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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 10. Ensembling Survival Models

The primary biliary cirrhosis data was introduced in first two chapters using the jackknife method. Observations in clinical trials are usually subject to censoring, and the jackknife method helps to complete incomplete observations through the idea of pseudo values. Since pseudo values are likely to be dependent on each other, the generalized estimating equation framework made it possible to estimate the impact of twelve covariates at the time of interest. The idea of pseudo values and the generalized estimating equation framework makes it easy for practitioners to interpret the results. However, this method might not be useful if the number of censored observations is exceptionally high. Furthermore, it is also preferable to have statistical methods that preserve the incompleteness of the observations and yet make good use of them. The general (linear) regression framework with time as the dependent variable and the error term following...

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