LightGBM and other regressors achieve R^{2}≈0.6–0.7 under random CV on CCSN GW catalogues but collapse to worse-than-mean performance under Leave-One-EoS-Out validation, exposing a generalisation gap for unseen EoS families.
The feature target correlations remain weak, showing that the relevant observables are not uniquely diagnostic of EoS parameters in this catalogue
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The Generalization Gap in Machine Learning EoS Inference from Core-Collapse Supernova Gravitational Waves
LightGBM and other regressors achieve R^{2}≈0.6–0.7 under random CV on CCSN GW catalogues but collapse to worse-than-mean performance under Leave-One-EoS-Out validation, exposing a generalisation gap for unseen EoS families.