用最优决策树做分位数回归，既准又懂。
Interpretable Quantile Regression by Optimal Decision Trees
- 基于最优决策树学习多分位数预测
- 无需假设分布即可完整估计目标变量条件分布
- 模型可解释性强，效率不输单棵树
机器学习领域对既准确又可解释、鲁棒的模型需求日益增长，以增强用户对AI系统的理解和信任。本文提出一种学习一组最优分位数回归树的新方法。该方法优势在于：（1）无需预先假设目标变量分布，即可提供其完整条件分布的预测；（2）预测结果具有可解释性；（3）在不牺牲算法效率的前提下，学习一组最优分位数回归树，性能优于传统单棵树方法。
原文摘要 · Abstract (English)
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.
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