案例学习：预测房价

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案例学习：预测房价

Linear Regression, Ridge Regression, Lasso (Statistics), Regression Analysis

4.8（4,217 个评分）

- 5 stars3,430 ratings
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- 1 star32 ratings

Apr 07, 2016

This is an excellent course. The presentation is clear, the graphs are very informative, the homework is well-structured and it does not beat around the bush with unnecessary theoretical tangents.

Jan 02, 2017

This course is great. Things are very clearly explained. I am particularly happy because it helped me to understand many mathematical concepts. I will try not to be scared about formulas anymore.

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 Fox

Amazon Professor of Machine Learning#### Carlos Guestrin

Amazon Professor of Machine Learning

[MUSIC]

So that's our discussion on high leverage points and influential observations,

but I wanna think about whether if you go back to our data.

I think it's easier to discuss this, looking at our observations.

Here what we see on the top part,

there's a collection of five different observations.

So these are five different towns that have very high value

compared to what you see for all of the other towns.

So question is even though these points aren't high leverage points,

because they are in this typical x range, Are they influential observations?

Meaning if we remove these observations will the fit change very much.

So, not let's just see what happens in this data set.

Okay, so we're gonna remove these, what we're saying here we're gonna remove these

high value outlier neighborhoods and redo our analysis.

So what we're doing here is we're creating a data set,

which I'm gonna call sales underscore no high end for no high end towns.

Which takes our data set, still with center city removed, and

just filters out all the towns that have average values greater than $350,000.

Okay, so let's fit this new data set.

And again, let's compare coefficients.

So I'm gonna compare the coefficients to our fit with Center City removed

to the fit that further removes these high end houses,

or sorry, these high end towns.

And what you see is, yeah, there is some influence on The estimated coefficient.

But not nearly as significant as what we saw by simply removing center city.

So in this case, we've removed five observations

out of a total of 97 observations.

And we see that impact of crime rate on predicted decrease and

house value changes by a couple hundred dollars, but not by the amount that we saw

by just removing that one center city observation earlier on.

So this shows that high leverage points can be much more

likely to be influential observations for just small deviations from the data set.

Then outline observations that are within our x, our typical x range.

Okay, so the summary of all of this analysis and

discussion is the fact that when you have your data, and you're making some fit and

making predictions or interpreting the coefficients.

It's really, really important to do some data analysis to do

visualizations of your data or different checks for

whether you have these high leverage points or these outline observations and

checking whether they might potentially be these influential observations.

Because that can dramatically change how you're interpreting or

what you're predicting based on your estimated fit.

[MUSIC]