用修正法将提升树蒸馏为可解释决策树，兼顾精度与可读性。
A Rectification-Based Approach for Distilling Boosted Trees into Decision Trees
- 通过修正机制实现提升树到决策树的蒸馏
- 相比重训练方法，预测性能更优且可解释性更强
- 适合需要模型透明度的高风险场景
我们提出一种新的方法，将提升树蒸馏为决策树，旨在生成在预测性能与可解释性之间取得良好平衡的机器学习模型。本文阐述了名为‘修正’（rectification）的方法如何用于实现这一蒸馏过程。实验表明，该方法在与通过重训练实现的蒸馏方法对比时，表现出更优的性能，同时保持了更高的可解释性。
原文摘要 · Abstract (English)
We present a new approach for distilling boosted trees into decision trees, in the objective of generating an ML model offering an acceptable compromise in terms of predictive performance and interpretability. We explain how the correction approach called rectification can be used to implement such a distillation process. We show empirically that this approach provides interesting results, in comparison with an approach to distillation achieved by retraining the model.
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