Interpretable Generalized Additive Models for Datasets with Missing Values
This addresses the problem of interpretable modeling for datasets with missing values, which is incremental as it builds on existing generalized additive models.
The paper tackles the challenge of maintaining interpretability in machine learning models when datasets have missing values, proposing M-GAM, a sparse generalized additive model that incorporates missingness indicators with l0 regularization, achieving similar or better accuracy than prior methods while significantly improving sparsity.
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.