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 and Infographic
Here's the deal — b is correct 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.
Full explanation below image
Full Explanation
B is correct 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. A is a simplification that ignores fit quality. C 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 (15 extra params × 2). D is wrong; a difference of 7 AIC units is considered meaningful.