[MUSIC] So in summary, we've presented
this concept of ridge regression, which is a regularized form of
standard linear regression. It allows us to account for
having lots and lots of features in a very
straightforward way, both intuitively and algorithmically,
as we've explored in this module. And what ridge regression is
allowing us to do is automatically perform this bias variance tradeoff. So we thought about how to
perform ridge regression for a specific value of lambda, and
then we talked about this method of cross validation in order to select
the actual lambda we're gonna use for our models that we would
use to make predictions. So in summary, we've described why ridge regression
might be a reasonable thing to do. Motivating that the magnitude term
that ridge regression introduces, the magnitude of the coefficients. Penalizing that makes
sense from the standpoint of over-fitted models tend to have
very large magnitude coefficients. Then we talked about the actual
ridge regression objective and thinking about how it's balancing fit
with the magnitude of these coefficients. And we talked about how to fit the model both as a closed form solution
as well as creating a descent. And then how to choose our value of lambda
using cross validation, and that method generalizes well beyond regression,
let alone just ridge regression. And then finally, we talked about
how to deal with the intercept term, if you wanna handle that specifically. [MUSIC]