Probabilistic models for classification

In machine learning, classification is considered an instance of the supervised learning methods, i.e., inferring a function from labeled training data. The training data consist of a set of training examples, where each example is a pair consisting of an input object (typically a vector) x = <x₁,x₂,…,x_d> and a desired output value (typically a class label) y ∊ {C₁,C₂,…,C_K}. Given such a set of training data, the task of a classification algorithm is to analyze the training data and produce an inferred function, which can be used to classify new (so far unseen) examples by assigning a correct class label to each of them. An example would be assigning a given email into “spam” or “non-spam” classes.