Book Image

Principles of Data Science

Book Image

Principles of Data Science

Overview of this book

Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you’ll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas. With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you’ll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You’ll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means.
Table of Contents (20 chapters)
Principles of Data Science
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Choosing an optimal number for K and cluster validation


A big part of K-means clustering is knowing the optimal number of clusters. If we knew this number ahead of time, then that might defeat the purpose of even using unsupervised learning. So we need a way to evaluate the output of our cluster analysis.

The problem here is that because we are not performing any kind of prediction, we cannot gauge how right the algorithm is at predictions. Metrics such as accuracy and RMSE go right out of the window.

The Silhouette Coefficient

The Silhouette Coefficient is a common metric for evaluating clustering performance in situations when the true cluster assignments are not known.

A Silhouette Coefficient is calculated for each observation as follows:

Let's look a little closer at the specific features of this formula:

  • a: Mean distance to all other points in its cluster

  • b: Mean distance to all other points in the next nearest cluster

It ranges from -1 (worst) to 1 (best). A global score is calculated by...