Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Elastic Net× | Regresión Ridge× | |
|---|---|---|
| Campo | Aprendizaje automático | Aprendizaje automático |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2005 | 1970 |
| Autor original≠ | Zou, H. & Hastie, T. | Hoerl, A.E. & Kennard, R.W. |
| Tipo≠ | Regularized linear regression (L1 + L2 penalty) | L2-regularized linear regression |
| Fuente seminal≠ | Zou, H. & Hastie, T. (2005). Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society: Series B, 67(2), 301–320. DOI ↗ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| Alias | Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Relacionados | 4 | 4 |
| Resumen≠ | Elastic Net is a regularized linear regression method introduced by Zou and Hastie in 2005 that blends the LASSO (L1) and Ridge (L2) penalties, so it performs variable selection and coefficient shrinkage at the same time. It is designed for predictive and explanatory modelling on data with many, possibly correlated, predictors. | 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 datos ↗ |
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