用SHAP指导模型训练，让预测更准且解释更清晰。
SHAP-Guided Regularization in Machine Learning Models
- 引入基于熵的惩罚项，让特征重要性更集中稀疏
- 在多个基准数据集上提升泛化性能与解释稳定性
- 适合追求可解释性与性能兼顾的模型开发者
特征归因方法如SHapley Additive exPlanations（SHAP）在理解机器学习模型方面已变得至关重要，但其在引导模型优化方面的潜力尚未充分探索。本文提出一种基于SHAP的正则化框架，将特征重要性约束融入模型训练，以同时提升预测性能与可解释性。该方法通过熵基惩罚项，促使特征归因更加稀疏集中，并增强样本间的一致性。框架适用于回归与分类任务。首次在树模型中结合TreeSHAP进行正则化探索，通过在多个基准回归与分类数据集上的大量实验，验证了该方法在提升泛化能力的同时，确保特征归因的鲁棒性与可解释性。所提技术提供了一种全新的、以可解释性为导向的正则化思路，使机器学习模型兼具更高准确率与更强可信度。
原文摘要 · Abstract (English)
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.
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