Lasso, or Least Absolute Shrinkage and Selection Operator, is a regression analysis method that performs both variable selection and regularization to enhance the prediction accuracy and interpretability of the statistical model it produces. It is particularly useful when dealing with high-dimensional data, such as genomic data in cancer research.
