Bayesian classifiers find the distribution of attribute values for each class
in the training data; when given a new instance d, they use the distribution
information to estimate, for each class c j , the probability that instance d belongs
to class c j , denoted by p(c j |d), in a manner outlined here. The classwithmaximum
probability becomes the predicted class for instance d.