mudelivalikuid
Mudeli valikud, or mudelivalikuid, refer to the process of choosing a statistical or predictive model from a set of candidate specifications based on data. The goal is to obtain a model that provides good predictive performance while maintaining reasonable complexity and interpretability.
Common approaches to mudelivalikuid include information criteria, such as AIC (Akaike Information Criterion) and BIC (Bayesian
Key considerations in mudelivalikuid include the risk of overfitting to the available data and the potential
In practice, mudelivalikuid is used across disciplines, including econometrics, biostatistics, and machine learning. Alternatives to selecting