AI RESEARCH
Interpretable Quantile Regression by Optimal Decision Trees
arXiv CS.LG
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ArXi:2604.21042v1 Announce Type: new 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.