Interpretable Quantile Regression by Optimal Decision Trees
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Abstract (arXiv)
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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References · 6
- 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,713
- 2017Interpretable Explanations of Black Boxes by Meaningful Perturbationcited by 611
- 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
- 2018Techniques for Interpretable Machine Learningcited by 238
- 2015Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction modelcited by 140
- 2020MurTree: Optimal Classification Trees via Dynamic Programming and Searchcited by 63
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