案例学习：预测房价

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來自 University of Washington 的課程

机器学习：回归

4068 個評分

案例学习：预测房价

從本節課中

Ridge Regression

You have examined how the performance of a model varies with increasing model complexity, and can describe the potential pitfall of complex models becoming overfit to the training data. In this module, you will explore a very simple, but extremely effective technique for automatically coping with this issue. This method is called "ridge regression". You start out with a complex model, but now fit the model in a manner that not only incorporates a measure of fit to the training data, but also a term that biases the solution away from overfitted functions. To this end, you will explore symptoms of overfitted functions and use this to define a quantitative measure to use in your revised optimization objective. You will derive both a closed-form and gradient descent algorithm for fitting the ridge regression objective; these forms are small modifications from the original algorithms you derived for multiple regression. To select the strength of the bias away from overfitting, you will explore a general-purpose method called "cross validation". <p>You will implement both cross-validation and gradient descent to fit a ridge regression model and select the regularization constant.

- Emily FoxAmazon Professor of Machine Learning

Statistics - Carlos GuestrinAmazon Professor of Machine Learning

Computer Science and Engineering

[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]