Many methods to explain black-box models, whether local or global, are additive. In this paper, we study global additive explanations for non-additive models, focusing on four explanation methods: partial dependence, Shapley explanations adapted to a global setting, distilled additive explanations, and gradient-based explanations. We show that different explanation methods characterize non-additive components in a black-box model's prediction function in different ways. We use the concepts of main and total effects to anchor additive explanations, and quantitatively evaluate additive and non-additive explanations. Even though distilled explanations are generally the most accurate additive explanations, non-additive explanations such as tree explanations that explicitly model non-additive components tend to be even more accurate. Despite this, our user study showed that machine learning practitioners were better able to leverage additive explanations for various tasks. These considerations should be taken into account when considering which explanation to trust and use to explain black-box models.

翻译：许多解释黑盒模型的方法，无论是局部还是全局的，都具有可加性。本文研究了非可加模型的全局可加性解释，重点关注四种解释方法：部分依赖图、适应全局设置的沙普利解释、蒸馏可加性解释以及基于梯度的解释。我们表明，不同的解释方法以不同方式刻画黑盒模型预测函数中的非可加成分。我们使用主效应和总效应的概念来锚定可加性解释，并定量评估可加性与非可加性解释。尽管蒸馏解释通常是最准确的可加性解释，但明确建模非可加成分的非可加性解释（例如树解释）往往更为准确。尽管如此，我们的用户研究表明，机器学习从业者能更好地利用可加性解释完成各种任务。在选择信任并使用哪种解释来解释黑盒模型时，应充分考虑这些因素。