ABSTRACT
A common subclass of classification is probabilistic classification, and in this chapter we will focus on some probabilistic classification methods. Probabilistic classification algorithms use statistical inference to find the best class for a given example. In addition to simply assigning the best class like other classification algorithms, probabilistic classification algorithms will output a corresponding probability of the example being a member of each of the possible classes. The class with the highest probability is normally then selected as the best class. In general, probabilistic classification algorithms has a few advantages over non-probabilistic classifiers: First, it can output a confidence value (i.e., probability) associated with its selected class label, and therefore it can abstain if its confidence of choosing any particular output is too low. Second, probabilistic classifiers can be more effectively incorporated into larger machine learning tasks, in a way that partially or completely avoids the problem of error propagation.
