# Akaike Information Criterion - Considers goodness-of-fit to the data and penalizes complexity of the model - $AIC=−2log⁡(L)+2q$ - where: - L: likelihood function for a particular model - q: number of variables of this model - If error terms $\epsilon$ follows [[Normal Distribution]] , expected value 0 + constant variance $AIC = \frac{1}{\eta \sigma^{2}}(RSS + 2p \hat \sigma^2)$