metaparameters
Metaparameters are quantities that govern the structure or learning process used to determine a model's parameters. They do not directly encode information about the data; rather, they influence how the parameters are estimated, regularized, or initialized.
The term is used differently across disciplines. In many statistical contexts, metaparameters are synonymous with hyperparameters,
Practically, metaparameters are chosen by the modeller and may be tuned via cross-validation, held-out performance, or
The choice of metaparameters affects convergence, bias, and generalization. Poorly chosen values can lead to slow
Metaparameters lie at the intersection of model specification and learning strategy, and understanding their role helps