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Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery
March 6, 2024, 5:42 a.m. | Yoshitomo Matsubara, Naoya Chiba, Ryo Igarashi, Yoshitaka Ushiku
cs.LG updates on arXiv.org arxiv.org
Abstract: This paper revisits datasets and evaluation criteria for Symbolic Regression (SR), specifically focused on its potential for scientific discovery. Focused on a set of formulas used in the existing datasets based on Feynman Lectures on Physics, we recreate 120 datasets to discuss the performance of symbolic regression for scientific discovery (SRSD). For each of the 120 SRSD datasets, we carefully review the properties of the formula and its variables to design reasonably realistic sampling ranges …
abstract arxiv benchmarks cs.ai cs.lg cs.ne cs.sc datasets discovery discuss evaluation feynman paper performance physics regression scientific discovery set type
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