Summary of Oblique Bayesian Additive Regression Trees, by Paul-hieu V. Nguyen and Ryan Yee and Sameer K. Deshpande
Oblique Bayesian additive regression trees
by Paul-Hieu V. Nguyen, Ryan Yee, Sameer K. Deshpande
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a new approach to Bayesian Additive Regression Trees (BART) by incorporating oblique trees into the model. Unlike traditional axis-aligned decision rules, oblique trees use linear combinations of features to partition the feature space. The authors develop an oblique BART implementation that leverages a data-adaptive decision rule prior and demonstrate its effectiveness on several benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making a machine learning tool called Bayesian Additive Regression Trees (BART) better by changing how it makes decisions. Instead of looking at one feature at a time, the new version looks at combinations of features to make more accurate predictions. The researchers tested this new method and found that it works well on many different types of data. |
Keywords
* Artificial intelligence * Machine learning * Regression
