What is regret analysis in model evaluation?

Regret measures performance loss compared to an optimal policy.

Regret quantifies how much reward is lost by not following the optimal decision strategy. It is a core metric in reinforcement learning and bandit systems. Lower regret indicates better learning efficiency and decision quality over time.