astroautomata/PySR

Health v2 · maintenance only

Adoption not part of the score

3739 stars · 349 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

PySR is a high-performance symbolic regression library that searches for symbolic mathematical expressions optimizing a given objective, implemented in Julia with a Python interface. It integrates with scikit-learn and supports distributed, evolutionary search for interpretable equation discovery.

Use cases

• discover symbolic equations from data
• fit interpretable mathematical models to datasets
• perform symbolic regression in Python
• find closed-form expressions approximating noisy data
• use evolutionary algorithms for equation discovery
• explainable machine learning model discovery

When to choose

• you need interpretable, human-readable equations instead of black-box models
• you want high-performance symbolic regression with distributed search
• you work in Python with scikit-learn-style APIs
• you need equation discovery for scientific data

When to avoid

• you need maximum predictive accuracy regardless of interpretability
• your data is best modeled by deep neural networks
• you need a pure-Python dependency without Julia runtime

Facets

library · maturity stable

machine-learning data-science math machine-learning data-science python jvm-scripting windows symbolic-regression julia genetic-algorithms interpretable-ml explainable-ai equation-discovery scikit-learn automl evolutionary-algorithms algorithms linux macos docker

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