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Data Analysis with R

You're reading from  Data Analysis with R

Product type Book
Published in Dec 2015
Publisher
ISBN-13 9781785288142
Pages 388 pages
Edition 1st Edition
Languages
Toc

Table of Contents (20) Chapters close

Data Analysis with R
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
1. RefresheR 2. The Shape of Data 3. Describing Relationships 4. Probability 5. Using Data to Reason About the World 6. Testing Hypotheses 7. Bayesian Methods 8. Predicting Continuous Variables 9. Predicting Categorical Variables 10. Sources of Data 11. Dealing with Messy Data 12. Dealing with Large Data 13. Reproducibility and Best Practices Index

k-Nearest Neighbors


You're at a train terminal looking for the right line to stand in to get on the train from Upstate NY to Penn Station in NYC. You've settled into what you think is the right line, but you're still not sure because it's so crowded and chaotic. Not wanting to wait in the wrong line, you turn to the person closest to you and ask them where they're going: "Penn Station," says the stranger, blithely.

You decide to get some second opinions. You turn to the second closest person and the third closest person and ask them separately: Penn Station and Nova Scotia respectively. The general consensus seems to be that you're in the right line, and that's good enough for you.

If you've understood the preceding interaction, you already understand the idea behind k-Nearest Neighbors (k-NN hereafter) on a fundamental level. In particular, you've just performed k-NN, where k=3. Had you just stopped at the first person, you would have performed k-NN, where k=1.

So, k-NN is a classification...

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