Сравнение методов

	Регрессия методом обыкновенных наименьших квадратов (ОНМК)	Гребневая регрессия
Область	Эконометрика	Машинное обучение
Семейство	Regression model	Machine learning
Год появления	2019	1970
Автор метода	Wooldridge (textbook treatment); classical least squares	Hoerl, A.E. & Kennard, R.W.
Тип	Linear regression	L2-regularized linear regression
Основополагающий источник	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.
Другие названия	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Связанные	5	4
Сводка	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.