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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

Product type Paperback

Published in Jul 2018

Publisher Packt

ISBN-13 9781788624145

Length 376 pages

Edition 1st Edition

Languages

R

Concepts

Machine Learning

Author (1):

Prabhanjan Narayanachar Tattar

View More author details

Table of Contents (15) Chapters

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

R package references

Index

Core concepts of survival analysis

Survival analysis deals with censored data, and it is very common that parametric models are unsuitable for explaining the lifetimes observed in clinical trials.

Let T denote the survival time, or the time to the event of interest, and we will naturally have , which is a continuous random variable. Suppose that the lifetime cumulative distribution is F and the associated density function is f. We define important concepts as required for further analysis. We will explore the concept of survival function next.

Suppose that T is the continuous random variable of a lifetime and that the associated cumulative distribution function is F. The survival function at time t is the probability the observation is still alive at the time, and it is defined by the following:

The survival function can take different forms. Let's go through some examples for each of the distributions to get a clearer picture of the difference in survival functions.

Exponential Distribution...

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Authors (1)

Tattar

Tattar

Prabhanjan Narayanachar Tattar is a lead statistician and manager at the Global Data Insights & Analytics division of Ford Motor Company, Chennai. He received the IBS(IR)-GK Shukla Young Biometrician Award (2005) and Dr. U.S. Nair Award for Young Statistician (2007). He held SRF of CSIR-UGC during his PhD. He has authored books such as Statistical Application Development with R and Python, 2nd Edition, Packt; Practical Data Science Cookbook, 2nd Edition, Packt; and A Course in Statistics with R, Wiley. He has created many R packages.

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