Interpretable Generalized Additive Models for Datasets with Missing Values

TL;DR

This paper introduces M-GAM, a sparse generalized additive model that effectively handles missing data by incorporating missingness indicators and interactions, achieving high interpretability and accuracy.

Contribution

M-GAM is a novel sparse modeling approach that integrates missingness indicators with interaction terms using l0 regularization, enhancing interpretability and performance.

Findings

M-GAM achieves comparable or better accuracy than existing methods.

M-GAM significantly improves model sparsity.

M-GAM effectively handles datasets with missing values.

Abstract

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.