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Applied Supervised Learning with R

You're reading from   Applied Supervised Learning with R Use machine learning libraries of R to build models that solve business problems and predict future trends

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Product type Paperback
Published in May 2019
Publisher
ISBN-13 9781838556334
Length 502 pages
Edition 1st Edition
Languages
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Authors (2):
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Jojo Moolayil Jojo Moolayil
Author Profile Icon Jojo Moolayil
Jojo Moolayil
Karthik Ramasubramanian Karthik Ramasubramanian
Author Profile Icon Karthik Ramasubramanian
Karthik Ramasubramanian
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Table of Contents (12) Chapters Close

Applied Supervised Learning with R
Preface
1. R for Advanced Analytics FREE CHAPTER 2. Exploratory Analysis of Data 3. Introduction to Supervised Learning 4. Regression 5. Classification 6. Feature Selection and Dimensionality Reduction 7. Model Improvements 8. Model Deployment 9. Capstone Project - Based on Research Papers Appendix

Cox Proportional-Hazards Regression Model


The basis for the Cox regression models comes from the survival analysis, a set of statistical methods helpful in investigating the time it takes for an event to occur. Some examples are as follows:

  • Time until a lead is converted to sales

  • Time until a product failure from the start of usage

  • Time after the start of the insurance policy until death

  • Time after diagnosing until death

  • Time until a warranty is claimed for a product

  • Time from customer registration

All these examples are some of the use cases of survival analysis. In most of the survival analysis, there are three wide-spread methods used for carrying out such time-to-event analysis:

  • Kaplan-Meier survival curves for analysis of different groups

  • The logrank test for comparing two or more survival curves

  • Cox proportional hazards regression to describe the effect of variables on survival

Keeping in mind the scope of this chapter and book, we will focus only on the Cox proportional hazards regression. The...

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