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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

The boot package

The boot package is one of the core R packages, and it is optimized for the implementation of bootstrap methods. In the previous examples, we mostly used loops for carrying out the resampling technique. Here, we will look at how to use the boot R package.

The main structure of the boot function is as follows:

boot(data, statistic, R, sim = "ordinary", stype = c("i", "f", "w"), 
     strata = rep(1,n), L = NULL, m = 0, weights = NULL, 
     ran.gen = function(d, p) d, mle = NULL, simple = FALSE, ...,
     parallel = c("no", "multicore", "snow"),
     ncpus = getOption("boot.ncpus", 1L), cl = NULL)

The central arguments of the function are data, statistic, R, and stype. The data argument is the standard one, as with most R functions. The statistic is the most important argument for the implementation of the boot function and it is this function that will be applied on the bootstrap samples obtained...

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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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