# GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions

arXiv: https://arxiv.org/abs/2003.07132 · HTML: https://mlsanity.com/paper/2003.07132/

## Key facts

- Cited by 82 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2020.

## Built on by (most cited first, 30 of 82)

- [Sparse Interaction Additive Networks via Feature Interaction Detection and Sparse Selection](https://mlsanity.com/paper/2209.09326/) (2022)
- [Designing Inherently Interpretable Machine Learning Models](https://mlsanity.com/paper/2111.01743/) (2021)
- [Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life](https://mlsanity.com/paper/2301.06676/) (2023)
- [Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean](https://mlsanity.com/paper/2301.11862/) (2023)
- [A Comprehensive Survey on Self-Interpretable Neural Networks](https://mlsanity.com/paper/2501.15638/) (2025)
- [Data-Driven Design for Metamaterials and Multiscale Systems: A Review](https://mlsanity.com/paper/2307.05506/) (2023)
- [SurvNAM: The machine learning survival model explanation](https://mlsanity.com/paper/2104.08903/) (2021)
- [Backpack Language Models](https://mlsanity.com/paper/2305.16765/) (2023)
- [Improving Neural Additive Models with Bayesian Principles](https://mlsanity.com/paper/2305.16905/) (2023)
- [Monotonic Neural Additive Models: Pursuing Regulated Machine Learning Models for Credit Scoring](https://mlsanity.com/paper/2209.10070/) (2022)
- [Feature Inference Attack on Shapley Values](https://mlsanity.com/paper/2407.11359/) (2024)
- [Interpretability with full complexity by constraining feature information](https://mlsanity.com/paper/2211.17264/) (2022)
- [GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints](https://mlsanity.com/paper/2204.09123/) (2022)
- [Structural Neural Additive Models: Enhanced Interpretable Machine Learning](https://mlsanity.com/paper/2302.09275/) (2023)
- [Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models](https://mlsanity.com/paper/2307.08175/) (2023)
- [Tree Ensemble Explainability through the Hoeffding Functional Decomposition and TreeHFD Algorithm](https://mlsanity.com/paper/2510.24815/) (2025)
- [Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges](https://mlsanity.com/paper/2409.13723/) (2024)
- [Explaining Adverse Actions in Credit Decisions Using Shapley Decomposition](https://mlsanity.com/paper/2204.12365/) (2022)
- [Monotonicity for AI ethics and society: An empirical study of the monotonic neural additive model in criminology, education, health care, and finance](https://mlsanity.com/paper/2301.07060/) (2023)
- [How to address monotonicity for model risk management?](https://mlsanity.com/paper/2305.00799/) (2023)
- [PiML Toolbox for Interpretable Machine Learning Model Development and Diagnostics](https://mlsanity.com/paper/2305.04214/) (2023)
- [Interpretable Machine Learning based on Functional ANOVA Framework: Algorithms and Comparisons](https://mlsanity.com/paper/2305.15670/) (2023)
- [MorphBoost: Self-Organizing Universal Gradient Boosting with Adaptive Tree Morphing](https://mlsanity.com/paper/2511.13234/) (2025)
- [Towards Intrinsic Interpretability of Large Language Models:A Survey of Design Principles and Architectures](https://mlsanity.com/paper/2604.16042/) (2026)
- [Attention-like feature explanation for tabular data](https://mlsanity.com/paper/2108.04855/) (2021)
- [Predicting Census Survey Response Rates With Parsimonious Additive Models and Structured Interactions](https://mlsanity.com/paper/2108.11328/) (2021)
- [Using Model-Based Trees with Boosting to Fit Low-Order Functional ANOVA Models](https://mlsanity.com/paper/2207.06950/) (2022)
- [E Pluribus Unum Interpretable Convolutional Neural Networks](https://mlsanity.com/paper/2208.05369/) (2022)
- [On marginal feature attributions of tree-based models](https://mlsanity.com/paper/2302.08434/) (2023)
- [SurvBeX: An explanation method of the machine learning survival models based on the Beran estimator](https://mlsanity.com/paper/2308.03730/) (2023)

## Abstract (arXiv)

The lack of interpretability is an inevitable problem when using neural network models in real applications. In this paper, an explainable neural network based on generalized additive models with structured interactions (GAMI-Net) is proposed to pursue a good balance between prediction accuracy and model interpretability. GAMI-Net is a disentangled feedforward network with multiple additive subnetworks; each subnetwork consists of multiple hidden layers and is designed for capturing one main effect or one pairwise interaction. Three interpretability aspects are further considered, including a) sparsity, to select the most significant effects for parsimonious representations; b) heredity, a pairwise interaction could only be included when at least one of its parent main effects exists; and c) marginal clarity, to make main effects and pairwise interactions mutually distinguishable. An adaptive training algorithm is developed, where main effects are first trained and then pairwise interactions are fitted to the residuals. Numerical experiments on both synthetic functions and real-world datasets show that the proposed model enjoys superior interpretability and it maintains competitive prediction accuracy in comparison to the explainable boosting machine and other classic machine learning models.