Compară metode

	Regresie polinomială	Regresia prin metoda celor mai mici pătrate ordinare (OLS)	Regresia Ridge
Domeniu	Statistică	Econometrie	Învățare automată
Familie	Regression model	Regression model	Machine learning
Anul apariției	2012	2019	1970
Autorul original	Montgomery, Peck & Vining (textbook treatment); classical least squares	Wooldridge (textbook treatment); classical least squares	Hoerl, A.E. & Kennard, R.W.
Tip	Linear regression in transformed predictors	Linear regression	L2-regularized linear regression
Sursa seminală	Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Denumiri alternative	polynomial least squares, curvilinear regression, Polinom Regresyonu	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Înrudite	4	5	4
Rezumat	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	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.