Porównaj metody

	Regresja kwantylowa	Regularyzacja grzbietowa (Ridge Regression)
Dziedzina	Ekonometria	Uczenie maszynowe
Rodzina	Regression model	Machine learning
Rok powstania	1978	1970
Twórca	Koenker & Bassett	Hoerl, A.E. & Kennard, R.W.
Typ	Conditional quantile regression	L2-regularized linear regression
Źródło pierwotne	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Inne nazwy	conditional quantile regression, regression quantiles, Kantil Regresyon	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Pokrewne	5	4
Podsumowanie	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.	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.