Ridge and Lasso Regression

You might have worked on some simple linear regression using ordinary least squares, and its more general regression of polynomial functions. You've also seen how we can overfit models to data using polynomials and interactions. In this blog post, I want to take a look at another way to tune our linear regression models. These methods all modify the mean squared error function that you are optimizing against. The modifications will add a penalty for large coefficient weights in the resulting model. Ridge and Lasso regression are some of the simple techniques to reduce model complexity and prevent over-fitting.