# SHAP-Guided Regularization in Machine Learning Models

arXiv: https://arxiv.org/abs/2507.23665 · HTML: https://mlsanity.com/paper/2507.23665/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 31 Jul 2025.

## Builds on (most cited first, 8 of 8)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Towards Robust Interpretability with Self-Explaining Neural Networks](https://mlsanity.com/paper/1806.07538/) (2018)
- [Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey](https://mlsanity.com/paper/2006.11371/) (2020)
- [Principles and Practice of Explainable Machine Learning](https://mlsanity.com/paper/2009.11698/) (2020)
- [A General Survey on Attention Mechanisms in Deep Learning](https://mlsanity.com/paper/2203.14263/) (2022)
- [Shapley Explanation Networks](https://mlsanity.com/paper/2104.02297/) (2021)
- [SHAPNN: Shapley Value Regularized Tabular Neural Network](https://mlsanity.com/paper/2309.08799/) (2023)
- [X-SHIELD: Regularization for eXplainable Artificial Intelligence](https://mlsanity.com/paper/2404.02611/) (2024)

## Abstract (arXiv)

Feature attribution methods such as SHapley Additive exPlanations (SHAP) have become instrumental in understanding machine learning models, but their role in guiding model optimization remains underexplored. In this paper, we propose a SHAP-guided regularization framework that incorporates feature importance constraints into model training to enhance both predictive performance and interpretability. Our approach applies entropy-based penalties to encourage sparse, concentrated feature attributions while promoting stability across samples. The framework is applicable to both regression and classification tasks. Our first exploration started with investigating a tree-based model regularization using TreeSHAP. Through extensive experiments on benchmark regression and classification datasets, we demonstrate that our method improves generalization performance while ensuring robust and interpretable feature attributions. The proposed technique offers a novel, explainability-driven regularization approach, making machine learning models both more accurate and more reliable.