解决缺失值数据的可解释模型构建难题，保持高精度与模型简洁性。
Interpretable Generalized Additive Models for Datasets with Missing Values
- 引入缺失指示变量及其交互项，结合l0正则化保持模型稀疏
- 在多个数据集上精度优于或相当传统方法，稀疏性显著提升
- 适合需要高可解释性的医疗、金融等缺失数据场景
许多重要数据集包含一个或多个特征值缺失的样本。在存在缺失数据的情况下保持机器学习模型的可解释性极具挑战。单独或多重填补缺失值会复杂化模型从特征到标签的映射关系。另一方面，使用表示缺失性的指示变量会引入大量额外项，破坏稀疏性。我们提出M-GAM，一种稀疏的广义加性建模方法，通过l0正则化整合缺失指示变量及其交互项，同时保持模型稀疏。实验表明，M-GAM在准确率上与现有方法相当或更优，且相对于插补法或直接引入指示变量的方法，稀疏性有显著提升。
原文摘要 · Abstract (English)
Many important datasets contain samples that are missing one or more feature values. Maintaining the interpretability of machine learning models in the presence of such missing data is challenging. Singly or multiply imputing missing values complicates the model's mapping from features to labels. On the other hand, reasoning on indicator variables that represent missingness introduces a potentially large number of additional terms, sacrificing sparsity. We solve these problems with M-GAM, a sparse, generalized, additive modeling approach that incorporates missingness indicators and their interaction terms while maintaining sparsity through l0 regularization. We show that M-GAM provides similar or superior accuracy to prior methods while significantly improving sparsity relative to either imputation or naive inclusion of indicator variables.
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