Oblique Bayesian additive regression trees
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915018365206528 |
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| author | Nguyen, Paul-Hieu V. Yee, Ryan Deshpande, Sameer K. |
| author_facet | Nguyen, Paul-Hieu V. Yee, Ryan Deshpande, Sameer K. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_08849 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Oblique Bayesian additive regression trees Nguyen, Paul-Hieu V. Yee, Ryan Deshpande, Sameer K. Machine Learning 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. |
| title | Oblique Bayesian additive regression trees |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2411.08849 |