Lasso

Summary

The lasso, short for least absolute shrinkage and selection operator, fits a linear model by penalised least squares. The penalty is a multiple of the sum of the absolute values of the coefficients. Because of the corners of the $\ell^1$ ball, the penalty sets some coefficients exactly to zero. So the lasso performs estimation and variable selection at the same time. It was introduced in statistics by Robert Tibshirani in 1996 and is a basic tool for regression with many more regressors than observations. It is a shrinkage estimator, and the elastic net combines it with ridge regression.