你是否好奇数据可以告诉你什么？你是否想在关于机器学习促进商业的核心方式上有深层次的理解？你是否想能同专家们讨论关于回归，分类，深度学习以及推荐系统的一切？在这门课上，你将会通过一系列实际案例学习来获取实践经历。在这门课结束的时候，

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来自 University of Washington 的课程

机器学习基础：案例研究

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你是否好奇数据可以告诉你什么？你是否想在关于机器学习促进商业的核心方式上有深层次的理解？你是否想能同专家们讨论关于回归，分类，深度学习以及推荐系统的一切？在这门课上，你将会通过一系列实际案例学习来获取实践经历。在这门课结束的时候，

从本节课中

Regression: Predicting House Prices

This week you will build your first intelligent application that makes predictions from data.<p>We will explore this idea within the context of our first case study, predicting house prices, where you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). <p>This is just one of the many places where regression can be applied.Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression.</p>You will also examine how to analyze the performance of your predictive model and implement regression in practice using an iPython notebook.

- Carlos GuestrinAmazon Professor of Machine Learning

Computer Science and Engineering - Emily FoxAmazon Professor of Machine Learning

Statistics

[MUSIC]

So for each model order that we might consider,

for example, a linear model or

we also talked about using a quadratic model,

all the way up to our very crazy, 13th order polynomial.

And of course, we could consider even higher order models.

Well, what happens to test error?

Sorry, I don't mean test error.

Let's start with training error.

A lot easier to think about.

So training error.

As we increase the model order, well, that models able

to better and better fit the observations in that training dataset.

So what we're gonna have is we're gonna have that our training error

decreases with increasing model order.

So remember the curves that we had.

We had the residual sum of squares associated with that linear fit,

quadratic fit, all the way up to the 13th order polynomial that basically hit

each one of those observations.

So, we saw that a residual sum of squares was going down and down and down.

So that's true even if we hold out some observations and

just look at our training dataset.

So we're gonna have our training error decreasing and

decreasing as we increase the flexibility of the model.

But, so let's just annotate this as being our training error,

particularly for our estimated model parameters w hat.

So let's be clear about what we mean by w hat.

So for every model complexity such as linear model, quadratic model, and so on.

What we're gonna do is we're gonna optimize and

find the parameters w hat for the linear model.

We're searching over all possible lines minimizing the training error.

Remember that's what we said,

couple slides ago we said that the way we estimate our model is we're gonna

minimize the air on that observations in our training dataset.

So that's how we get w hat for the linear model,

and we compute the training error associated with that w hat.

Then we look at all possible quadratic fits.

Minimize the training error for over all the quadratic fits,

that's how we get w hat for the quadratic model.

And then we plot the training error associated with the w hat for

the quadratic model and so on.

Well, we can also talk about test error, but

here it's a little bit more complicated because what do we think is gonna

happen as we increase and increase our model order?

Well, what we saw, if you remember that 13th order polynomial fit,

that really crazy wiggly line, we had really, really bad predictions.

So when we think about holding out our test data, fitting a 13th order

polynomial just on the training data, we're gonna get some wiggly, crazy fit.

And then when we look at those test observations, those houses that we

held out, we're probably gonna have very poor predictions of those actual values.

So what we're gonna expect is that at some point,

our test error is likely to increase.

So the curves for test error tend to look something like the following

where maybe the error is going down for some period of time but after a point

The error starts to increase again.

So here this curve is our test error for

these fitted models,

where the models were fit

using the training data.

So these are what curves tend to look like for training error and

test error as a function of model complexity, and

how we think about using these ideas to actually select the model or

the complexity of the model that we should use for making our predictions.

We're gonna discuss in a lot more detail in the regression and

classification courses.

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