Akaike Information Criterion


Understanding the Akaike Information Criterion: A Powerful Tool for Model Selection Introduction In the realm of statistical modeling and machine learning, choosing the best-fitting model is a crucial step in the analysis process. The Akaike Information Criterion (AIC), named after its creator Hirotugu Akaike, is a widely used metric designed to facilitate model selection by quantifying the trade-off between model complexity and goodness of fit. In this article, we will delve into the theory behind AIC, its mathematical formulation, and its practical applications in various fields. The Concept of Model Selection Model selection is an essential aspect [...]