Under supervised learning, when the output variable is discrete or categorical instead of continuous, one has a classification problem instead of a regression problem. Several classification methods are covered: linear discriminant analysis, logistic regression, naive Bayes classifier, K-nearest neighbours, extreme learning machine classifier and multi-layer perceptron classifier. In classification, the cross-entropy objective function is often used in place of the mean squared error function.