Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
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

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 Regression models – parametric and Cox proportional hazards models 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:

Regression models – parametric and Cox proportional hazards models

Here, t is the lifetime, Regression models – parametric and Cox proportional hazards models is the lifetime indicator, Regression models – parametric and Cox proportional hazards models is the covariate vector, Regression models – parametric and Cox proportional hazards models is the vector of regression coefficients, and Regression models – parametric and Cox proportional hazards models is the baseline hazard rate. A relative risks model that is of specific interest is the following:

Regression models – parametric and Cox proportional hazards models

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 Regression models – parametric and Cox proportional hazards models through a parametric model, for example...

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 $19.99/month. Cancel anytime
Banner background image