For many years, machine learning methods have been used in a wide range of
fields, including computer vision and natural language processing. While
machine learning methods have significantly improved model performance over
traditional methods, their black-box structure makes it difficult for
researchers to interpret results. For highly regulated financial industries,
transparency, explainability, and fairness are equally, if not more, important
than accuracy. Without meeting regulated requirements, even highly accurate
machine learning methods are unlikely to be accepted. We address this issue by
introducing a novel class of transparent and interpretable machine learning
algorithms known as generalized gloves of neural additive models. The
generalized gloves of neural additive models separate features into three
categories: linear features, individual nonlinear features, and interacted
nonlinear features. Additionally, interactions in the last category are only
local. The linear and nonlinear components are distinguished by a stepwise
selection algorithm, and interacted groups are carefully verified by applying
additive separation criteria. Empirical results demonstrate that generalized
gloves of neural additive models provide optimal accuracy with the simplest
architecture, allowing for a highly accurate, transparent, and explainable
approach to machine learning.

介绍了一种透明且可解释的机器学习算法，应用于高度监管的金融行业中，能够提供最优准确度和可解释性。