Book Image

Machine Learning with R

By : Brett Lantz
Book Image

Machine Learning with R

By: Brett Lantz

Overview of this book

Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of "big data" and "data science". Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from your data. "Machine Learning with R" is a practical tutorial that uses hands-on examples to step through real-world application of machine learning. Without shying away from the technical details, we will explore Machine Learning with R using clear and practical examples. Well-suited to machine learning beginners or those with experience. Explore R to find the answer to all of your questions. How can we use machine learning to transform data into action? Using practical examples, we will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process. We will learn how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data. "Machine Learning with R" will provide you with the analytical tools you need to quickly gain insight from complex data.
Table of Contents (19 chapters)
Machine Learning with R
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
9
Finding Groups of Data – Clustering with k-means
Index

Summary


Our findings support the popular adage that "birds of a feather flock together." By using machine learning methods to cluster teenagers with others who have similar interests, we were able to develop a typology of teen identities that was predictive of personal characteristics like gender and the number of friends. These same methods can be applied to other contexts with similar results.

This chapter covered only the fundamentals of clustering. As a very mature machine learning method, there are a myriad of variants to the k-means algorithm as well as many alternatives which bring unique biases and heuristics to the task. Based on what you have learned here, you will be able to understand and apply other clustering methods to new problems.

In the next chapter, we will begin to look at methods for measuring the success of a learning algorithm that are applicable across many machine learning tasks. While our process has always devoted some effort to evaluating the success of learning...