Naive Bayes

Naive Bayes 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. In other words, the conditional probabilities are inverted, 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 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 of some causal factors. A good example is natural language processing, where a piece...