Krahasoni metodat

	Regresioni linear robust	Regresioni Lasso
Fusha	Mësimi i makinës	Mësimi i makinës
Familja	Machine learning	Machine learning
Viti i origjinës	1964–1987	1996
Krijuesi	Huber, P. J.; Rousseeuw, P. J.	Tibshirani, R.
Lloji	Outlier-resistant supervised regression	Regularized linear regression (L1 penalty)
Burimi themelues	Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73–101.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Emërtime të tjera	robust regression, M-estimator regression, Huber regression, outlier-resistant regression	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
Të lidhura	5	4
Përmbledhja	Robust linear regression fits a linear model between predictors and a continuous outcome while down-weighting or discarding influential outliers, preventing the few anomalous observations that OLS is famously sensitive to from distorting the entire estimated line. Major variants include Huber regression, iteratively reweighted least squares (IRLS), RANSAC, and Theil-Sen estimation.	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.