案例学习：预测房价

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案例学习：预测房价

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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

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So let's generate some data and fit polynomials of increasing degrees and

see what happens to the estimated coefficients.

So to start with, let's just import some libraries that are gonna be useful.

And then we're gonna just create 30 different x values.

So, in the end we're gonna create a data set with 30 observations.

And then what we're gonna do is,

we're gonna compute the value of this sign functions.

So, evaluate the sign function at these 30 x values.

But of course when we're doing our analysis we're going to assume we have

noisy data so we're gonna add noise to these

true sign values to get our actual observations.

So here we're just adding noise.

And then we're gonna put this into an S frame.

And so here's what our data looks like.

We have a set of X values, and our corresponding Y values,

but of course, it's easier to just visualize what this data set looks like.

So let's just make a plot of X versus Y.

So, here you can see that there's an underlying trend like this so

the true trend like we talked about is this sin function.

So it's going up and coming down here and these black dots are our observed values.

Okay, so now let's get to our polynomial regression task and

to start with what we're gonna do is we're first just gonna.

Define our polynomial features so

what we're doing with this function polynomial underscore features is we're

taking our S frame and then we're just gonna make a copy of that S frame.

And for any degree polynomial that we're considering,

we're gonna manipulate the S frame to include extra columns that are powers

of X based on whatever degree we've specified for that polynomial.

So that's what this function does right here and then the very important function

is polynomial regression which is implementing our multiple regression model

using the features specified by this polynomial underscore features function.

So, again, for simplicity we're just using graphlab create and we're gonna

use the dot linear regression function where the features we're specifying

are just the powers specified by the degree of the polynomial we're looking at.

And then our target is our observation Y, and then there are these two

terms our l2 penalty and l1 penalty that we set to be equal to zero.

So this module on ridge regression is gonna be all about this l2 penalty,

and we're gonna get to that.

And then the next module is gonna be all about this l1 penalty,

but for now, let's just understand that if we set these values to

zero we just return to our standard least squares regression.

Okay, so that's what our polynomial regression function is doing.

And the next function we're gonna define allows us to plot our fit.

And finally we're gonna define a function that allows us to,

in a very nice way, print the coefficients of our polynomial regression.

And for this we're gonna use this numpy library because that allows for

this really pretty printing of our polynomial.

Okay, so now we're gonna use all these functions again and

again as we explore different degrees of polynomials fit to this data.

So to start with let's just consider fitting a very low order

degree two polynomial.

So first we're gonna do our polynomial regression fit taking our s-frame,

which we call data, and specifying that the degree is two for

this polynomial regression.

Then let's look at the coefficients that we've estimated.

And here's where we've done this really nice printing of these coefficients using

that NumPy library where here what we see is that

we have some coefficient on X squared, a coefficient on X,

and just our intercept term here.

And these values are, I don't know how to call them reasonable or

not reasonable, but they're relatively small numbers.

Number we can kind of appreciate, number like five and four and

something close to zero.

And now let's plot what our estimated fit looks like.

And this looks pretty good.

It's a nice smooth curve.

Goes between the values pretty well,

and in between values you'd imagine believing what this fit is.

But now let's go to a slightly higher degree polynomial just

a order four polynomial.

And here we're doing all the steps at once, where we're going to fit our model,

print the coefficients and plot the fit.

And so if we look at the estimated coefficients of our

fourth order polynomial, we see that the coefficients have increased in magnitude.

We have numbers like 23 and 53 and a 35.

And the fit is looking a bit wigglier.

Still actually looks pretty reasonable but now let's get to our degree 16 polynomial.

So remember we only have 30 observations and

we're trying to fit 16 order polynomial.

So what happens here?

So in this case,

we see that the coefficients have just become really, really massive.

Here we have 2.583 times 10 to the 6 and here 1.295 times 10 to the 7th.

So these are really, really, really large numbers.

And let's look at the fit.

As expected, this fit is also really wiggly and crazy.

And we probably don't believe that this is what's really going on in this data.

So this is an example pictorially of an overfit function.

But what we see in the take-home message from this demo is the fact that when we're

in these situations of being very overfit, then we get these very,

very, very large estimated coefficients associated with our model.

So yeah, whoa, these coefficients are crazy.

So what ridge regression is gonna do is it's going to quantify

overfitting through this measure of the magnitude of the coefficients.

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