Naive Bayes and Discriminant Analysis

Naive Bayes algorithms are a family of powerful and easy-to-train classifiers that determine the probability of an outcome given a set of conditions using Bayes' theorem. The dynamic is based on the inversion of the conditional probabilities (that are associated with the causes) so that the query can be expressed as a function of measurable quantities. The approach is simple, and the adjective naive has been attributed not because these algorithms are limited or less efficient, but because of a fundamental assumption about the causal factors that we're going to discuss. Naive Bayes algorithms are multi-purpose classifiers, and it's easy to find their application in many different contexts. However, their performance is particularly good in all those situations, where the probability of a class is determined by the probabilities...