Sammenlign metoder

	Responsflateanalyse (RSM)	Ridge Regression
Fagfelt	Forsøksdesign	Maskinlæring
Familie	Hypothesis test	Machine learning
Opprinnelsesår	1951	1970
Opphavsperson	George E. P. Box & K. B. Wilson	Hoerl, A.E. & Kennard, R.W.
Type	Second-order polynomial response surface model	L2-regularized linear regression
Opprinnelig kilde	Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Alias	RSM, Central Composite Design, Box-Behnken Design, CCD	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Relaterte	7	4
Sammendrag	Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics.	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.