In the early days of machine learning (ML), the emphasis was on developing complex algorithms to achieve best predictive performance. To understand and explain the model results, one had to rely on post hoc explainability techniques, which are known to have limitations. Recently, with the recognition that interpretability is just as important, researchers are compromising on small increases in predictive performance to develop algorithms that are inherently interpretable. While doing so, the ML community has rediscovered the use of low-order functional ANOVA (fANOVA) models that have been known in the statistical literature for some time. This paper starts with a description of challenges with post hoc explainability and reviews the fANOVA framework with a focus on main effects and second-order interactions. This is followed by an overview of two recently developed techniques: Explainable Boosting Machines or EBM (Lou et al., 2013) and GAMI-Net (Yang et al., 2021b). The paper proposes a new algorithm, called GAMI-Lin-T, that also uses trees like EBM, but it does linear fits instead of piecewise constants within the partitions. There are many other differences, including the development of a new interaction filtering algorithm. Finally, the paper uses simulated and real datasets to compare selected ML algorithms. The results show that GAMI-Lin-T and GAMI-Net have comparable performances, and both are generally better than EBM.

翻译：在机器学习早期，重点在于开发复杂算法以实现最佳预测性能。为了理解和解释模型结果，研究人员不得不依赖后验可解释性技术，但这些技术存在局限性。近年来，随着可解释性与预测性能同等重要的认知被广泛接受，研究者开始接受预测性能的小幅下降，转而开发具有内在可解释性的算法。在此过程中，机器学习社区重新发现了在统计学文献中早已存在的低阶函数化方差分析（fANOVA）模型的应用价值。本文首先阐述了后验可解释性面临的挑战，并重点回顾了主效应与二阶交互效应的fANOVA框架，继而概述了两种近期开发的技术：可解释增强机（EBM，Lou等人，2013）与GAMI-Net（Yang等人，2021b）。本文提出了一种新算法GAMI-Lin-T，该算法虽与EBM同样使用树模型，但在划分区域内采用线性拟合而非分段常数。此外，二者还存在诸多差异，包括新型交互过滤算法的开发。最后，本文利用模拟数据集与真实数据集对所选机器学习算法进行了比较。结果表明，GAMI-Lin-T与GAMI-Net性能相当，且均整体优于EBM。