用约束求解方法找模型无关解释，更准更快。
Learning Model Agnostic Explanations via Constraint Programming
- 把解释生成转为约束优化问题，自动找最小特征集
- 在多个数据集上显著优于当前最优的Anchors方法
- 适合需要可解释性且对精度要求高的场景
可解释机器学习面临的核心挑战是如何以人类可理解的方式解释集成模型、核方法或神经网络等黑箱模型的预测结果。当模型被视为黑箱时，目标是找到一组少量特征，联合决定黑箱输出且误差最小。然而，寻找此类模型无关解释在计算上非常困难，即使对二分类器也属不可行。本文将该任务建模为约束优化问题，由约束求解器针对输入样本及黑箱生成的样本集，寻找误差最小且规模受限的解释。理论上，该方法提供类似PAC的保证。实验表明，该方法在多个数据集上统计显著优于当前最先进的启发式方法Anchors。
原文摘要 · Abstract (English)
Interpretable Machine Learning faces a recurring challenge of explaining the predictions made by opaque classifiers such as ensemble models, kernel methods, or neural networks in terms that are understandable to humans. When the model is viewed as a black box, the objective is to identify a small set of features that jointly determine the black box response with minimal error. However, finding such model-agnostic explanations is computationally demanding, as the problem is intractable even for binary classifiers. In this paper, the task is framed as a Constraint Optimization Problem, where the constraint solver seeks an explanation of minimum error and bounded size for an input data instance and a set of samples generated by the black box. From a theoretical perspective, this constraint programming approach offers PAC-style guarantees for the output explanation. We evaluate the approach empirically on various datasets and show that it statistically outperforms the state-of-the-art heuristic Anchors method.
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