This is another modification of ordinary least squares that also adds a penalty term to the sum of the squared errors, but the penalty term is proportional to the absolute value of the regression coefficients. This has a sparsity-inducing effect, meaning that some of the coefficients are forced to zero, resulting in a simpler model.