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Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature Interactions

arXiv — cs.LG•Monday, November 17, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
- The introduction of Higher-order Neural Additive Models (HONAMs) marks a significant advancement in machine learning, as they enhance the capabilities of Neural Additive Models (NAMs) by capturing complex feature interactions. This development is crucial for industries where interpretability and predictive accuracy are paramount, allowing for better decision-making based on data insights. Although no related articles were found, the evolution from NAMs to HONAMs reflects a broader trend in AI towards models that balance complexity with interpretability.
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