Lasso Regression

Lasso (Least Absolute Shrinkage and Selection Operator) regression is a linear regression technique that adds a penalty term to the ordinary least squares (OLS) objective function. This penalty is based on the absolute values of the coefficients, effectively shrinking some coefficients towards zero. This shrinkage not only helps prevent overfitting, especially when dealing with high-dimensional data, but also performs feature selection by potentially eliminating less important features from the model.