LARS stands for Least Angle Regression. It is a feature selection technique for multiple regression that incorporates a penalty.

LARS is an extension of the LASSO, which constrains regression coefficients to no more than a possible absolute sum. The LARS algorithm can be understood as recalculating the regression model step by step while slowly relaxing the LASSO constraint. The result of this is analogous to a forward stepwise selection algorithm, but is valid, whereas forward stepwise selection is not.

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