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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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 Tattar Tattar
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Tattar
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Table of Contents (17) Chapters Close

Hands-On Ensemble Learning with R
Contributors
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?
Bibliography Index

Regression models – parametric and Cox proportional hazards models


You may recall that the survival data consists of complete as well as censored observations, and we saw that the lifetimes look like 400, 4500+, 1012, 1925, 1504+, … for the pbc dataset. Although the lifetimes are continuous random variables, a regression model of the form will not be appropriate here. In fact, there were many attempts to correct and improvise on models of this form in the 1970s, and most often the results were detrimental. We will define a generic hazards regression model as follows:

Here, t is the lifetime, is the lifetime indicator, is the covariate vector, is the vector of regression coefficients, and is the baseline hazard rate. A relative risks model that is of specific interest is the following:

We will focus solely on this class of model. First, the parametric hazards regression is considered. This means that we will specify the hazard rate through a parametric model, for example, through exponential...

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