论文提出部分可解释决策树：复杂输入可交给黑盒模型
Partially Interpretable Decision Trees
一篇把可解释性和精度做成滑块的论文：简单输入自己解释，难的交给黑盒。还证明了多数情况下普通 CART 就够了。
arXiv 论文 2611.06428 提出三种训练部分可解释决策树的算法：Branches 的部分可解释扩展、基于 CART 的转换算法、以及考虑延迟决策的 CART 泛化版本。在多个真实数据集上三种算法表现相近，作者用形式化上界解释了这一现象：当黑盒模型精度在输入域上足够均匀时，聪明的树构建带来的收益有限。在黑盒精度不均匀的合成数据集上，延迟感知版 CART 优于普通 CART，并接近精确搜索的表现。论文还展示了在 concept bottleneck models 中的应用，用可解释决策树替换瓶颈层之后的神经网络。
Partially Interpretable Decision Trees
Partially interpretable models extend simple interpretable models with the option to defer complicated inputs to an accurate but uninterpretable black-box model. By deferring more inputs, the model gets more accurate; by deferring fewer, it gets more interpretable. Existing procedures to train such partially interpretable models build on linear models or decision rules. We propose three algorithms for decision trees: a partially interpretable extension of the existing Branches algorithm, which performs exact search to optimize the tree; a conversion algorithm that we run on top of standard CART, which is computationally more efficient; and a generalized CART whose splitting criterion takes into account the option to defer while growing the tree. Perhaps surprisingly, the performance of the three algorithms is similar across many realworld datasets. We can explain this through a formal bound on the potential gain of smart tree construction, provided the black box accuracy is sufficiently homogeneous across the domain. We also construct a synthetic dataset where the accuracy of the black box is deliberately inhomogeneous. There, our deferral-aware generalization of CART does outperform regular CART and matches the performance of exact search. Finally, we evaluate an application to concept bottleneck models. Partially interpretable decision trees can replace uninterpretable neural networks after the bottleneck. On undeferred inputs, the model is fully interpretable, and deferring preserves accuracy.