可解释性设计：条件可加局部模型 CALMs 兼顾 GAM 可解释性与 GA²Ms 精度

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Abstract:Generalized additive models (GAMs) offer high 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. We propose Conditionally Additive Local Models (CALMs), a new model class that narrows this trade-off: it stays close to GAMs in interpretability and close to GA$^2$Ms in accuracy. 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 broadly comparable to GA$^2$Ms, while preserving the univariate auditability that GA$^2$Ms forfeit.

Comments:	Accepted at NeurIPS 2026. 35 pages 10 figures. Code: this https URL
Subjects:	Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2602.16503 [cs.LG]
	(or arXiv:2602.16503v2 [cs.LG] for this version)
	https://doi.org/10.48550/arXiv.2602.16503 arXiv-issued DOI via DataCite

Submission history

From: Vasilis Gkolemis [view email]
[v1] Wed, 18 Feb 2026 14:45:33 UTC (350 KB)
[v2] Fri, 30 Oct 2026 17:53:59 UTC (459 KB)