Treed Gaussian Process Models for Classification

Abstract

Recognizing the success of the treed Gaussian process (TGP) model as an interpretable and thrifty model for nonstationary regression, we seek to extend the model to classification. By combining Bayesian CART and the latent variable approach to classification via Gaussian processes (GPs), we develop a Bayesian model averaging scheme to traverse the full space of classification TGPs (CTGPs). We illustrate our method on synthetic and real data and thereby show how the combined approach is highly flexible, offers tractable inference, produces rules that are easy to interpret, and performs well out of sample.