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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

Classification trees and pruning

A classification tree is a particular type of decision tree, and its focus is mainly on classification problems. Breiman, et al. (1984) invented the decision tree and Quinlan (1984) independently introduced the C4.5 algorithm. Both of these had a lot in common, but we will focus on the Breiman school of decision trees. Hastie, et al. (2009) gives a comprehensive treatment of decision trees, and Zhang and Singer (2010) offer a treatise on the recursive partitioning methods. An intuitive and systematic R programmatic development of the trees can be found in Chapter 9, Ensembling Regression Models, of Tattar (2017).

A classification tree has many arguments that can be fine-tuned for improving performance. However, we will first simply construct the classification tree with default settings and visualize the tree. The rpart function from the rpart package can create classification, regression, as well as survival trees. The function first inspects whether the...

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