Сравнение методов

	Простая линейная регрессия	Гребневая регрессия
Область	Статистика	Машинное обучение
Семейство	Regression model	Machine learning
Год появления	1805	1970
Автор метода	Adrien-Marie Legendre (least squares, 1805); Francis Galton (regression concept, 1886)	Hoerl, A.E. & Kennard, R.W.
Тип	Parametric bivariate regression	L2-regularized linear regression
Основополагающий источник	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]	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Другие названия	SLR, ordinary least squares regression, OLS regression, bivariate regression	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Связанные	7	4
Сводка	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.