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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

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Product type Paperback
Published in Jul 2018
Publisher Packt
ISBN-13 9781788624145
Length 376 pages
Edition 1st Edition
Languages
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Author (1):
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Prabhanjan Narayanachar Tattar Prabhanjan Narayanachar Tattar
Author Profile Icon Prabhanjan Narayanachar Tattar
Prabhanjan Narayanachar Tattar
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Table of Contents (15) Chapters Close

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 Index

Bootstrap – a statistical method

In this section, we will explore complex statistical functional. What is the statistical distribution of the correlation between two random variables? If normality assumption does not hold for the multivariate data, then what is an alternative way to obtain the standard error and confidence interval? Efron (1979) invented the bootstrap technique, which provides the solutions that enable statistical inference related to complex statistical functionals. In Chapter 1, Introduction to Ensemble Techniques, the permutation test, which repeatedly draws samples of the given sample and carries out the test for each of the resamples, was introduced. In theory, the permutation test requires Bootstrap – a statistical method number of resamples, where m and n are the number of observations in the two samples, though one does take their foot off the pedal after having enough resamples. The bootstrap method works in a similar way and is an important resampling method.

Let Bootstrap – a statistical method be an independent random...

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