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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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，

從本節課中

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

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In this module, we've seen how regression can be used to predict house prices and

also be useful in a wide range of other applications.

So, in the introduction to this course, Carlos introduced this machine

learning pipeline, where we go from data, which gets shoved

into some machine learning method, and we use that to derive intelligence.

Well, let's dig into this block diagram and expand it, now that we've seen

these some of these machine learning tools, in a little bit more detail.

Okay, so now we know what we actually use to fit our data is some training data set,

so that's gonna be our data.

And in our housing application,

where we're going to predict the price of some house,

the data that we collected was, we had this table of the house ID,

and some set of house attributes, as well as the house's sales price.

And we had this for a whole bunch of houses in our neighborhood, and

we collected this data into some table.

So, that represented our training data set.

And then we took that data, and what we did was shoved it through some feature

extractor, which in this case is a very simple feature extractor,

where we just choose some subset of the house attributes.

So, in the examples we looked at, X, our set of features,

represented things, like, we looked at square feet of the house,

and we also looked at number of bathrooms.

And we talked about possibly using more features.

Again, we'll talk about that more in the regression course, but

those were two that we examined in this module.

And so then, what did we do with these features?

Well, our goal was to take these features and

have some type of model that led to a prediction of the house price.

Okay, so our output, the intelligence that

we're deriving, is the predicted house price.

And we're going to do this for every house in our training data set.

We're gonna take its associated features, and

shove it through this machine learning model, and predict the house price.

And what's the machine learning model we talked about?

Well, in this case, it's regression.

That's our specific machine learning model that we're looking at here.

Okay, but remember that this machine learning model had some set of parameters.

Okay, so the parameters we call W.

These are the weights on our features.

So, for example, it's the weight on square feet or number of bathrooms, and so on.

More technically, these are called regression coefficients.

And we talked about estimating these parameters from data,

so our W hat is our estimate of these parameters.

And how did we talk about doing that?

Well, we took our predicted output, so

our predicted house price, and we compared it to the true house price.

So, the actual sales price that we recorded in our training data table.

So, Y, here, is our actual sales

price for the houses in our training data.

And we compare to the predicted house price.

And we use a quality metric to measure how well we're doing with our prediction,

using our model, using W hat as the parameters of that model.

Well, how well are we doing?

What was the error metric we talked about?

The error metric we talked about was something called residual sum of squares,

where we just sum up the square difference between the actual house sales price and

the predicted house sales price, summing over all houses in our training data set.

Okay, so our quality metric is gonna take our predictions and

our actual house sales observations, spit out this error, and

it's gonna go into a machine learning algorithm that's gonna be used to update

the weights, update our parameters of our model.

And we're gonna talk about this machine learning algorithm, or

different variants of it, a lot more in the actual course on regression.

But this is the overall flowchart for this machine learning method for

our house prediction problem.

And this loop here, where we're taking our predictions,

computing our error relative to the actual house sales prices,

and updating the weights or our model parameters.

This process tends to happen in an iterative way,

where we update values again and again.

Okay, so if we abstract a way, what we see is we have some training data set.

We have some feature extraction process.

We have some machine learning model.

And it's gonna produce some intelligence, which in this case is a prediction.

And we're gonna assess the quality of our intelligence with some quality measure.

And we're gonna use that error or accuracy, depending which way we're

thinking about measuring it, to adjust our model parameters using some algorithm.

And we're gonna see this type of flow for machine learning again and again.

In this module, we've seen how to take our data and

derive intelligence using something called regression,

where we have a model that relates our features to our output.

And we talked about this in the context of predicting house values, and

you also work through a really interesting IPython notebook.

And from this, you should be able to deploy really interesting regression

models in practice now.

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