Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Regressão Bayesiana× | Regressão Ridge× | |
|---|---|---|
| Área≠ | Bayesiano | Aprendizado de máquina |
| Família≠ | Bayesian methods | Machine learning |
| Ano de origem≠ | — | 1970 |
| Autor original≠ | — | Hoerl, A.E. & Kennard, R.W. |
| Tipo≠ | Bayesian linear model | L2-regularized linear regression |
| Fonte seminal≠ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| Outros nomes≠ | bayesian linear regression, probabilistic regression, bayesian regresyon | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Relacionados≠ | 2 | 4 |
| Resumo≠ | Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off. | Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated. |
| ScholarGateConjunto de dados ↗ |
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