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]

Okay, so in this module we've talked about simple linear regression.

And what we've seen is we've described what the model is.

We just have a single input, single output, and fitting a line or model.

It's just a simple line, to describe the relationship between our input x and

our output y.

We've talked about goodness of fit of a specific line to our data and the measure

being the residual sum of squares that we've talked about in this module.

And we've also talked about some ways to think about

interpreting our fitted line and using it to form predictions.

But a big emphasis was on thinking about how do we actually fit

that line to the data, and we talked about different optimization techniques.

The big one being, gradient descent, and using that to minimize our residual

sum squares, to come up with our fitted line that we're gonna use for predictions.

Even though this is a very very simple and

basic tool, it's actually incredibly powerful.

And we'll look at this in some of our assignments.

[MUSIC]