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Machine Learning with R Cookbook, Second Edition

You're reading from   Machine Learning with R Cookbook, Second Edition Analyze data and build predictive models

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781787284395
Length 572 pages
Edition 2nd Edition
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Authors (2):
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Ashish Bhatia Ashish Bhatia
Author Profile Icon Ashish Bhatia
Ashish Bhatia
Yu-Wei, Chiu (David Chiu) Yu-Wei, Chiu (David Chiu)
Author Profile Icon Yu-Wei, Chiu (David Chiu)
Yu-Wei, Chiu (David Chiu)
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Toc

Table of Contents (15) Chapters Close

Preface 1. Practical Machine Learning with R FREE CHAPTER 2. Data Exploration with Air Quality Datasets 3. Analyzing Time Series Data 4. R and Statistics 5. Understanding Regression Analysis 6. Survival Analysis 7. Classification 1 - Tree, Lazy, and Probabilistic 8. Classification 2 - Neural Network and SVM 9. Model Evaluation 10. Ensemble Learning 11. Clustering 12. Association Analysis and Sequence Mining 13. Dimension Reduction 14. Big Data Analysis (R and Hadoop)

Introduction


Survival analysis is also known as duration analysis, transition analysis, failure time analysis, and time-to-event analysis. In survival analysis, subjects are tracked until an event happens or we lose them from the sample. Events can be anything, such as death, marriage, divorce, and so on. Subjects are followed for a specific time and the focus is on the time the event occurs. The three terms used commonly while talking about survival analysis are birth event, death event, and censorship. A birth event is the time when the observation started, a death event is the time at the end of the event or study, and censorship means a death event will not always occur. Censoring occurs because the subject does not experience the event during the time, the subject is lost, or the subject withdraws from the study. Censoring can be thought of as a kind of missing data problem. Consider the study of a clinical trial being conducted for 2 years; if the person under observation does not...

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