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.

该论文介绍了可解释的机器学习算法的重要性，重点讨论了低阶函数 ANOVA 模型框架以及解释性强的算法，例如 Explainable Boosting Machines 和 GAMI-Lin-T，并使用数据集比较了算法性能。