What is the primary purpose of the Akaike Information Criterion (AIC) in model selection?

What is the primary purpose of the Akaike Information Criterion (AIC) in model selection?

• A. To Maximize Likelihood Without Penalty
• B. To Balance Goodness-of-Fit and Complexity
• C. To Minimize Dataset Size
• D. To Choose The Model With The Most Parameters

Answer: (B)

AIC is about trading off how well a model fits the data with how complex it is. More parameters can improve fit, but they also risk capturing noise rather than the true signal. AIC introduces a penalty for each estimated parameter, so the overall score rewards good fit but discourages unnecessary complexity. The usual form is AIC = 2k − 2 ln(L), where k is the number of parameters and L is the maximum likelihood. Lower AIC means a model is closer to the true information content of the data, so you compare candidates and choose the one with the smallest AIC. This focuses on selecting a model that explains the data well without overfitting by being too complex.

AIC is about trading off how well a model fits the data with how complex it is. More parameters can improve fit, but they also risk capturing noise rather than the true signal. AIC introduces a penalty for each estimated parameter, so the overall score rewards good fit but discourages unnecessary complexity. The usual form is AIC = 2k − 2 ln(L), where k is the number of parameters and L is the maximum likelihood. Lower AIC means a model is closer to the true information content of the data, so you compare candidates and choose the one with the smallest AIC. This focuses on selecting a model that explains the data well without overfitting by being too complex.