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

Training models at scale


In an earlier section of this chapter, we listed and studied what the industry experts agree on as the most common phases of any predictive analytics project.

To recall, they are as follows:

  • Defining the data source

  • Profiling and preparation of the data source

  • Determining the question(s) that you want to ask your data

  • Choosing an algorithm to train on the data source

  • Application of a predictive model

In a predictive analytics project using big data, those same phases are present, but may be slightly varied and require some supplementary efforts.

Pain by phase

In the initial phase of a project, once you've chosen a source for your data (determined the data source), the data must be attained. Some industry experts describe this as the acquisition and recording of data. In a predictive project that involves a more common data source, access to the data might be as straightforward as opening a file on your local disk; with a big data source, it's a bit more difficult. For example...