# Interpretable Generalized Additive Models for Datasets with Missing Values

arXiv: https://arxiv.org/abs/2412.02646 · HTML: https://mlsanity.com/paper/2412.02646/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 50 references that are in the map.
- First posted on arXiv 3 Dec 2024.

## Builds on (most cited first, 15 of 15)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [What's a good imputation to predict with missing values?](https://mlsanity.com/paper/2106.00311/) (2021)
- [Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms](https://mlsanity.com/paper/1803.01454/) (2018)
- [Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives](https://mlsanity.com/paper/2001.06471/) (2020)
- [Classification of datasets with imputed missing values: does imputation quality matter?](https://mlsanity.com/paper/2206.08478/) (2022)
- [Linear predictor on linearly-generated data with missing values: non consistency and solutions](https://mlsanity.com/paper/2002.00658/) (2020)
- [Fast Sparse Classification for Generalized Linear and Additive Models](https://mlsanity.com/paper/2202.11389/) (2022)
- [The Missing Indicator Method: From Low to High Dimensions](https://mlsanity.com/paper/2211.09259/) (2022)
- [L0Learn: A Scalable Package for Sparse Learning using L0 Regularization](https://mlsanity.com/paper/2202.04820/) (2022)
- [Prediction with Missing Data via Bayesian Additive Regression Trees](https://mlsanity.com/paper/1306.0618/) (2013)
- [Sharing pattern submodels for prediction with missing values](https://mlsanity.com/paper/2206.11161/) (2022)
- [Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness](https://mlsanity.com/paper/2208.06648/) (2022)
- [BEST : A decision tree algorithm that handles missing values](https://mlsanity.com/paper/1804.10168/) (2018)
- [MINTY: Rule-based Models that Minimize the Need for Imputing Features with Missing Values](https://mlsanity.com/paper/2311.14108/) (2023)

## Built on by (most cited first, 7 of 7)

- [Leveraging Predictive Equivalence in Decision Trees](https://mlsanity.com/paper/2506.14143/) (2025)
- [Additive Models Explained: A Computational Complexity Approach](https://mlsanity.com/paper/2510.21292/) (2025)
- [KANFIS: A Neuro-Symbolic Framework for Interpretable and Uncertainty-Aware Learning](https://mlsanity.com/paper/2602.03034/) (2026)
- [To impute or not to impute: How machine learning modelers treat missing data](https://mlsanity.com/paper/2503.16644/) (2025)
- [Prediction Models That Learn to Avoid Missing Values](https://mlsanity.com/paper/2505.03393/) (2025)
- [Explaining Concept Shift with Interpretable Feature Attribution](https://mlsanity.com/paper/2505.20634/) (2025)
- [Integrated Imputation-Classification for Supervised Learning with Missing Data](https://mlsanity.com/paper/2610.04273/) (2026)

## Abstract (arXiv)

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.