Book Image

Mastering Machine Learning with R, Second Edition - Second Edition

Book Image

Mastering Machine Learning with R, Second Edition - Second Edition

Overview of this book

This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more. You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you’ll understand how these concepts work and what they do. With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets.
Table of Contents (23 chapters)
Title Page
Credits
About the Author
About the Reviewers
Packt Upsell
Customer Feedback
Preface
16
Sources

Chapter 11. Creating Ensembles and Multiclass Classification

"This is how you win ML competitions: you take other people's work and ensemble them together."                                                                                        - Vitaly Kuznetsov, NIPS2014

You may have already realized that we have discussed ensemble learning. It is defined by www.scholarpedia.org as "the process by which multiple models, such as classifiers or experts, are strategically generated and combined to solve a particular computational intelligence problem". In random forest and gradient boosting, we combined the "votes" of hundreds or thousands of trees to make a prediction. Thus, by definition, those models are ensembles. This methodology can be extended to any learner to create ensembles, which some refer to as meta-ensembles or meta-learners. We will look at one of these methods referred to as "stacking". In this methodology, we will produce a number of classifiers and use their predicted class...