Methoden vergleichen

	Einfache lineare Regression	Ridge Regression
Fachgebiet	Statistik	Maschinelles Lernen
Familie	Regression model	Machine learning
Entstehungsjahr	1805	1970
Urheber	Adrien-Marie Legendre (least squares, 1805); Francis Galton (regression concept, 1886)	Hoerl, A.E. & Kennard, R.W.
Typ	Parametric bivariate regression	L2-regularized linear regression
Wegweisende Quelle	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.
Aliasnamen	SLR, ordinary least squares regression, OLS regression, bivariate regression	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Verwandt	7	4
Zusammenfassung	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.