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Oblique Bayesian additive regression trees

arXiv 2411.08849 ↗First posted 13 Nov 2024stat.ML cs.LGcited by 0

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Abstract (arXiv)

Current implementations of Bayesian Additive Regression Trees (BART) are based on axis-aligned decision rules that recursively partition the feature space using a single feature at a time. Several authors have demonstrated that oblique trees, whose decision rules are based on linear combinations of features, can sometimes yield better predictions than axis-aligned trees and exhibit excellent theoretical properties. We develop an oblique version of BART that leverages a data-adaptive decision rule prior that recursively partitions the feature space along random hyperplanes. Using several synthetic and real-world benchmark datasets, we systematically compared our oblique BART implementation to axis-aligned BART and other tree ensemble methods, finding that oblique BART was competitive with -- and sometimes much better than -- those methods.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 48 references that are in the map. First posted on arXiv 13 Nov 2024.

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References · 18

  1. 2015ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and Rcited by 171
  2. 2017Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competitioncited by 106
  3. 2017Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effectscited by 41
  4. 2015Random-projection ensemble classificationcited by 29
  5. 2015Sparse Projection Oblique Randomer Forestscited by 15
  6. 2015Canonical Correlation Forestscited by 12
  7. 2022Convergence Rates of Oblique Regression Trees for Flexible Function Librariescited by 9
  8. 2018Semiparametric Mixed-Scale Models Using Shared Bayesian Forestscited by 8
  9. 2020Adaptive Conditional Distribution Estimation with Bayesian Decision Tree Ensemblescited by 7
  10. 2022GP-BART: a novel Bayesian additive regression trees approach using Gaussian processescited by 7
  11. 2019Linear Aggregation in Tree-based Estimatorscited by 5
  12. 2021Density Regression with Bayesian Additive Regression Treescited by 5
6 more
  1. 2023Model Mixing Using Bayesian Additive Regression Treescited by 4
  2. 2019Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networkscited by 3
  3. 2022flexBART: Flexible Bayesian regression trees with categorical predictorscited by 3
  4. 2017Heteroscedastic BART Using Multiplicative Regression Treescited by 2
  5. 2020BART-based inference for Poisson processescited by 2
  6. 2024Combining Climate Models using Bayesian Regression Trees and Random Pathscited by 1

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