DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery
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 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
The increasing demand for explainable AI in critical fields like medicine and social science, combined with advancements in AI techniques, makes the development of interpretable models particularly timely.
This development addresses a fundamental limitation of 'black-box' AI models by providing transparent, directly interpretable analytical equations, crucial for trust and adoption in sensitive domains.
The ability to more effectively apply symbolic regression to high-dimensional and irregular data sets with principled formula selection enhances the potential for AI-driven scientific discovery and clinical application.
- · Medical Researchers
- · Social Scientists
- · AI Explainability Researchers
- · Drug Discovery
- · Developers of purely black-box AI models
- · Clinical decision support systems lacking transparency
DeepPySR enables more reliable and transparent AI applications in scientific and clinical domains by generating interpretable formulas from data.
Increased adoption of interpretable AI could lead to faster scientific breakthroughs and regulatory acceptance in areas requiring high transparency.
A shift towards 'glass-box' AI models might reduce reliance on complex post-hoc explainability tools, simplifying AI integration into sensitive workflows.
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Read at arXiv cs.LG