The least absolute shrinkage and selection operator (LASSO) is also called L1. In this case, the preceding penalty parameter is replaced by |βj|:
By minimizing the preceding function, the coefficients are found and adjusted. In this scenario, as lambda becomes larger, λ → ∞, the penalty component rises, and so estimates start shrinking and become 0 (it doesn't happen in the case of ridge regression; rather, it would just be close to 0).