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

Improvements to the M5 model


The standard M5 algorithm tree currently has been received as the most state-of-the-art model among decision trees for completing complex regression tasks. This is mainly because of the accurate results it yields as well as its ability to handle tasks with a very large number of dimensions with upwards of hundreds of attributes.

In an attempt to improve on or otherwise optimize the standard M5 algorithm, M5Flex has recently been introduced as perhaps the most viable option. The M5Flex algorithm approach will attempt to augment a standard M5 tree model with domain knowledge. In other words, M5Flex empowers someone who has familiarity with the data population to review and choose the split attributes and split values for those important nodes (within the model tree) with the assumption that, since they may "know best," the resulting model will be even more accurate, consistent, and appropriate for practical applications than it would be by relying exclusively on...