Prabhanjan Narayanachar Tattar - Hands-On Ensemble Learning with R: A beginners guide to combining the power of machine learning algorithms using ensemble techniques (English Edition)
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Explore powerful R packages to create predictive models using ensemble methods
Key Features- Implement machine learning algorithms to build ensemble-efficient models
- Explore powerful R packages to create predictive models using ensemble methods
- Learn to build ensemble models on large datasets using a practical approach
Ensemble techniques are used for combining two or more similar or dissimilar machine learning algorithms to create a stronger model. Such a model delivers superior prediction power and can give your datasets a boost in accuracy.
Hands-On Ensemble Learning with R begins with the important statistical resampling methods. You will then walk through the central trilogy of ensemble techniques bagging, random forest, and boosting then youll learn how they can be used to provide greater accuracy on large datasets using popular R packages. You will learn how to combine model predictions using different machine learning algorithms to build ensemble models. In addition to this, you will explore how to improve the performance of your ensemble models.
By the end of this book, you will have learned how machine learning algorithms can be combined to reduce common problems and build simple efficient ensemble models with the help of real-world examples.
What you will learn- Carry out an essential review of re-sampling methods, bootstrap, and jackknife
- Explore the key ensemble methods: bagging, random forests, and boosting
- Use multiple algorithms to make strong predictive models
- Enjoy a comprehensive treatment of boosting methods
- Supplement methods with statistical tests, such as ROC
- Walk through data structures in classification, regression, survival, and time series data
- Use the supplied R code to implement ensemble methods
- Learn stacking method to combine heterogeneous machine learning models
This book is for you if you are a data scientist or machine learning developer who wants to implement machine learning techniques by building ensemble models with the power of R. You will learn how to combine different machine learning algorithms to perform efficient data processing. Basic knowledge of machine learning techniques and programming knowledge of R would be an added advantage.
Table of Contents- Introduction to Ensemble Techniques
- Bootstrapping
- Bagging
- Random Forests
- The Bare Bones Boosting Algorithms
- Boosting Refinements
- The General Ensemble Technique
- Ensemble Diagnostics
- Ensembling Regression Models
- Ensembling Survival Models
- Ensembling Time Series Models
- Whats Next?
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Copyright 2018 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Commissioning Editor: Sunith Shetty
Acquisition Editor: Tushar Gupta
Content Development Editor: Aaryaman Singh
Technical Editor: Dinesh Chaudhary
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Graphics: Jisha Chirayil
Production Coordinator: Nilesh Mohite
First published: July 2018
Production reference: 1250718
Published by Packt Publishing Ltd.
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ISBN 978-1-78862-414-5
www.packtpub.com
On the personal front, I continue to benefit from the support of my family: my daughter, Pranathi; my wife, Chandrika; and my parents, Lakshmi and Narayanachar. The difference in their support from acknowledgement in earlier books is that now I am in Chennai and they support me from Bengaluru. It involves a lot of sacrifice to allow a writer his private time with writing. I also thank my managers, K. Sridharan, Anirban Singha, and Madhu Rao, at Ford Motor Company for their support. Anirban had gone through some of the draft chapters and expressed confidence in the treatment of topics in the book.
My association with Packt is now six years and four books! This is the third title I have done with Tushar Gupta and it is needless to say that I enjoy working with him. Menka Bohra and Aaryaman Singh have put a lot of faith in my work and strived to accommodate the delays, so special thanks to both of them. Manthan Patel and Snehal Kolte have also extended their support. Finally, it is a great pleasure to thank Storm Mann for improving the language of the book. If you still come across a few mistakes, the blame is completely mine.
It is a pleasure to dedicate this book to them for all their support.
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Prabhanjan Narayanachar Tattar is a lead statistician and manager at the Global Data Insights & Analytics division of Ford Motor Company, Chennai. He received the IBS(IR)-GK Shukla Young Biometrician Award (2005) and Dr. U.S. Nair Award for Young Statistician (2007). He held SRF of CSIR-UGC during his PhD. He has authored books such as Statistical Application Development with R and Python, 2nd Edition , Packt; Practical Data Science Cookbook, 2nd Edition , Packt; and A Course in Statistics with R , Wiley. He has created many R packages.
The statistics and machine learning community, powered by software engineers, is striving to make the world a better, safer, and more efficient place. I would like to thank these societies on behalf of the reader.
Antonio L. Amadeu is a data science consultant and is passionate about artificial intelligence and neural networks. He uses machine learning and deep learning algorithms in his daily challenges, solving all types of issues in any business field. He has worked for Unilever, Lloyds Bank, TE Connectivity, Microsoft, and Samsung. As an aspiring astrophysicist, he does some research with the Virtual Observatory group at So Paulo University in Brazil, a member of the International Virtual Observatory Alliance IVOA.
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Ensemble learning! This specialized topic of machine learning broadly deals with putting together multiple models with the aim of providing higher accuracy and stable model performance. The ensemble methodology is based on sound theory and its usage has seen successful applications in complex data science scenarios. This book grabs the opportunity of dealing with this important topic.
Moderately sized datasets are used throughout the book. All the conceptswell, most of themhave been illustrated using the software, and R packages have been liberally used to drive home the point. While care has been taken to ensure that all the codes are error free, please feel free to write us with any bugs or errors in the codes. The approach has been mildly validated through two mini-courses based on earlier drafts. The material was well received by my colleagues and that gave me enough confidence to complete the book.
The Packt editorial team has helped a lot with the technical review, and the manuscript reaches you after a lot of refinement. The bugs and shortcomings belong to the author.
This book is for anyone who wants to master machine learning by building ensemble models with the power of R. Basic knowledge of machine learning techniques and programming knowledge of R are expected in order to get the most out of the book.
, Introduction to Ensemble Techniques , will give an exposition to the need for ensemble learning, important datasets, essential statistical and machine learning models, and important statistical tests. This chapter displays the spirit of the book.
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