Generalized Additive Models (GAMs) are widely used explainable-by-design
models in various applications. GAMs assume that the output can be represented
as a sum of univariate functions, referred to as components. However, this
assumption fails in ML problems where the output depends on multiple features
simultaneously. In these cases, GAMs fail to capture the interaction terms of
the underlying function, leading to subpar accuracy. To (partially) address
this issue, we propose Regionally Additive Models (RAMs), a novel class of
explainable-by-design models. RAMs identify subregions within the feature space
where interactions are minimized. Within these regions, it is more accurate to
express the output as a sum of univariate functions (components). Consequently,
RAMs fit one component per subregion of each feature instead of one component
per feature. This approach yields a more expressive model compared to GAMs
while retaining interpretability. The RAM framework consists of three steps.
Firstly, we train a black-box model. Secondly, using Regional Effect Plots, we
identify subregions where the black-box model exhibits near-local additivity.
Lastly, we fit a GAM component for each identified subregion. We validate the
effectiveness of RAMs through experiments on both synthetic and real-world
datasets. The results confirm that RAMs offer improved expressiveness compared
to GAMs while maintaining interpretability.

Regionally Additive Models (RAMs) 是一种新型的可解释设计模型，能够在捕获多个特征之间的交互项的同时，保持可解释性。通过在特征空间中确定减少交互的子区域，并为每个识别出的子区域拟合一个 GAM 成分，RAMs 能提供比普通的广义可加性模型（GAMs）更出色的表达能力和保持可解释性。

区域可加性模型：最小化特征相互作用的可解释性设计模型

Regionally Additive Models: Explainable-by-design models minimizing  feature interactions

Deployment of machine learning models in real high-risk settings (e.g.
healthcare) often depends not only on the model's accuracy but also on its
fairness, robustness, and interpretability. Generalized Additive Models (GAMs)
are a class of interpretable models with a long history of use in these
high-risk domains, but they lack desirable features of deep learning such as
differentiability and scalability. In this work, we propose a neural GAM
(NODE-GAM) and neural GA$^2$M (NODE-GA$^2$M) that scale well and perform better
than other GAMs on large datasets, while remaining interpretable compared to
other ensemble and deep learning models. We demonstrate that our models find
interesting patterns in the data. Lastly, we show that we improve model
accuracy via self-supervised pre-training, an improvement that is not possible
for non-differentiable GAMs.

本文提出了一种神经广义加性模型（NODE-GAM）和神经广义加性和模型（NODE-GA^2M），这种模型相对于其他集成和深度学习模型而言更具有可解释性，且可以自监督预训练以提高模型准确性。

NODE-GAM: 神经广义加性模型用于可解释的深度学习

NODE-GAM: Neural Generalized Additive Model for Interpretable Deep  Learning

Standard autoregressive seq2seq models are easily trained by max-likelihood,
but tend to show poor results under small-data conditions. We introduce a class
of seq2seq models, GAMs (Global Autoregressive Models), which combine an
autoregressive component with a log-linear component, allowing the use of
global \textit{a priori} features to compensate for lack of data. We train
these models in two steps. In the first step, we obtain an \emph{unnormalized}
GAM that maximizes the likelihood of the data, but is improper for fast
inference or evaluation. In the second step, we use this GAM to train (by
distillation) a second autoregressive model that approximates the
\emph{normalized} distribution associated with the GAM, and can be used for
fast inference and evaluation. Our experiments focus on language modelling
under synthetic conditions and show a strong perplexity reduction of using the
second autoregressive model over the standard one.

介绍了一个基于 seq2seq 模型的 GAM（Global Autoregressive Models）的类，将自回归组件与对数线性组件相结合，在小数据条件下使用全局先验特征来补偿数据不足，并使用两个步骤来训练此模型，实验表明，使用第二个自回归模型可以强有力地减少混乱度。