  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,
model transparency is equally important to accuracy. Without understanding how
models work, even highly accurate machine learning methods are unlikely to be
accepted. We address this issue by introducing a novel class of transparent
machine learning models known as generalized groves of neural additive models.
The generalized groves 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. A stepwise selection algorithm distinguishes the linear and nonlinear
components, and interacted groups are carefully verified by applying additive
separation criteria. Through some empirical examples in finance, we demonstrate
that generalized grove of neural additive models exhibit high accuracy and
transparency with predominantly linear terms and only sparse nonlinear ones.
