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Most implementations of Bayesian additive regression trees (BART) one-hotencode categorical predictors, replacing each one with several binaryindicators, one for every level or category. Regression trees built with theseindicators partition the discrete set of categorical levels by repeatedlyremoving one level at a time. Unfortunately, the vast majority of partitionscannot be built with this strategy, severely limiting BART's ability topartially pool data across groups of levels. Motivated by analyses of baseballdata and neighborhood-level crime dynamics, we overcame this limitation byre-implementing BART with regression trees that can assign multiple levels toboth branches of a decision tree node. To model spatial data aggregated intosmall regions, we further proposed a new decision rule prior that createsspatially contiguous regions by deleting a random edge from a random spanningtree of a suitably defined network. Our re-implementation, which is availablein the flexBART package, often yields improved out-of-sample predictiveperformance and scales better to larger datasets than existing implementationsof BART.

flexBART: Flexible Bayesian regression trees with categorical predictors