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Abstract:
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In ``BART'' (Bayesian Additive Regression Trees) Chipman, George, and McCulloch developed a fully Bayesian approach to the model: y = f(x) + e, where the errors may be drawn from any symmetric distribution. In the spirit of ``ensemble models'' the unknown function $f$ was modeled as the sum of many simple tree models. The contribution of each tree was kept small through the use of a strong regularization prior. The BART methodology was shown to be very competitive in terms of out-of-sample prediction. However, the BART model, prior, and MCMC algorithm are all geared toward the case where the response is numeric. In this talk, we explore the use of the BART methodology in classification problems. Different approaches to extending BART to classification are discussed.
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