विधियों की तुलना करें

	लासो रिग्रेशन	Least Trimmed Squares (LTS) रिग्रेशन	क्वांटाइल रिग्रेशन
क्षेत्र	मशीन अधिगम	सांख्यिकी	अर्थमिति
परिवार	Machine learning	Regression model	Regression model
उद्भव वर्ष	1996	1984	1978
प्रवर्तक	Tibshirani, R.	Peter J. Rousseeuw	Koenker & Bassett
प्रकार	Regularized linear regression (L1 penalty)	Robust linear regression	Conditional quantile regression
मौलिक स्रोत	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.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
उपनाम	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	LTS, least trimmed squares regression, trimmed least squares, robust regression	conditional quantile regression, regression quantiles, Kantil Regresyon
संबंधित	4	5	5
सारांश	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.	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.