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Hands-On Ensemble Learning with R

You're reading from   Hands-On Ensemble Learning with R A beginner's guide to combining the power of machine learning algorithms using ensemble techniques

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Product type Paperback
Published in Jul 2018
Publisher Packt
ISBN-13 9781788624145
Length 376 pages
Edition 1st Edition
Languages
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Author (1):
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Prabhanjan Narayanachar Tattar Prabhanjan Narayanachar Tattar
Author Profile Icon Prabhanjan Narayanachar Tattar
Prabhanjan Narayanachar Tattar
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Table of Contents (15) Chapters Close

Preface 1. Introduction to Ensemble Techniques FREE CHAPTER 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

Chapter 5. The Bare Bones Boosting Algorithms

What do we mean by bare bones boosting algorithms? The boosting algorithm (and its variants) is arguably one of the most important algorithms in the machine learning toolbox. Any data analyst needs to know this algorithm, and eventually the push for higher accuracy invariably drives towards the need for the boosting technique. It has been reported on the www.kaggle.org forums that boosting algorithms for complex and voluminous data run for several weeks and that most award-winning solutions are based on this. Furthermore, the algorithms run on modern graphical device machines.

Taking its importance into account, we will study the boosting algorithm in detail here. Bare bones is certainly not a variant of the boosting algorithm. Since the boosting algorithm is one of the very important and vital algorithms, we will first state the algorithm and implement it in a rudimentary fashion, which will show each step of the algorithm in action...

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