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

Simple Linear Regression

Our course starts from the most basic regression model: Just fitting a line to data. This simple model for forming predictions from a single, univariate feature of the data is appropriately called "simple linear regression".<p> In this module, we describe the high-level regression task and then specialize these concepts to the simple linear regression case. You will learn how to formulate a simple regression model and fit the model to data using both a closed-form solution as well as an iterative optimization algorithm called gradient descent. Based on this fitted function, you will interpret the estimated model parameters and form predictions. You will also analyze the sensitivity of your fit to outlying observations.<p> You will examine all of these concepts in the context of a case study of predicting house prices from the square feet of the house.

- Emily FoxAmazon Professor of Machine Learning

Statistics - Carlos GuestrinAmazon Professor of Machine Learning

Computer Science and Engineering

[MUSIC]

Well, we can use the same type of algorithm to find the minimum of

a function.

So, here, our interest is min over all w g of w.

And on this picture here, for this convex function, that's this point right here.

And, but let's think a little bit about what happens in this case.

So let's say we're starting at some w value here,

and I'd like to know whether I should move again to the left or to the right.

So increase or decrease w?

Well let's look at the derivative of the function.

And what I see is that the derivative is negative.

The derivative is negative, and yet in this case,

I want to be moving to the right and increasing w.

Now, let's look at a point on the other side of the optimum, so some point w here.

Look at the derivative.

In this case the derivative is positive and

when I ask whether I want to move to the left or to the right,

the answer in this case is, I want to decrease the value of w.

I want to move to the left.

So what we're saying is that,

when the derivative is

positive we want to decrease w and

when the derivative is negative,

you wanna increase w.

So again, in this picture I have that the derivative of this

function g everywhere on the left-hand side of the optimum,

in this case, is negative, everywhere on the right-hand side is positive.

So when I go to do what I'm gonna call a hill descent algorithm to contrast

with the hill climbing algorithm,

the update is gonna look almost exactly the same as the hill climbing.

Except because of what we just discussed,

instead of having a plus sign here, and moving in the same direction,

meaning the same sign of the derivative, we're going to move in the opposite.

Okay, so when the derivative, just to be very clear,

when the derivative is positive, what's going to happen?

Well this term is going to be negative, we're going to decrease w.

When the derivative is negative, this term,

this joint term here is going to be positive, we're going to increase w.

So that satisfies exactly what we stated here.

Okay.

So that is finding the minimum of a convex function.

And I wanna emphasize this slide right here because we're gonna be

looking at a lot of convex functions in this course, and in this module.

So this is really the picture that I want you to have in mind.

[MUSIC]