案例学习：预测房价

Loading...

來自 University of Washington 的課程

机器学习：回归

3809 個評分

案例学习：预测房价

從本節課中

Multiple Regression

The next step in moving beyond simple linear regression is to consider "multiple regression" where multiple features of the data are used to form predictions. <p> More specifically, in this module, you will learn how to build models of more complex relationship between a single variable (e.g., 'square feet') and the observed response (like 'house sales price'). This includes things like fitting a polynomial to your data, or capturing seasonal changes in the response value. You will also learn how to incorporate multiple input variables (e.g., 'square feet', '# bedrooms', '# bathrooms'). You will then be able to describe how all of these models can still be cast within the linear regression framework, but now using multiple "features". Within this multiple regression framework, you will fit models to data, interpret estimated coefficients, and form predictions. <p>Here, you will also implement a gradient descent algorithm for fitting a multiple regression model.

- Emily FoxAmazon Professor of Machine Learning

Statistics - Carlos GuestrinAmazon Professor of Machine Learning

Computer Science and Engineering

[MUSIC]

Okay, so we've discussed at great length this multiple-regression model and

we've also talked about how to think about interpreting the coefficients

of the fitted model.

Now, let's turn to how to actually fit this model to a set of data.

And so you actually did this in the first course of this specialization,

you did a multiple regression fit.

But in that case, you're using some pre-implemented algorithms.

But, now what we're going to do it show you what's under the hood.

So, that you can go and implement these algorithm's yourself.

So, to be clear in this part of this module,

we're talking about the algorithms associated with multiple regression.

And in particular, like we did in the simple linear regression case,

we're gonna talk about two different algorithms.

One is just a closed form solution and the other is gradient descent.

And there are gonna be multiple steps that we have to take to build up to deriving

these algorithms and the first is simply to rewrite our regression model.

In particular,

we're gonna rewrite our multiple regression model, which is shown here.

This was the equation that was in that green box a little while ago.

So we're gonna rewrite this in matrix notation.

To do this, what we note is the fact that our ith observation,

which I'm gonna show as this pink square.

Can be written in terms of the multiplication of two vectors.

Where the first vector are gonna be all of our perimeters.

All of our regression coefficients, W0, W1, W2.

All the way up to W capital D, and then this green vector.

Which are all of our different features of the input so this is going to be h zero of

xi h1 of xi, h2 of xi,

all the way up to h capital D our Dth feature of xi.

And then we're going to add our error term which I'm going to use this grey square

to represent.

So when we multiply these two vectors, let's just describe what happens.

Well we take this first element,

multiply it by the first element of this blue vector.

And then we're gonna add the second element of this

green vector times the second element of this blue vector.

And keep doing this all along so what we get, just to be very explicit,

is w0 h0 xi plus, then when

we multiply the second elements we get w1 times h1 of xi.

Plus all the way up to the Dth.

The multiplication we do is wD, hD of xi,

and then we're gonna go and we're gonna add epsilon i.

Okay, so that's how we do a multiplication between a real vector and a column vector.

It's often called an inner product.

And what we can denote it by Is,

we're gonna call this entire vector here, we're gonna call it w.

And on this slide I'm gonna write it, I'm gonna emphasize the fact that it's bold

to capture my vector notation, but I'm not gonna go through that on every slide.

I'm just hoping that if I don't write a subscript like

wj that you remember that it's the vector.

Okay and this here, this vector, I'm going to write as

h, again this bold notation.

h of xi and x of course is bold as well.

Okay but in particular when I'm going to think of vectors

always as being defined as columns, as a column.

So a vertical, vertical line that we're showing here.

And if it defines a row, then I'm going to call that the transpose, okay?

The notation is you just put a little t on the top of the vector.

And what it means is take your vertical column and just lay it on its side.

So, let me take this pen, if this were my vector, and

I take its transpose, it's gonna become a row vector.

All the same elements, so if there are a bunch of elements in this vector,

they'll still appear in that order in the vector defined as a row.

Okay, and the same is gonna be true for matrices,

we're gonna talk about that later.

When we think about a square matrix and

think about the transpose we're just going to lay it on it's side.

Okay, so that's what transpose is.

So let's rewrite this multiplication, so what we have is this bold

w transpose times this bold h vector

of bold x of i plus epsilon i.

And epsilon i is just a scalar, just a little quiz, what's w transpose times h?

Is that a vector or a scalar?

It's a scalar.

Okay, so the result, and we can see that here, that this is just a scalar.

Okay, so equivalent to this.

Multiplying w transpose times h,

well you can also think about multiplying h transpose of x times w.

And the reason is because when you go through and

multiply, must be very pedantic here to be clear in these early slides

because we are going to be using this linear algebra again and again.

So I'll just write this out, of xi, w0,

w1, all the way up to wD.

When I go through and multiply these things out, it's exactly the same result.

I get h0, xI times w0, or you could have written that in the other order,

like I did before, h1, hi, w1 plus.

All the way up to hD(xi)wd + epsilon i.

So exactly the same as what we had before.

And we're gonna be using this notation throughout the rest of this course.

[MUSIC]