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: