AIC

AIC is a model selection criterion that evaluates both how well a model fits the data and how complex that model is. Looking only at fit often rewards overly complex models, so AIC introduces a penalty to help produce a more balanced decision. It is commonly used in statistical modeling and probabilistic model comparison. The value of AIC lies in its attempt to balance “good fit” against “unnecessary complexity.”