While interpretability is crucial for machine learning applications in safety-critical domains and for regulatory compliance, existing tabular foundation models like TabPFN lack transparency. Generalized Additive Models (GAMs) provide the needed interpretability through their additive structure, but traditional GAM methods rely on iterative learning algorithms (such as splines, boosted trees, or neural networks) that are fundamentally incompatible with the in-context learning paradigm of foundation models. In this paper, we introduce GAMformer, the first tabular foundation model for GAMs that bridges the gap between the power of foundation models and the interpretability requirements of critical real-world applications. GAMformer estimates GAM shape functions in a single forward pass using in-context learning, representing a significant departure from conventional iterative approaches. Building on previous research on tabular foundation models, we train GAMformer exclusively on synthetically generated tables to prevent data leakage. Our experiments demonstrate that GAMformer performs comparably to other leading GAMs across various classification benchmarks.

翻译：尽管可解释性对于安全关键领域的机器学习应用及合规监管至关重要，但现有表格基础模型（如TabPFN）缺乏透明度。广义可加模型（GAMs）通过其可加结构提供了必要的可解释性，但传统GAM方法依赖于迭代学习算法（如样条、提升树或神经网络），这些算法与基础模型的上下文学习范式存在根本性不兼容。本文提出GAMformer——首个面向GAM的表格基础模型，它弥合了基础模型的强大能力与关键实际应用的可解释性需求之间的鸿沟。GAMformer利用上下文学习在单次前向传播中估计GAM形状函数，这标志着对传统迭代方法的重大突破。基于先前对表格基础模型的研究，我们仅使用合成生成的表格训练GAMformer以防止数据泄露。实验表明，GAMformer在多种分类基准测试中与其他主流GAM方法性能相当。