Interpretable Quantile Regression by Optimal Decision Trees

2026-04-22

AI Summary

This study addresses the challenge of constructing regression models that are both accurate and interpretable without assuming a specific conditional distribution for the target variable. To this end, the authors propose a novel approach that leverages an optimal decision tree learning framework to jointly optimize a set of quantile regression trees, thereby capturing the full conditional distribution of the response variable. This method is the first to achieve a globally optimal, highly interpretable ensemble of quantile regression trees without requiring distributional priors, all while maintaining computational efficiency. Experimental results demonstrate that the proposed model effectively balances predictive accuracy and model transparency, offering a new theoretical and practical pathway for interpretable machine learning.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

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.

Problem

• interpretable machine learning
• quantile regression
• optimal decision trees
• conditional distribution prediction

Innovation

• interpretable machine learning
• quantile regression
• optimal decision trees
• conditional distribution estimation
• algorithmic efficiency