A practical utility-based but objective approach to model selection for regression in scientific applications.
Andrea MurariRiccardo RossiLuca SpolladoreMichele LungaroniPasquale GaudioMichela GelfusaPublished in: Artif. Intell. Rev. (2023)
Keyphrases
- model selection
- cross validation
- regression model
- bayesian learning
- parameter estimation
- hyperparameters
- sample size
- machine learning
- statistical inference
- meta learning
- mixture model
- error estimation
- statistical learning
- generalization error
- selection criterion
- variable selection
- model selection criteria
- bayesian model selection
- real world
- generalization bounds
- data mining
- motion segmentation
- decision trees
- feature selection
- learning machines
- gaussian process
- leave one out cross validation
- multivariate regression