Machine learning
Lasso Regression
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.
Open in MethodMindSoonVideoSoon
Read the full method
Members only
Sign inSign in with a free account to read this section.
Sources
- Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI: 10.1111/j.2517-6161.1996.tb02080.x ↗
Related methods
Referenced by
Adaptive Cox Proportional HazardsBayesian LASSO RegressionBayesian Model AveragingBayesian Multiple linear regressionBayesian Ridge RegressionElastic NetElastic Net RegressionMachine learning-assisted epigenome-wide association studyMachine learning-augmented instrumental variablesMultiple Linear RegressionNonparametric Quantile RegressionOLS RegressionOrdinary Least SquaresPolynomial RegressionPrincipal Component AnalysisQuantile RegressionRegularized Support Vector MachineRidge RegressionRobust Linear RegressionRobust Multiple linear regressionRobust RegressionRobust Ridge regressionStepwise RegressionSupport Vector Regression