LASSO [Neurosurgery Wiki]

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====== LASSO ======

In [[statistics]] and [[machine learning]], lasso (least absolute shrinkage and selection operator) (also Lasso or LASSO) is a [[regression analysis]] [[method]] that performs both variable [[selection]] and regularization in order to enhance the prediction accuracy and interpretability of the statistical model it produces. It was originally introduced in geophysics literature in 1986 but was independently rediscovered and popularized by Robert Tibshirani in 1996 who coined out the term and provided further insights into the observed performance.

Lasso was originally formulated for least squares models and this simple case reveals a substantial amount about the behavior of the estimator, including its relationship to ridge regression and best subset selection and the connections between lasso coefficient estimates and so-called soft thresholding. It also reveals that (like standard linear regression) the coefficient estimates need not be unique if covariates are collinear.