Lasso, also known as L1 regularization, is a statistical technique used in regression analysis to enhance the prediction accuracy and interpretability of the resultant model. It achieves this by enforcing a penalty on the absolute size of the regression coefficients. This penalty forces some of the coefficients to be exactly zero, effectively performing variable selection and simplifying the model.
