Book Image

Advanced Machine Learning with R

By : Cory Lesmeister, Dr. Sunil Kumar Chinnamgari
Book Image

Advanced Machine Learning with R

By: Cory Lesmeister, Dr. Sunil Kumar Chinnamgari

Overview of this book

R is one of the most popular languages when it comes to exploring the mathematical side of machine learning and easily performing computational statistics. This Learning Path shows you how to leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. You’ll work through realistic projects such as building powerful machine learning models with ensembles to predict employee attrition. Next, you’ll explore different clustering techniques to segment customers using wholesale data and even apply TensorFlow and Keras-R for performing advanced computations. Each chapter will help you implement advanced machine learning algorithms using real-world examples. You’ll also be introduced to reinforcement learning along with its use cases and models. Finally, this Learning Path will provide you with a glimpse into how some of these black box models can be diagnosed and understood. By the end of this Learning Path, you’ll be equipped with the skills you need to deploy machine learning techniques in your own projects.
Table of Contents (30 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Chapter 9. Cluster Analysis

"Quickly bring me a beaker of wine, so that I may wet my mind and say something clever."

- Aristophanes, Athenian Playwright

In the earlier chapters, we focused on trying to learn the best algorithm in order to solve an outcome or response, for example, customer satisfaction or home prices. In all these cases, we had y, and that y is a function of x, or y = f(x). In our data, we had the actual y values and we could train x accordingly. This is referred to as supervised learning. However, there are many situations where we try to learn something from our data, and either we do not have the y, or we actually choose to ignore it. If so, we enter the world of unsupervised learning. In this world, we build and select our algorithm based on how well it addresses our business needs versus how accurate it is.

Why would we try and learn without supervision? First of all, unsupervised learning can help you understand and identify patterns in your data, which may be valuable...