Model Selection

Model selection studies how to choose among candidate models or configurations so predictions generalize while meeting goals such as interpretability, cost, or speed. Common approaches include cross-validation, information criteria such as AIC, BIC, and minimum description length, Bayesian methods, and regularization for selecting features or model complexity. For large model pools, active evaluation, bandits, meta-learning, and routing systems allocate tests or queries to identify promising models, ensembles, or task-specific specialists. A central challenge is that selection can overfit validation data, and rankings can change with the evaluation protocol or deployment setting, so no single criterion works everywhere.