The naive Bayes classifier belongs to the family of probabilistic classifiers that computes the probabilities of each predictive feature (also called attribute) of the data belonging to each class in order to make a prediction of probability distribution over all classes, besides the most likely class that the data sample is associated with. And what makes it special is as its name indicates:
- Bayes: It maps the probabilities of observing input features given belonging classes, to the probability distribution over classes based on Bayes' theorem. We will explain Bayes' theorem by examples in the next section.
- Naive: It simplifies probability computations by assuming that predictive features are mutually independent.