Neural-ANOVA: Model Decomposition for Interpretable Machine Learning
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 glassbox models using the ANOVA decomposition. Our app...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
22.08.2024
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Subjects | |
Online Access | Get full text |
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Summary: | 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 glassbox models using the ANOVA decomposition. Our
approach formulates a learning problem, which enables rapid and closed-form
evaluation of integrals over subspaces that appear in the calculation of the
ANOVA decomposition. Finally, we conduct numerical experiments to illustrate
the advantages of enhanced interpretability and model validation by a
decomposition of the learned interaction effects. |
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DOI: | 10.48550/arxiv.2408.12319 |