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 for the first part of our course on regression,

we're gonna start with something that's called simple regression.

And as the name implies, it's just a very simple form of regression,

where we assume that we just have one input.

And we're just trying to fit a line.

Okay, but before we get to starting to talk about this simple regression model,

let's just recall our task of interest.

Where our case study is discussing how to predict house prices.

So in particular, we have some house that we wanna list for

sale, but we don't the value of this house.

And as we discussed at fairly great length in the first course of the specialization,

what we're going to do in this case,

is we're going to look at other houses that sold in the recent past.

And look at how much they've sold and different characteristics of those houses,

and use that data to inform our listing price for

our house that we'd like to sell.

[MUSIC]