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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

R package references

Prabhanjan Tattar (2015). ACSWR: A Companion Package for the Book "A

Course in Statistics with R". R package version 1.0.

https://CRAN.R-project.org/package=ACSWR

Alfaro, E., Gamez, M. Garcia, N.(2013). adabag: An R Package for

Classification with Boosting and Bagging. Journal of Statistical

Software, 54(2), 1-35. URL http://www.jstatsoft.org/v54/i02/.

Angelo Canty and Brian Ripley (2017). boot: Bootstrap R (S-Plus)

Functions. R package version 1.3-19.

John Fox and Sanford Weisberg (2011). An {R} Companion to Applied

Regression, Second Edition. Thousand Oaks CA: Sage. URL:

http://socserv.socsci.mcmaster.ca/jfox/Books/Companion car

Max Kuhn. Contributions from Jed Wing, Steve Weston, Andre Williams,

Chris Keefer, Allan Engelhardt, Tony Cooper, Zachary Mayer, Brenton

Kenkel, the R Core Team, Michael Benesty, Reynald Lescarbeau, Andrew

Ziem, Luca Scrucca, Yuan Tang, Can Candan and Tyler Hunt. (2017).

caret: Classification and Regression Training. R package...

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