Naive Bayes Classifier Algorithm

A naive Bayes classifier is a member of a family of probabilistic classifiers that assign a class label to an observation by applying Bayes' theorem while assuming every feature is conditionally independent of every other feature given the class, an assumption that gives the method its name because it rarely holds exactly in real data. Despite this unrealistic independence assumption, naive Bayes classifiers train quickly through closed-form probability estimates rather than expensive iterative optimization, and remain competitive for tasks such as spam filtering and document classification. This description is adapted from Wikipedia contributors under CC BY-SA 4.0; changes were made. https://creativecommons.org/licenses/by-sa/4.0/