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Hands-On Ensemble Learning with R

You're reading from  Hands-On Ensemble Learning with R

Product type Book
Published in Jul 2018
Publisher Packt
ISBN-13 9781788624145
Pages 376 pages
Edition 1st Edition
Languages
Author (1):
Prabhanjan Narayanachar Tattar Prabhanjan Narayanachar Tattar
Profile icon Prabhanjan Narayanachar Tattar
Toc

Table of Contents (17) Chapters close

Hands-On Ensemble Learning with R
Contributors
Preface
1. Introduction to Ensemble Techniques 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

The jackknife technique


Quenouille (1949) invented the jackknife technique. The purpose of this was to reduce bias by looking at multiple samples of data in a methodical way. The name jackknife seems to have been coined by the well-known statistician John W. Tukey. Due mainly to the lack of computational power, the advances and utility of the jackknife method were restricted. Efron invented the bootstrap method in 1979 (see the following section for its applications) and established the connection with the jackknife method. In fact, these two methods have a lot in common and are generally put under the umbrella of resampling methods.

Suppose that we draw a random sample of size n from a probability distribution F, and we denote by the parameter of interest. Let be an estimator of , and here we don't have the probability distribution of for a given . Resampling methods will help in carrying out statistical inference when the probability distribution is unknown. A formal definition of the...

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