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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

Boosting regression models

Chapter 5, Boosting, introduced the boosting method for trees when we had a categorical variable of interest. The adaptation of boosting to the regression problem requires lot of computational changes. For more information, refer to papers by Zemel and Pitassi (2001), http://papers.nips.cc/paper/1797-a-gradient-based-boosting-algorithm-for-regression-problems.pdf , or Ridgeway, et al. (1999), http://dimacs.rutgers.edu/Research/MMS/PAPERS/BNBR.pdf.

The gbm function from the gbm library will be used to boost the weak learners generated by using random forests. We generate a thousand trees, n.trees=1e3, and use the shrinkage factor of 0.05, and then boost the regression trees using the gradient boosting algorithm for regression data:

> housing_gbm <- gbm(formula=HT_Formula,data=HT_Build,distribution = "gaussian",
+                    n.trees=1e3,shrinkage = 0.05,keep.data=TRUE,
+                    interaction.depth=1,
+                    cv.folds...

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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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