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 Information Criterion (BIC), which balance goodness of fit against model complexity. Cross-validation evaluates predictive performance on unseen data and guides the choice of model size or hyperparameters. Likelihood ratio tests can compare nested models, while regularization methods like LASSO and ridge regression implicitly perform variable selection by shrinking or zeroing coefficients, effectively choosing a subset of predictors.

In a Bayesian setting, model selection uses posterior model probabilities and Bayes factors to compare models, with the option of model averaging to account for model uncertainty rather than committing to a single model. This approach can provide more robust predictions when multiple models have substantial support.

Practical considerations in model selection include sample size, the risk of misspecification, and the potential for overfitting or data leakage during validation. The computational cost rises with larger model spaces or complex hierarchies. The choice of validation strategy may also depend on the data structure, such as temporal dependence in time series data.

Model selection is a central activity in statistics and machine learning, guiding choices about which variables to include, which model class to use, and how to build interpretable and generalizable predictive systems.