On evaluating models, either in-sample or out-of-sample.

In-sample model evaluation techniques can be based on measures of fit or , but note that in-sample fit will typically increase spuriously as the model becomes more complex, which is called . For this reason, typically in-sample fit is penalized based on model complexity, like adjusted , or . AIC and BIC are also examples of information criteria, which can also be used in-sample.

Out-of-sample model evaluation usually relies on predictive accuracy and again on . Distributional predictions can be evaluated using .