Interpretable Quantile Regression by Optimal Decision Trees
It addresses the need for interpretable and robust models that offer distributional predictions, benefiting practitioners who require trustworthy AI systems.
The paper introduces a method for learning optimal quantile regression trees that provide interpretable predictions of the full conditional distribution of a target variable, achieving algorithmic efficiency comparable to learning a single tree.
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.