পদ্ধতির তুলনা করুন

	ল্যাসো রিগ্রেশন	Least Trimmed Squares (LTS) Regression
ক্ষেত্র	যন্ত্র শিখন	পরিসংখ্যান
পরিবার	Machine learning	Regression model
উদ্ভবের বছর	1996	1984
প্রবর্তক	Tibshirani, R.	Peter J. Rousseeuw
ধরন	Regularized linear regression (L1 penalty)	Robust linear regression
মৌলিক উৎস	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.
অপর নাম	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	LTS, least trimmed squares regression, trimmed least squares, robust regression
সম্পর্কিত	4	5
সারসংক্ষেপ	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.