案例学习：预测房价

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.

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案例学习：预测房价

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

4.8（4,211 个评分）

- 5 stars3,426 ratings
- 4 stars666 ratings
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- 1 star32 ratings

PH

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.

FR

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

Nearest Neighbors & Kernel Regression

Up to this point, we have focused on methods that fit parametric functions---like polynomials and hyperplanes---to the entire dataset. In this module, we instead turn our attention to a class of "nonparametric" methods. These methods allow the complexity of the model to increase as more data are observed, and result in fits that adapt locally to the observations. <p> We start by considering the simple and intuitive example of nonparametric methods, nearest neighbor regression: The prediction for a query point is based on the outputs of the most related observations in the training set. This approach is extremely simple, but can provide excellent predictions, especially for large datasets. You will deploy algorithms to search for the nearest neighbors and form predictions based on the discovered neighbors. Building on this idea, we turn to kernel regression. Instead of forming predictions based on a small set of neighboring observations, kernel regression uses all observations in the dataset, but the impact of these observations on the predicted value is weighted by their similarity to the query point. You will analyze the theoretical performance of these methods in the limit of infinite training data, and explore the scenarios in which these methods work well versus struggle. You will also implement these techniques and observe their practical behavior.

#### Emily Fox

Amazon Professor of Machine Learning#### Carlos Guestrin

Amazon Professor of Machine Learning

[MUSIC]

So in summary, we've talked about nearest neighbor and kernel regression.

And these are, as we've seen, really simple approaches.

Very simple to think about intuitively, and

really simple to implement in practice.

But they have surprisingly good performance in just a very

wide range of different applications.

And some things in particular that we talked about in this module are how to

perform one nearest neighbor or k-NN regression.

And we also talked about ideas of weighting our k-NNs,

leading us to this idea of doing kernel regression.

And for this, there was this choice of our bandwidth parameter,

which is kind of akin to the k and k-NN.

And we said we could just choose this using cross validation.

And then we talked about some of the theoretical and

practical aspects of k-NN and kernel regression.

Talking about some really nice properties of k-NN as you get lots and lots of data.

But also some computational challenges that you run into.

And challenges you run into if you don't have a lot of data or

if you're in really high dimensional input spaces.

And finally, we talked about how one can use k-NN for classification.

And we're gonna talk a lot more about classification in the next course,

which is all about classification, so stay tuned for that course.

[MUSIC]