In the context of regularization, what is the primary difference between L1 and L2 regularization?
- L1 regularization adds the absolute values of coefficients as a penalty, leading to feature selection
- L1 regularization adds the squared values of coefficients as a penalty, promoting sparsity
- L2 regularization adds the absolute values of coefficients as a penalty, promoting sparsity
- L2 regularization adds the squared values of coefficients as a penalty, leading to feature selection
L1 regularization, also known as Lasso, adds the absolute values of coefficients as a penalty, which promotes feature selection by driving some coefficients to zero. In contrast, L2 regularization, or Ridge, adds the squared values of coefficients as a penalty, which doesn't drive coefficients to zero and instead promotes a "shrinking" effect.
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