Bayes classifier

In statistical classification, the Bayes classifier minimizes the probability of misclassification. Suppose a pair takes values in , where is the class label of . Assume that the conditional distribution of X, given that the label Y takes the value r is given by for where "" means "is distributed as", and where denotes a probability distribution. A classifier is a rule that assigns to an observation X=x a guess or estimate of what the unobserved label Y=r actually was. In theoretical terms, a classifier is a measurable function , with the interpretation that C classifies the point x to the class C(x). The probability of misclassification, or risk, of a classifier C is defined as The Bayes classifier is In practice, as in most of statistics, the difficulties and subtleties are associated with modeling the probability distributions effectively—in this case, . The Bayes classifier is a useful benchmark in statistical classification. The excess risk of a general classifier (possibly depending on some training data) is defined as Thus this non-negative quantity is important for assessing the performance of different classification techniques. A classifier is said to be consistent if the excess risk converges to zero as the size of the training data set tends to infinity. Considering the components of to be mutually independent, we get the naive bayes classifier, where Proof that the Bayes classifier is optimal and Bayes error rate is minimal proceeds as follows. Define the variables: Risk , Bayes risk , all possible classes to which the points can be classified . Let the posterior probability of a point belonging to class 1 be . Define the classifier as Then we have the following results: (a) , i.e. is a Bayes classifier, (b) For any classifier , the excess risk satisfies (c) Proof of (a): For any classifier , we have (due to Fubini's theorem) Notice that is minimised by taking , Therefore the minimum possible risk is the Bayes risk, .