Lasso regression, or Least Absolute Shrinkage and Selection Operator, is a type of linear regression that includes a penalty term to constrain or shrink the coefficients of the model. This penalty term is the sum of the absolute values of the coefficients multiplied by a tuning parameter, which effectively performs variable selection by shrinking some coefficients to zero.
