A data scientist is selecting between a model with 10 parameters (AIC=245) and one with 25 parameters (AIC=238). Which model should be selected and why?
Select an answer to reveal the explanation.
Short Explanation
You should choose the 25-parameter model because AIC already balances fit and complexity. The lower AIC means it gives the better overall tradeoff, even though it has more parameters.
Full Explanation
The 25-parameter model should be selected because AIC already accounts for model complexity through the 2k penalty term. When comparing models, the one with the lower AIC is preferred because it achieves a better balance between goodness of fit and parameter parsimony. A difference of 7 AIC units strongly favors the 25-parameter model. The 10-parameter model choice based on fewer parameters always meaning better generalization is wrong because it ignores fit quality. The 10-parameter model choice based on AIC penalizing each additional parameter by exactly 2 per parameter is factually correct about the penalty but reaches the wrong conclusion: the 25-parameter model's lower AIC means the fit improvement outweighs the 30-unit penalty from 15 extra parameters. The option saying neither model should be selected because a difference of 7 AIC units is below the threshold for meaningful comparison is wrong; a difference of 7 AIC units is considered meaningful.