השוואת שיטות

	רגרסיית רכס רובסטית	רגרסיית לאסו
תחום	סטטיסטיקה	למידת מכונה
משפחה	Regression model	Machine learning
שנת המקור	1991	1996
הוגה השיטה	Silvapulle (1991); building on Tikhonov (1963) and Huber (1964)	Tibshirani, R.
סוג	Regularized robust linear regression	Regularized linear regression (L1 penalty)
מקור מכונן	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.
כינויים	ridge M-estimation, robust regularized regression, M-estimator ridge, outlier-resistant ridge regression	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
קשורות	5	4
תקציר	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.