What is LARS?

LARS most likely refers to Least Angle Regression, a regression algorithm in statistics and machine learning. It's used for high-dimensional data, where the number of predictors is greater than the number of observations. LARS builds a model by iteratively selecting variables that are most correlated with the response variable, adding them to the active set, and then moving the coefficients of the active set in a direction equiangular to the correlations with the residual. This process continues until all variables are in the model or a stopping criterion is met. LARS is useful for feature selection and model building in contexts like genomics, finance, and signal processing.