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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):

Tattar

View More author details

Table of Contents (17) Chapters

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

Summary

The main purpose of dealing with the bootstrap method in detail is that it lays the foundation for the resampling methods. We began the chapter with a very early resampling method: the jackknife method. This method is illustrated for the purpose of multiple scenarios, including survival data, which is inherently complex. The bootstrap method kicked off for seemingly simpler problems, and then we immediately applied it to complex problems, such as principal components and regression data. For the regression data, we also illustrated the bootstrap method for survival data and time series data. In the next chapter, we will look at the central role the bootstrap method plays in resampling decision trees, a quintessential machine learning tool.

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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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