The Naïve 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 (of course, including the most likely class that the data sample is associated with). What it does, as its name indicates, is as follows:
- Bayes: As in, 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 with the later examples in this chapter
- Naïve: As in, it simplifies probability computation by assuming that predictive features are mutually independent.
