You're reading from scikit-learn Cookbook - Second Edition

Product typeBook

Published inNov 2017

Reading LevelIntermediate

PublisherPackt

ISBN-139781787286382

Edition2nd Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Author (1)

Trent Hauck

Using k-means for outlier detection

In this recipe, we'll look at both the debate and mechanics of k-means for outlier detection. It can be useful to isolate some types of errors, but care should be taken when using it.

Getting ready

We'll use k-means to do outlier detection on a cluster of points. It's important to note that there are many camps when it comes to outliers and outlier detection. On one hand, we're potentially removing points that were generated by the data-generating process by removing outliers. On the other hand, outliers can be due to a measurement error or some other outside factor.

This is the most credence we'll give to the debate. The rest of this recipe is about finding outliers...

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scikit-learn Cookbook - Second Edition

Published in: Nov 2017Publisher: PacktISBN-13: 9781787286382

Author (1)

Trent Hauck

Trent Hauck is a data scientist living and working in the Seattle area. He grew up in Wichita, Kansas and received his undergraduate and graduate degrees from the University of Kansas. He is the author of the book Instant Data Intensive Apps with pandas How-to, Packt Publishing—a book that can get you up to speed quickly with pandas and other associated technologies.
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