modelselection
Model selection refers to the process of choosing a statistical or machine learning model from a set of candidate models based on data and prior information. The aim is to identify a model that provides accurate predictions or valid inferences while avoiding overfitting and excessive complexity.
Common approaches to model selection include information criteria such as Akaike Information Criterion (AIC) and Bayesian
In a Bayesian setting, model selection uses posterior model probabilities and Bayes factors to compare models,
Practical considerations in model selection include sample size, the risk of misspecification, and the potential for
Model selection is a central activity in statistics and machine learning, guiding choices about which variables