方法对比
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| 简单线性回归× | 岭回归（Ridge Regression）× | |
|---|---|---|
| 领域≠ | 统计学 | 机器学习 |
| 方法族≠ | Regression model | Machine learning |
| 起源年份≠ | 1805 | 1970 |
| 提出者≠ | Adrien-Marie Legendre (least squares, 1805); Francis Galton (regression concept, 1886) | Hoerl, A.E. & Kennard, R.W. |
| 类型≠ | Parametric bivariate regression | L2-regularized linear regression |
| 开创性文献≠ | 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 ↗ |
| 别名≠ | SLR, ordinary least squares regression, OLS regression, bivariate regression | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| 相关≠ | 7 | 4 |
| 摘要≠ | Simple linear regression is the foundational parametric method for modelling a straight-line relationship between one continuous predictor and one continuous outcome, estimating the slope and intercept by ordinary least squares (OLS). The least squares principle was first published by Adrien-Marie Legendre in 1805, and Francis Galton introduced the concept of regression to the mean in 1886, coining the term that names the entire family of methods. | 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. |
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