Variable selection chooses which predictor variables enter a statistical model. The Lasso produces exact zero slopes along its Lasso regularization path; ridge regression generally shrinks without excluding variables. Selection for prediction should use predictive cross-validation or another stated criterion, rather than equating a coefficient p-value with practical usefulness.
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