Lasso is a type of regression analysis method that performs both variable selection and regularization. It adds a penalty equal to the absolute value of the magnitude of coefficients to the least squares method. This penalty causes some regression coefficients to shrink toward zero, effectively performing variable selection. In simpler terms, Lasso helps in identifying the most important variables while discarding the less significant ones.
