Both ridge and lasso regression are regularized methods.

Both ridge and lasso regression are regularized methods.

• A. True
• B. False
• C. Neither
• D. Sometimes

Answer: (A)

Regularization involves adding a penalty term to the regression objective to discourage large coefficients and reduce overfitting. Ridge regression uses an L2 penalty (the sum of squared coefficients), which shrinks coefficients toward zero but generally doesn’t set them exactly to zero. Lasso regression uses an L1 penalty (the sum of absolute coefficients), which can drive some coefficients to zero, effectively performing feature selection. Because both methods modify the objective by including a penalty term to control model complexity, they are both regularized regression techniques. Ordinary least squares has no penalty, so it doesn’t regularize. Elastic net is another related approach that combines both penalties. This makes the statement true.

Regularization involves adding a penalty term to the regression objective to discourage large coefficients and reduce overfitting. Ridge regression uses an L2 penalty (the sum of squared coefficients), which shrinks coefficients toward zero but generally doesn’t set them exactly to zero. Lasso regression uses an L1 penalty (the sum of absolute coefficients), which can drive some coefficients to zero, effectively performing feature selection. Because both methods modify the objective by including a penalty term to control model complexity, they are both regularized regression techniques. Ordinary least squares has no penalty, so it doesn’t regularize. Elastic net is another related approach that combines both penalties. This makes the statement true.