In this paper we propose a method for the optimal allocation of observations between an intrinsically explainable glass box model and a black box model. An optimal allocation being defined as one which, for any given explainability level (i.e. the proportion of observations for which the explainable model is the prediction function), maximizes the performance of the ensemble on the underlying task, and maximizes performance of the explainable model on the observations allocated to it, subject to the maximal ensemble performance condition. The proposed method is shown to produce such explainability optimal allocations on a benchmark suite of tabular datasets across a variety of explainable and black box model types. These learned allocations are found to consistently maintain ensemble performance at very high explainability levels (explaining $74\%$ of observations on average), and in some cases even outperforming both the component explainable and black box models while improving explainability.

翻译：本文提出一种在本质可解释的透明盒模型与黑盒模型之间实现观测值最优分配的方法。最优分配的定义为：在任意给定可解释性水平（即可解释模型作为预测函数的观测值比例）下，该分配能最大化集成模型在底层任务上的性能，并在满足集成性能最大化条件的前提下，最大化可解释模型在其分配观测值上的性能。实验表明，所提方法能在多种可解释模型与黑盒模型类型的表格数据集基准测试中生成此类可解释性最优分配。研究发现，通过学习得到的分配方案能在极高可解释性水平下（平均可解释$74\%$的观测值）持续保持集成模型性能，在某些情况下甚至能超越组成该集成的可解释模型与黑盒模型，同时提升可解释性。