Neural Additive Models (NAMs) have recently demonstrated promising predictive performance while maintaining interpretability. However, their capacity is limited to capturing only first-order feature interactions, which restricts their effectiveness on real-world datasets. To address this limitation, we propose Higher-order Neural Additive Models (HONAMs), an interpretable machine learning model that effectively and efficiently captures feature interactions of arbitrary orders. HONAMs improve predictive accuracy without compromising interpretability, an essential requirement in high-stakes applications. This advantage of HONAM can help analyze and extract high-order interactions present in datasets. The source code for HONAM is publicly available at https://github.com/gim4855744/HONAM/.
@article{arxiv.2209.15409,
title = {Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature Interactions},
author = {Minkyu Kim and Hyun-Soo Choi and Jinho Kim},
journal= {arXiv preprint arXiv:2209.15409},
year = {2025}
}
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IEEE International Conference on Data Mining (ICDM) 2025