Machine learning methods have seen a meteoric rise in their applications in the scientific community. However, little effort has been put into understanding these "black box" models. We show how one can apply integrated gradients (IGs) to understand these models by designing different baselines, by taking an example case study in particle physics. We find that the zero-vector baseline does not provide good feature attributions and that an averaged baseline sampled from the background events provides consistently more reasonable attributions.

翻译：机器学习方法在科学界的应用呈现爆发式增长。然而，理解这些"黑箱"模型的研究投入甚少。本文以粒子物理学的案例研究为例，展示了如何通过设计不同基准来应用集成梯度（IGs）以理解这些模型。研究发现，零向量基准无法提供良好的特征归因，而从背景事件中采样的平均基准则能持续提供更合理的归因结果。