no code implementations • 12 Jan 2023 • Parikshit Ram, Alexander G. Gray, Horst C. Samulowitz, Gregory Bramble
We show, to our knowledge, the first theoretical treatments of two common questions in cross-validation based hyperparameter selection: (1) After selecting the best hyperparameter using a held-out set, we train the final model using {\em all} of the training data -- since this may or may not improve future generalization error, should one do this?
