Σύγκριση μεθόδων

	Επιβλητική Παλινδρόμηση Ridge (Robust Ridge Regression)	Παλινδρόμηση Lasso
Πεδίο	Στατιστική	Μηχανική Μάθηση
Οικογένεια	Regression model	Machine learning
Έτος προέλευσης	1991	1996
Δημιουργός	Silvapulle (1991); building on Tikhonov (1963) and Huber (1964)	Tibshirani, R.
Τύπος	Regularized robust linear regression	Regularized linear regression (L1 penalty)
Θεμελιώδης πηγή	Silvapulle, M. J. (1991). Robust ridge regression based on an M-estimator. Australian Journal of Statistics, 33(3), 319–333.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Εναλλακτικές ονομασίες	ridge M-estimation, robust regularized regression, M-estimator ridge, outlier-resistant ridge regression	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
Συναφείς	5	4
Σύνοψη	Robust Ridge regression combines M-estimation with L2 (ridge) regularization to produce coefficient estimates that are simultaneously resistant to outliers and stable under multicollinearity. It minimizes a robust loss function (such as Huber's) penalized by the squared norm of the coefficient vector, downweighting influential observations while shrinking correlated predictors toward zero.	Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.