain text features.

Both variants share the exact same scikit-learn API. You just swap the class name, then call fit and predict as usual. 🔄

When unsure, train both and compare on a held-out set. The model with higher accuracy on real data is the right call.

Which model best fits very short texts where presence matters most?

You compared Multinomial counts against Bernoulli presence, learned when each shines, and saw they share one API. Try both and let data choose. ✅