HyperSHAP: Shapley Values and Interactions for Explaining Hyperparameter Optimization

Hyperparameter optimization (HPO) is a crucial step in achieving strong predictive performance. The impact of individual hyperparameters on model generalization is highly context-dependent, prohibiting a one-size-fits-all solution and requiring opaque automated machine learning (AutoML) systems to find optimal configurations. However, the black-box nature of most AutoML systems undermines user trust and discourages adoption. To address this, we propose a game-theoretic explainability framework for HPO that is 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 to generalization performance and their interactions. The framework, named HyperSHAP, offers insights into ablations, 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 versatile applicability.