Models and Selection Criteria for Regression and Classification
When performing regression or classification, we are interested in the conditional probability distribution for an outcome or class variable Y given a set of explanatory or input variables X. We consider Bayesian models for this task. In particular, we examine a special class of models, which we call Bayesian regression/classification (BRC) models, that can be factored into independent conditional (y[x) and input (x) models. These models are convenient, because the conditional model (the portion of the full model that we care about) can be analyzed by itself. We examine the practice of transforming arbitrary Bayesian models to BRC models, and argue that this practice is often inappropriate because it ignores prior knowledge that may be important for learning. In addition, we examine Bayesian methods for learning models from data. We discuss two criteria for Bayesian model selection that are appropriate for repression/classification: one described by Spiegelhalter etah (1993), and other by Buntine (1993). We contrast these two criteria using the prequentia] framework of Dawid (1984), and give sufficient conditions under which the criteria agree.