为随机森林和提升树提供逻辑严谨的预测解释方法
Rigorous Explanations for Tree Ensembles
- 基于逻辑推理构建可信赖的解释框架
- 确保解释严格反映模型内在决策机制
- 适合需透明决策的高风险应用领域
树集成（Tree Ensembles, TEs）在众多实际场景中广泛应用，是通用且准确度高的机器学习方法之一。尽管其表示简洁，但对人类决策者而言仍难以理解。建立对树集成信任的一种方式是自动生成其预测的解释。然而，只有当解释真正反映所解释模型的内在属性时，才能实现可信。本文研究了两类典型树集成——随机森林和提升树——的严格定义、逻辑上合理的解释计算方法。
原文摘要 · Abstract (English)
Tree ensembles (TEs) find a multitude of practical applications. They represent one of the most general and accurate classes of machine learning methods. While they are typically quite concise in representation, their operation remains inscrutable to human decision makers. One solution to build trust in the operation of TEs is to automatically identify explanations for the predictions made. Evidently, we can only achieve trust using explanations, if those explanations are rigorous, that is truly reflect properties of the underlying predictor they explain This paper investigates the computation of rigorously-defined, logically-sound explanations for the concrete case of two well-known examples of tree ensembles, namely random forests and boosted trees.
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