Neural-ANOVA: Analytical Model Decomposition using Automatic Integration
Machine Learning
2025-08-01 v2 Machine Learning
Abstract
The analysis of variance (ANOVA) decomposition offers a systematic method to understand the interaction effects that contribute to a specific decision output. In this paper we introduce Neural-ANOVA, an approach to decompose neural networks into the sum of lower-order models using the functional ANOVA decomposition. Our approach formulates a learning problem, which enables fast analytical evaluation of integrals over subspaces that appear in the calculation of the ANOVA decomposition. Finally, we conduct numerical experiments to provide insights into the approximation properties compared to other regression approaches from the literature.
Cite
@article{arxiv.2408.12319,
title = {Neural-ANOVA: Analytical Model Decomposition using Automatic Integration},
author = {Steffen Limmer and Steffen Udluft and Clemens Otte},
journal= {arXiv preprint arXiv:2408.12319},
year = {2025}
}
Comments
6 pages, 3 figures, 3 tables, accepted for publication at MLSP 2025