Lasso Regression, or Least Absolute Shrinkage and Selection Operator, is a type of linear regression that uses regularization to enhance the prediction accuracy and interpretability of the statistical model it produces. By adding a penalty equal to the absolute value of the magnitude of coefficients, lasso regression can shrink some coefficients to zero, effectively performing both variable selection and regularization.
