Book Image

Mastering Predictive Analytics with R - Second Edition

By : James D. Miller, Rui Miguel Forte
Book Image

Mastering Predictive Analytics with R - Second Edition

By: James D. Miller, Rui Miguel Forte

Overview of this book

R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems. The book begins with a dedicated chapter on the language of models and the predictive modeling process. You will understand the learning curve and the process of tidying data. Each subsequent chapter tackles a particular type of model, such as neural networks, and focuses on the three important questions of how the model works, how to use R to train it, and how to measure and assess its performance using real-world datasets. How do you train models that can handle really large datasets? This book will also show you just that. Finally, you will tackle the really important topic of deep learning by implementing applications on word embedding and recurrent neural networks. By the end of this book, you will have explored and tested the most popular modeling techniques in use on real- world datasets and mastered a diverse range of techniques in predictive analytics using R.
Table of Contents (22 chapters)
Mastering Predictive Analytics with R Second Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
8
Dimensionality Reduction
Index

Modeling the topics of online news stories


To see how topic models perform on real data, we will look at two datasets containing articles originating from BBC News during the period of 2004-2005. The first dataset, which we will refer to as the BBC dataset, contains 2,225 articles that have been grouped into five topics. These are business, entertainment, politics, sports, and technology.

The second dataset, which we will call the BBCSports dataset, contains 737 articles only on sports. These are also grouped into five categories according to the type of sport being described. The five sports in question are athletics, cricket, football, rugby, and tennis. Our objective will be to see if we can build topic models for each of these two datasets that will group together articles from the same major topic.

Note

Both BBC datasets were presented in a paper by D. Greene and P. Cunningham, entitled Producing Accurate Interpretable Clusters from High-Dimensional Data and published in the proceedings...