Robust priors for regularized regression.
Cogn Psychol
; 132: 101444, 2022 02.
Article
en En
| MEDLINE
| ID: mdl-34861584
ABSTRACT
Induction benefits from useful priors. Penalized regression approaches, like ridge regression, shrink weights toward zero but zero association is usually not a sensible prior. Inspired by simple and robust decision heuristics humans use, we constructed non-zero priors for penalized regression models that provide robust and interpretable solutions across several tasks. Our approach enables estimates from a constrained model to serve as a prior for a more general model, yielding a principled way to interpolate between models of differing complexity. We successfully applied this approach to a number of decision and classification problems, as well as analyzing simulated brain imaging data. Models with robust priors had excellent worst-case performance. Solutions followed from the form of the heuristic that was used to derive the prior. These new algorithms can serve applications in data analysis and machine learning, as well as help in understanding how people transition from novice to expert performance.
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Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
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Encéfalo
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Año:
2022
Tipo del documento:
Article