CALMs模型在保持可解释性的同时，提升对特征交互的建模能力。
Interpretability-by-Design with Accurate Locally Additive Models and Conditional Feature Effects
- 为每个特征设计分区域的局部加性函数，通过逻辑条件划分输入空间。
- 在分类与回归任务中，精度超越GAMs，接近GA²Ms水平。
- 适合需要可解释性与高精度平衡的机器学习应用。
广义加性模型（GAMs）通过独立的一元特征效应提供可解释性，但在存在特征交互时欠拟合。GA²Ms引入选定的成对交互项以提高精度，但牺牲了可解释性并限制了模型审计。本文提出一种新模型类——条件加性局部模型（CALMs），在保持GAMs可解释性的同时，实现接近GA²Ms的精度。CALMs允许每个特征拥有多个一元形状函数，每个函数在输入空间的不同子区域激活，这些区域由其交互特征的简单逻辑条件（如阈值）定义。因此，模型在局部仍保持加性，同时可在不同子区域变化以捕捉交互作用。我们进一步提出基于提炼的训练流程，通过区域感知的回代方法识别出交互有限的同质区域，并拟合可解释的形状函数。在多种分类和回归任务上的实验表明，CALMs始终优于GAMs，在精度上接近GA²Ms。总体而言，CALMs在预测精度与可解释性之间提供了极具吸引力的权衡。
原文摘要 · Abstract (English)
Generalized additive models (GAMs) offer interpretability through independent univariate feature effects but underfit when interactions are present in data. GA$^2$Ms add selected pairwise interactions which improves accuracy, but sacrifices interpretability and limits model auditing. We propose \emph{Conditionally Additive Local Models} (CALMs), a new model class, that balances the interpretability of GAMs with the accuracy of GA$^2$Ms. CALMs allow multiple univariate shape functions per feature, each active in different regions of the input space. These regions are defined independently for each feature as simple logical conditions (thresholds) on the features it interacts with. As a result, effects remain locally additive while varying across subregions to capture interactions. We further propose a principled distillation-based training pipeline that identifies homogeneous regions with limited interactions and fits interpretable shape functions via region-aware backfitting. Experiments on diverse classification and regression tasks show that CALMs consistently outperform GAMs and achieve accuracy comparable with GA$^2$Ms. Overall, CALMs offer a compelling trade-off between predictive accuracy and interpretability.
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