Ridge regression and the lasso may be regarded as an extension of least squares regression by including constraints into the least squares problem. From the answers below, find the correct one. Ridge and lasso regression select a model with fewer than the original number of predictors. Commonly, lasso regression will have selected a subset of predictors in their final model (some parameter estimates may be equal to zero). For the same tuning parameter, ridge and lasso regression will always select the same number of predictors. The lasso shrinks the coefficient estimates to zero but cannot have them equal to zero