LM101-076: How to Choose the Best Model using AIC and GAIC
Learning Machines 101 - A podcast by Richard M. Golden, Ph.D., M.S.E.E., B.S.E.E.
Categorie:
The precise semantic interpretation of the Akaike Information Criterion (AIC) and Generalized Akaike Information Criterion (GAIC) for selecting the best model are provided, explicit assumptions are provided for the AIC and GAIC to be valid, and explicit formulas are provided for the AIC and GAIC so they can be used in practice. AIC and GAIC provide a way of estimating the average prediction error of your learning machine on test data without using test data or cross-validation methods.
