Σύγκριση μεθόδων

	Παλινδρομική Ανάλυση Πολυωνύμου	Παλινδρόμηση Ridge
Πεδίο	Στατιστική	Μηχανική Μάθηση
Οικογένεια	Regression model	Machine learning
Έτος προέλευσης	2012	1970
Δημιουργός	Montgomery, Peck & Vining (textbook treatment); classical least squares	Hoerl, A.E. & Kennard, R.W.
Τύπος	Linear regression in transformed predictors	L2-regularized linear regression
Θεμελιώδης πηγή	Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Εναλλακτικές ονομασίες	polynomial least squares, curvilinear regression, Polinom Regresyonu	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Συναφείς	4	4
Σύνοψη	Polynomial regression is a regression method that models non-linear relationships by including squared and higher-degree terms of an explanatory variable, and it is a core tool of response surface analysis. As developed in Montgomery, Peck and Vining's Introduction to Linear Regression Analysis (2012), it remains linear in its parameters even though the fitted curve bends.	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.