In this recipe, we'll learn about ridge regression. It is different from vanilla linear regression; it introduces a regularization parameter to shrink coefficients. This is useful when the dataset has collinear factors.
Ridge regression is actually so powerful in the presence of collinearity that you can model polynomial features: vectors x, x2, x3, ... which are highly collinear and correlated.