Papers › neuralGAM: Explainable generalized additive neural networks with independent neural...
neuralGAM: Explainable generalized additive neural networks with independent neural network training
Ines Ortega-Fernandez, Marta Sestelo, Nora M. Villanueva
Neural Networks are one of the most popular methods nowadays given their high performance on diverse tasks, such as computer vision, anomaly detection, computer-aided disease detection and diagnosis, or natural language processing. However, it is usually unclear how neural networks make decisions, and current methods that try to provide interpretability to neural networks are not robust enough. We introduce neuralGAM, a fully explainable neural network framework based on Generalized Additive Models, which trains a different neural network to estimate and visualize the contribution of each feature to the response variable. In contrast to other Neural Additive Models implementations, in neuralGAM neural networks are trained independently leveraging the local scoring and backfitting algorithms to ensure that the Generalized Additive Model converges and it is additive. The resultant model is a highly accurate and explainable deep learning model, which can be used for high-risk AI practices where decision-making should be based on accountable and interpretable algorithms. neuralGAM is also available as an R package at the CRAN: https://cran.r-project.org/web/packages/neuralGAM/index.html
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections