Book description
Explore powerful R packages to create predictive models using ensemble methods
Key Features
 Implement machine learning algorithms to build ensembleefficient models
 Explore powerful R packages to create predictive models using ensemble methods
 Learn to build ensemble models on large datasets using a practical approach
Book Description
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.
HandsOn 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 you'll 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 realworld examples.
What you will learn
 Carry out an essential review of resampling 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
Who this book is for
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.
Publisher resources
Table of contents

HandsOn Ensemble Learning with R
 Table of Contents
 HandsOn Ensemble Learning with R
 Contributors
 Preface
 1. Introduction to Ensemble Techniques
 2. Bootstrapping
 3. Bagging
 4. Random Forests
 5. The Bare Bones Boosting Algorithms
 6. Boosting Refinements
 7. The General Ensemble Technique
 8. Ensemble Diagnostics
 9. Ensembling Regression Models
 10. Ensembling Survival Models
 11. Ensembling Time Series Models
 12. What's Next?
 A. Bibliography
 Index
Product information
 Title: HandsOn Ensemble Learning with R
 Author(s):
 Release date: July 2018
 Publisher(s): Packt Publishing
 ISBN: 9781788624145
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