What regularization method uses the l2 norm for its regularization term?

Ridge regression employs the l2 norm for its regularization term, which helps mitigate issues such as overfitting in a regression model by penalizing the magnitude of the coefficients. This method adds a penalty equal to the square of the magnitude of coefficients to the loss function, effectively shrinking the coefficients towards zero but not eliminating them entirely.

The use of the l2 norm helps in cases where multicollinearity exists among independent variables, as it stabilizes the solutions by controlling the weights assigned to each feature. This effectively provides a balance between fitting the training data closely and maintaining model generalization for unseen data.

In contrast, Lasso regression, which uses the l1 norm, tends to produce sparse models by setting some coefficients exactly to zero, thereby performing variable selection. Elastic net regression combines both l1 and l2 norms, which provides a more flexible regularization strategy that can also handle situations where the number of predictors exceeds the number of observations.

Linear regression, on the other hand, does not implement any form of regularization and solely focuses on minimizing the residual sum of squares without any penalty on the coefficients. Thus, Ridge regression is the appropriate choice for the application of l2 norm regularization.