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

Latent Dirichlet Allocation


Latent Dirichlet Allocation (LDA) is the prototypical method of performing topic modeling. Rather unfortunately, the acronym LDA is also used for another method in machine learning. This latter method is completely different from LDA and is commonly used as a way to perform dimensionality reduction and classification.

Although LDA involves a substantial amount of mathematics, it is worth exploring some of its technical details in order to understand how the model works and the assumptions that it uses. First and foremost, we should learn about the Dirichlet distribution, which lends its name to LDA.

Note

An excellent reference for a fuller treatment of Topic Models with LDA is the Topic Models chapter in Text Mining: Classification, Clustering, and Applications, edited by A. Srivastava and M. Sahami and published by Chapman & Hall, 2009.

The Dirichlet distribution

Suppose we have a classification problem with K classes and the probability of each class is fixed...