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.

翻译：广义可加模型通过独立的单变量特征效应提供可解释性，但当数据中存在交互作用时会出现欠拟合。GA²M模型通过添加选定的成对交互项提升了准确性，但牺牲了可解释性并限制了模型可审计性。我们提出条件可加局部模型这一新模型类别，在GAMs的可解释性与GA²Ms的准确性之间取得平衡。CALMs允许每个特征具有多个单变量形状函数，每个函数在输入空间的不同区域激活。这些区域由每个特征与其交互特征间的简单逻辑条件独立定义。因此，效应在保持局部可加性的同时，能够通过子区域的变化捕捉交互作用。我们进一步提出基于蒸馏原理的训练流程，该流程通过区域感知反向拟合识别交互有限的同质区域并拟合可解释形状函数。在多样化分类与回归任务上的实验表明，CALMs始终优于GAMs，并达到与GA²Ms相当的准确性。总体而言，CALMs在预测准确性与可解释性之间提供了理想的权衡。