Bandingkan metode

	Regresi Lasso	Regresi Least Trimmed Squares (LTS)
Bidang	Pembelajaran Mesin	Statistika
Keluarga	Machine learning	Regression model
Tahun asal	1996	1984
Pencetus	Tibshirani, R.	Peter J. Rousseeuw
Tipe	Regularized linear regression (L1 penalty)	Robust linear regression
Sumber perintis	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.
Alias	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	LTS, least trimmed squares regression, trimmed least squares, robust regression
Terkait	4	5
Ringkasan	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.	Least Trimmed Squares is a robust linear regression method introduced by Peter J. Rousseeuw in 1984. Instead of fitting all residuals, it estimates the coefficients by minimising the sum of only the h smallest squared residuals, which gives it a breakdown point of up to 50% and reliable estimates on data heavily contaminated by outliers.