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News/DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery
arxiv
PublishedJuly 10, 2026 at 4:00 AM
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DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2607.08150v1 Announce Type: new 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 clini

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Discussion
Mentioned models
04
  • 01
    DeepPySR
  • 02
    PySR
  • 03
    SHAP
  • 04
    LIME
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#symbolic regression#interpretable models#benchmark#transparency

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Mentioned models
04
  • 01
    DeepPySR
  • 02
    PySR
  • 03
    SHAP
  • 04
    LIME
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#symbolic regression#interpretable models#benchmark#transparency

Related coverage

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