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The document introduces Higher-order Neural Additive Models (HONAMs), which enhance Neural Additive Models (NAMs) by capturing feature interactions of arbitrary orders while maintaining interpretability. HONAMs address the limitations of NAMs, which can only model first-order interactions, thereby improving predictive accuracy and interpretability in high-stakes applications. The authors provide a detailed architecture and methodology for HONAMs, demonstrating their effectiveness through experiments on vari

Author

LudwigMaximilian

Language

EN