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

Negative Binomial regression


While Poisson regression assumes a (known) average, Negative Binomial regression is implemented using what is referred to as maximum likelihood estimation.

Remember that, although Poisson distribution assumes that the mean and variance are the same, sometimes data will show greater variability or extra variation that is greater than the mean. When this occurs, Negative Binomial regression is a better choice because of its greater flexibility in that regard.

To illustrate, what if we consider that a university wants to predict the average number of days a student athlete may miss each year. Predictors (of the number of days of absence from class) include the type of sport the student athlete is a member of and their average GPA score. The variable sport is a four-level nominal variable indicating which sport the athlete participates in (in this case it's either Football, Track, Field Hockey, or Volleyball).

If we profile our data, suppose we find the following statistics...