{"ID":6267052,"CreatedAt":"2026-07-10T01:11:38.759438437Z","UpdatedAt":"2026-07-13T01:02:08.706470581Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.08150","arxiv_id":"2607.08150","title":"DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery","abstract":"Symbolic regression (SR) discovers analytical equations from data, yielding glass-box models with directly interpretable formulas, unlike black-box methods that rely on unstable post-hoc tools such as SHAP or LIME. This transparency is crucial in clinical medicine and social science, but SR faces three challenges: high-dimensional inputs, principled selection of Pareto-front formulae, and data irregularities such as multicollinearity and class imbalance. We introduce DeepPySR, which addresses these issues with a dynamic variable-pruning schedule to remove irrelevant features during search, an exponential Pareto selection criterion that eliminates trade-offs between accuracy and complexity, and a multi-layer architecture for hierarchical symbolic composition. On four Feynman physics benchmarks and seven biomedical and social-science datasets, DeepPySR outperforms PySR and baselines on body fat (R$^2$: 0.794 vs.\\ 0.702), heart disease (F1: 0.898 vs.\\ 0.787), student performance (R$^2$: 0.964 vs.\\ 0.948), and Raine BMI (R$^2$: 0.525 vs.\\ 0.370), producing interpretable formulas aligned with domain risk factors.","short_abstract":"Symbolic regression (SR) discovers analytical equations from data, yielding glass-box models with directly interpretable formulas, unlike black-box methods that rely on unstable post-hoc tools such as SHAP or LIME. This transparency is crucial in clinical medicine and social science, but SR faces three challenges: high...","url_abs":"https://arxiv.org/abs/2607.08150","url_pdf":"https://arxiv.org/pdf/2607.08150v1","authors":"[\"Fuling Chen\",\"Kevin Vinsen\",\"Phillip Melton\",\"Rae-Chi Huang\"]","published":"2026-07-09T06:41:53Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}