Bayesian classifier: introduction A Bayesian classifier is based on idea that can predict values of features for members of learned classes. In data set patterns are grouped in classes because they have common features. Such classes are often called natural kinds. The idea behind a Bayesian classifier is that, if someone knows the classes, it can predict values of similar other patterns. If it does not know the class, Bayes’ rule can be used to predict the class given according to attributes. In a Bayesian classifier, the learning model is a probabilistic model of attributes and that predict the classification labels of a new similar Pattern. A latent variable is a probabilistic variable that is not observed. A Bayesian classifier is a probabilistic model where the classification is a latent variable that is probabilistically related to the observed variables. Classification then becomes inference in the probabilistic model. Naive Bayes is a family of probabilistic algorithms that take advantage of probability theory and Bayes’ Theorem to predict the category of a sample (like a piece of news or a customer review). They are probabilistic, which means that they calculate the probability of each category for a given sample, and then output the category with the highest one. The…