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Hands-On Ensemble Learning with R

You're reading from  Hands-On Ensemble Learning with R

Product type Book
Published in Jul 2018
Publisher Packt
ISBN-13 9781788624145
Pages 376 pages
Edition 1st Edition
Languages
Author (1):
Prabhanjan Narayanachar Tattar Prabhanjan Narayanachar Tattar
Profile icon Prabhanjan Narayanachar Tattar
Toc

Table of Contents (17) Chapters close

Hands-On 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?
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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