Lasso regression is a type of linear regression that adds a regularization term to the loss function to
reduce overfitting and improve generalization. Lasso regression uses an L1 norm as the regularization
term, which is the sum of the absolute values of the coefficients. Lasso regression can shrink some of
the coefficients to zero, which effectively eliminates some of the features from the model. Lasso
regression is preferable over ridge regression when there are many features with no association with
the dependent variable, as it can perform feature selection and reduce the complexity and noise of
the model.
