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

Defining DR


It is a most commonly accepted rule of thumb that it is difficult to understand or visualize data represented in or by more than three dimensions.

Dimensional (-ity) reduction is the process of attempting to reduce the number of random variables (or data dimensions) under statistical consideration, or perhaps better put: finding a lower-dimensional representation of the feature-set that is of interest.

This allows the data scientist to:

  • Avoid what is referred to as the curse of dimensionality

    Note

    The curse of dimensionality refers to a phenomenon that arises when attempting to analyze data in high-dimensional spaces (often with hundreds or thousands of dimensions) that do not occur in low-dimensional settings or everyday experience.

  • Reduce the amount of time and memory required for the proper analysis of the data

  • Allow the data to be more easily visualized

  • Eliminate features irrelevant to the model's purpose

  • Reduce model noise

A useful (albeit perhaps over-used) conceptual example of...