Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Jul 2018
Publisher Packt
ISBN-13 9781788624145
Length 376 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Prabhanjan Narayanachar Tattar Prabhanjan Narayanachar Tattar
Author Profile Icon Prabhanjan Narayanachar Tattar
Prabhanjan Narayanachar Tattar
Arrow right icon
View More author details
Toc

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...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime