Abstract:The field of machine learning is subject to an increasing interest in models that are not only accurate but also interpretable and robust, thus allowing their end users to understand and trust AI systems. This paper presents a novel method for learning a set of optimal quantile regression trees. The advantages of this method are that (1) it provides predictions about the complete conditional distribution of a target variable without prior assumptions on this distribution; (2) it provides predictions that are interpretable; (3) it learns a set of optimal quantile regression trees without compromising algorithmic efficiency compared to learning a single tree.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.21042 [cs.LG] |
| (or arXiv:2604.21042v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.21042 arXiv-issued DOI via DataCite (pending registration) |
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| Related DOI: | https://doi.org/10.1007/978-3-031-58553-1_17
DOI(s) linking to related resources |
From: Valentin Lemaire [view email]
[v1]
Wed, 22 Apr 2026 19:40:16 UTC (424 KB)
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