Reader small image

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

Product typeBook
Published inNov 2017
Reading LevelIntermediate
PublisherPackt
ISBN-139781787286382
Edition2nd Edition
Languages
Right arrow
Author (1)
Trent Hauck
Trent Hauck
author image
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.
Read more about Trent Hauck

Right arrow

Optimizing the number of centroids

When doing k-means clustering, we really do not know the right number of clusters in advance, so finding this out is an important step. Once we know (or estimate) the number of centroids, the problem will start to look more like a classification one as our knowledge to work with will have increased substantially.

Getting ready

Evaluating the model performance for unsupervised techniques is a challenge. Consequently, sklearn has several methods for evaluating clustering when a ground truth is known, and very few for when it isn't.

We'll start with a single cluster model and evaluate its similarity. This is more for the purpose of mechanics as measuring the similarity of one cluster...

lock icon
The rest of the page is locked
Previous PageNext Page
You have been reading a chapter from
scikit-learn Cookbook - Second Edition
Published in: Nov 2017Publisher: PacktISBN-13: 9781787286382

Author (1)

author image
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.
Read more about Trent Hauck