ridge-regression-and-the-lasso

This post will be about two methods that slightly modify ordinary least squares (OLS) regression – ridge regression and the lasso. Like OLS, ridge attempts to minimize residual sum of squares of predictors in a given model. However, ridge regression includes an additional'shrinkage' term – the square of the coefficient estimate – which shrinks the estimate of the coefficients towards zero. Two interesting implications of this design are the facts that when λ 0 the OLS coefficients are returned and when λ, coefficients will approach zero.