用博弈论方法解释超参数对模型表现的影响
HyperSHAP: Shapley Values and Interactions for Explaining Hyperparameter Optimization
- 基于谢林值分解性能贡献，量化各超参作用
- 揭示超参间交互关系，发现优化关键因素
- 适合想理解优化过程的研究者和工程师
超参数优化（HPO）是实现良好预测性能的关键步骤。然而，单个超参数对模型泛化能力的影响高度依赖于具体上下文，难以采用通用方案，且多数HPO方法黑箱特性导致用户信任度低、应用受限。为此，我们提出一种基于谢林值与交互项的可解释性框架——HyperSHAP，通过性能指标在超参数间的加性分解，实现局部与全局层面的贡献分析及交互关系揭示。该框架可辅助开展消融研究、评估学习算法可调性，并分析优化器在不同超参空间中的行为特征。我们在多个HPO基准上验证了HyperSHAP的能力，分析了对应优化问题的交互结构，展现了其广泛适用性与改进HPO的实际价值。
原文摘要 · Abstract (English)
Hyperparameter optimization (HPO) is a crucial step in achieving strong predictive performance. Yet, the impact of individual hyperparameters on model generalization is highly context-dependent, prohibiting a one-size-fits-all solution and requiring opaque HPO methods to find optimal configurations. However, the black-box nature of most HPO methods undermines user trust and discourages adoption. To address this, we propose a game-theoretic explainability framework for HPO based on Shapley values and interactions. Our approach provides an additive decomposition of a performance measure across hyperparameters, enabling local and global explanations of hyperparameters' contributions and their interactions. The framework, named HyperSHAP, offers insights into ablation studies, the tunability of learning algorithms, and optimizer behavior across different hyperparameter spaces. We demonstrate HyperSHAP's capabilities on various HPO benchmarks to analyze the interaction structure of the corresponding HPO problems, demonstrating its broad applicability and actionable insights for improving HPO.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。