Determining the Akaike Information Criterion (AIC) involves a specific formula that balances a model’s goodness-of-fit with its complexity. This balance is achieved by assessing the likelihood function, which measures how well the model explains observed data, against the number of parameters the model uses. For example, comparing two models predicting stock prices, the one with a lower AIC, assuming similar explanatory power, is generally preferred because it achieves a comparable fit with fewer parameters, reducing the risk of overfitting.
This metric provides a crucial tool for model selection, allowing analysts to choose the model that best represents the underlying process generating the data without unnecessary complexity. Its use is widespread across diverse fields, from ecology and econometrics to machine learning, enhancing the reliability and interpretability of statistical modeling. Hirotugu Akaike’s development of this criterion in the 1970s revolutionized model comparison, offering a robust framework for navigating the trade-off between fit and complexity.
