Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Mínimos Cuadrados Ordinarios (MCO)× | Regresión Ridge× | |
|---|---|---|
| Campo≠ | Estadística | Aprendizaje automático |
| Familia≠ | Regression model | Machine learning |
| Año de origen≠ | 1805 | 1970 |
| Autor original≠ | Adrien-Marie Legendre (1805); Carl Friedrich Gauss (1809) | Hoerl, A.E. & Kennard, R.W. |
| Tipo≠ | Linear parameter estimation | L2-regularized linear regression |
| Fuente seminal≠ | Legendre, A.-M. (1805). Nouvelles méthodes pour la détermination des orbites des comètes. Firmin Didot, Paris. [Appendix: Sur la Méthode des moindres quarrés, pp. 72–80.] link ↗ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| Alias≠ | OLS, OLS regression, linear least squares, classical linear regression | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Relacionados≠ | 8 | 4 |
| Resumen≠ | Ordinary Least Squares (OLS) is the canonical method for estimating the parameters of a linear regression model by minimizing the sum of squared differences between observed and predicted values. First published by Adrien-Marie Legendre in 1805 and independently developed by Carl Friedrich Gauss (who claimed priority from 1795), OLS is provably optimal under the Gauss-Markov theorem: given its assumptions, it yields the Best Linear Unbiased Estimator (BLUE) of the regression coefficients. | 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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