## What is the difference between Gaussian, Multinomial and Bernoulli Naïve Bayes classifiers?

**Gaussian Naive Bayes**

Gaussian Naive Bayes is useful when working with continuous values which probabilities can be modeled using a Gaussian distribution:

**Multinomial naive Bayes**

A multinomial distribution is useful to model feature vectors where each value represents, for example, the number of occurrences of a term or its relative frequency. If the feature vectors have *n *elements and each of them can assume *k *different values with probability *pk*, then:

**Bernoulli naive Bayes**

If X is random variable Bernoulli-distributed, it can assume only two values (for simplicity, let’s call them 0 and 1) and their probability is:

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