What are the advantages and disadvantages of using AIC as a regression model selection criterion?
When you are building a regression model, you often face the challenge of choosing the best subset of predictors from a large pool of candidates. One way to tackle this problem is to use a criterion that measures the trade-off between the complexity and the fit of the model, such as the Akaike information criterion (AIC). In this article, you will learn what AIC is, how it works, and what are its advantages and disadvantages as a regression model selection criterion.