案例学习：预测房价

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來自 University of Washington 的課程

机器学习：回归

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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

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So that's multiple regression.

And as you've seen, we've talked about what we said was the most widely used

machine learning tool out there, along with the most widely used

algorithmic tool, this gradient descent algorithm.

So this was a really, really important module that we've just covered.

And now that you've worked through this module, what you're able to do is

describe linear regression when you have multiple features of just a single input,

when you're talking about polynomial regression.

Or things like modeling seasonality and time series, but we also talked about how

to handle multiple different inputs, and features of these different inputs.

And all of these models fall in the context of multiple regression.

And for this multiple linear regression model we talked about how to fit

the model, using both a closed form solution as well as gradient descent.

And we also talked about how we can interpret the coefficients of some cases

of this multiple regression model as well as using it for prediction.

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