Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
The Data Science Workshop

You're reading from   The Data Science Workshop Learn how you can build machine learning models and create your own real-world data science projects

Arrow left icon
Product type Paperback
Published in Aug 2020
Publisher Packt
ISBN-13 9781800566927
Length 824 pages
Edition 2nd Edition
Languages
Arrow right icon
Authors (5):
Arrow left icon
Robert Thas John Robert Thas John
Author Profile Icon Robert Thas John
Robert Thas John
Thomas Joseph Thomas Joseph
Author Profile Icon Thomas Joseph
Thomas Joseph
Anthony So Anthony So
Author Profile Icon Anthony So
Anthony So
Dr. Samuel Asare Dr. Samuel Asare
Author Profile Icon Dr. Samuel Asare
Dr. Samuel Asare
Andrew Worsley Andrew Worsley
Author Profile Icon Andrew Worsley
Andrew Worsley
+1 more Show less
Arrow right icon
View More author details
Toc

Table of Contents (16) Chapters Close

Preface
1. Introduction to Data Science in Python 2. Regression FREE CHAPTER 3. Binary Classification 4. Multiclass Classification with RandomForest 5. Performing Your First Cluster Analysis 6. How to Assess Performance 7. The Generalization of Machine Learning Models 8. Hyperparameter Tuning 9. Interpreting a Machine Learning Model 10. Analyzing a Dataset 11. Data Preparation 12. Feature Engineering 13. Imbalanced Datasets 14. Dimensionality Reduction 15. Ensemble Learning

Initializing Clusters

Since the beginning of this chapter, we've been referring to k-means every time we've fitted our clustering algorithms. But you may have noticed in each model summary that there was a hyperparameter called init with the default value as k-means++. We were, in fact, using k-means++ all this time.

The difference between k-means and k-means++ is in how they initialize clusters at the start of the training. k-means randomly chooses the center of each cluster (called the centroid) and then assigns each data point to its nearest cluster. If this cluster initialization is chosen incorrectly, this may lead to non-optimal grouping at the end of the training process. For example, in the following graph, we can clearly see the three natural groupings of the data, but the algorithm didn't succeed in identifying them properly:

Figure 5.26: Example of non-optimal clusters being found

k-means++ is an attempt to find better clusters...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at £16.99/month. Cancel anytime