The Akaike information criterion (AIC) is an estimator of out-of-sample prediction error, and thus a measure of quality of statistical models, for a given set of data. Its definition is1

\text{AIC} = 2d - 2\text{loglik} ,

where d is the number of parameters estimated by the model and \text{loglik} is the log of the maximized likelihood function.