AI engineering glossary · ML fundamentals
What is AIC (akaike information criterion)?
Akaike information criterion (also called AIC): A score for comparing models that rewards fit but penalises the number of parameters, calculated as AIC = 2k - 2 ln(L), where k is the parameter count and L the maximised likelihood. Lower values are preferred, and it estimates relative information lost by each model.
Akaike information criterion: an example
Model A has AIC 210 and Model B 204, so B is preferred as the better trade-off of fit and complexity.
Related terms
More ML fundamentals terms
- Annotation
- Baseline model
- Bayesian information criterion
- Bias error
- Bias-variance tradeoff
- Classification
- Concept drift
- Cost-sensitive learning
Where you learn it
Akaike information criterion is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.