adaptive lasso

Adaptive lasso is a variation of the Lasso regression method used for feature selection in statistical modeling. The Lasso method is used to select a subset of predictors from a larger set of potential predictors by imposing a constraint on the sum of the absolute values of the coefficients of the predictors. This constraint is known as the L1 penalty.

Adaptive lasso is a modification of the Lasso method that adapts the penalty value for each predictor based on its importance in the model. In adaptive lasso, the penalty value is inversely proportional to the absolute value of the estimated coefficient of each predictor. This means that predictors with large estimated coefficients are assigned smaller penalty values, while predictors with smaller estimated coefficients are assigned larger penalty values.

The adaptive lasso method is particularly useful when the predictors are highly correlated and the standard Lasso method fails to identify the most important predictors. It has been shown to have better performance than the standard Lasso method in simulations and real-world applications.