Bayesian additive regression trees with model trees

Batista Do Prado, Estevao and Parnell, Andrew C and de Andrade Moral, Rafael (2021) Bayesian additive regression trees with model trees. Statistics and Computing, 31 (3). pp. 1-13. ISSN 0960-3174

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Abstract

Bayesian additive regression trees (BART) is a tree-based machine learning method that has been successfully applied to regression and classification problems. BART assumes regularisation priors on a set of trees that work as weak learners and is very flexible for predicting in the presence of nonlinearity and high-order interactions. In this paper, we introduce an extension of BART, called model trees BART (MOTR-BART), that considers piecewise linear functions at node levels instead of piecewise constants. In MOTR-BART, rather than having a unique value at node level for the prediction, a linear predictor is estimated considering the covariates that have been used as the split variables in the corresponding tree. In our approach, local linearities are captured more efficiently and fewer trees are required to achieve equal or better performance than BART. Via simulation studies and real data applications, we compare MOTR-BART to its main competitors. R code for MOTR-BART implementation is available at https://github.com/ebprado/MOTR-BART.

Item Type:
Journal Article
Journal or Publication Title:
Statistics and Computing
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
?? BAYESIAN NON-PARAMETRICBAYESIAN ADDITIVE REGRESSION TREESMARKOV CHAIN MONTE CARLO (MCMC)YES - EXTERNALLY FUNDEDNOCOMPUTATIONAL THEORY AND MATHEMATICSTHEORETICAL COMPUTER SCIENCESTATISTICS AND PROBABILITYSTATISTICS, PROBABILITY AND UNCERTAINTY ??
ID Code:
185749
Deposited By:
Deposited On:
13 Feb 2023 11:00
Refereed?:
Yes
Published?:
Published
Last Modified:
12 Oct 2023 16:45