Lasso Regression, short for Least Absolute Shrinkage and Selection Operator, is a type of regression analysis that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model it produces. It does this by imposing a constraint on the coefficients, which drives some of them to be exactly zero. This effectively performs feature selection and helps in managing high-dimensional data.
