Bayes Formula

To calculate the posterior probability, which is the probability of an event given previous evidence, Naive Bayes utilizes the Bayes formula. It calculates the probability of a certain class given the features, updating the probability as new evidence is gathered.

Types of Naive Bayes Algorithms

There are three common types of Naive Bayes algorithms:

1. Gaussian Naive Bayes: This variant is suitable for continuous numerical attributes and assumes that the features follow a Gaussian distribution.
2. Multinomial Naive Bayes: It is used for discrete features, often in the case of text classification. It assumes that the features are generated from a multinomial distribution.
3. Bernoulli Naive Bayes: This variant is also used for discrete features but assumes that all features are binary (i.e., presence or absence of a particular attribute).