Question

What do AIC and BIC do to remove the overfitting effect when adding parameters?

Answer 1

AIC and BIC remove overfitting effect by penalizing models with more parameters and balancing model complexity with goodness of fit.

Step-by-step explanation:

When adding parameters, AIC (Akaike's information criterion) and BIC (Bayesian information criterion) help to remove the overfitting effect by penalizing models with more parameters. Both AIC and BIC balance model complexity with goodness of fit to prevent overfitting. AIC favors more complex models when sample sizes are small, while BIC is recommended for large sample sizes when the aim is to determine the most important variables explaining variation in the response.

AIC and BIC are calculated differently:

• AIC = -2log(L) + 2k
• BIC = -2log(L) + k log(n)