SRBench++ : principled benchmarking of symbolic regression with domain-expert interpretation.

ABSTRACT: Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main promise of this approach is that it may return an interpretable model that can be insightful to users, while maintaining high accuracy. The current standard for benchmarking these algorithms is SRBench, which evaluates methods on hundreds of datasets that are a mix of real-world and simulated processes spanning multiple domains. At present, the ability of SRBench to evaluate interpretability is limited to measuring the size of expressions on real-world data, and the exactness of model forms on synthetic data. In practice, model size is only one of many factors used by subject experts to determine how interpretable a model truly is. Furthermore, SRBench does not characterize algorithm

SUBMITTER: de Franca FO 

PROVIDER: S-EPMC12321164 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature