Akaike and Bayesian Information Criteria $($AIC and BIC$)$ guard us against models that are too complex. We should choose models at or near AIC$/$BIC minimum. When the gain in likelihood $($improvement in the fit$)$ due to the addition of yet another model parameter is completely offset by the penalty associated with that parameter $($i$.$e$.$ AIC$/$BIC starts growing as we add variables$),$ the model is likely to overfit and should be avoided $($or at least cross$-$validated very thoroughly and with prejudice!$).$

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True

False

Flag question: Question $8$