案例学习：预测房价

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来自 华盛顿大学 的课程

机器学习：回归

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案例学习：预测房价

从本节课中

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

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So let's go back to something a little bit more interesting, or

hopefully more interesting, I guess more interesting if you're a statistician.

Which is our regression model.

And in this slide, I'm being very careful about the boldface notation, and

you'll see that I'm very careful with this boldface notation throughout this course.

So, it's meaningful.

Okay, so when we have these multiple inputs,

the simplest models we can think of is just assuming that our eye thoughts or

vision is just a function directly of the inputs themselves.

Not other functions of the inputs just taking number of square feet,

number of bathrooms, number of bedrooms and

plugging those directly entirely into out linear model.

And again, we still have this noise term, epsilon i.

So, just to be very explicit about the features associated with

this simple hyperplane model, well,

the first feature in our model is just this one, this constant feature.

The second feature is the first input.

For example, number of square feet.

The third feature, indexing is weird, but third feature is our second input.

For example, number of bathrooms.

And this goes on and on till we get to our last input,

which is the little d+1 feature.

For example may be lot size.

For generically, instead of just a simple hyperplane just like we talked about,

instead of a single line, you can fit a polynomial.

Well instead of just a hyperplane, we can fit some D-dimensional curve.

This is capital D-dimensional curve.

Because we're gonna assume that there's some capital D different

features of this of these multiple inputs.

So just as an example, maybe our zero feature is just

that one constant term and that's pretty typical.

That just shifts up and down where this curve leads in the space and

maybe our first feature might be just our first input like in

the hyperplane example which is quite fit.

And the second feature, it could be the second input like in our

hyperplane example, or could be some other function of any of the inputs.

Maybe we wanna take log of the seventh input,

which happens to be number of bedrooms, times just the number of bathrooms.

So, in this case our second feature of the model is relating log number of bathrooms

times number, log number of bedrooms times number of bathrooms to the output.

And then we get all the way up to our capital D feature

which is some function of any of our inputs to our regression model.

So this is our generic multiple regression model with multiple features.

And again we can take this big sum and

represent it with this capita sigma notation.

So this formula, Yi, equals the sum of Wj, Hj of X, plus Epsilon i,

that is gonna be an equation that we're gonna use a lot.

That's why I put this green box around the equation.

This is an important equation that's gonna follow us for

the rest of this module, and throughout this course.

Okay, so just one more slide on notation.

We're gonna use capital N to represent the number of observation we have.

We're gonna use little d to represent the number of inputs, and we're gonna use

capital D to represent the number of features we take of those inputs.

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