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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 h2o package


The R software .exe file size is 75 MB (version 3.4.1). The size of the h2o R package is 125 MB. This will probably indicate to you the importance of the h2o package. All the datasets used in this book are very limited in size, with the number of observations not exceeding 10,000. In most cases, the file size has been of a maximum of a few MB. However, the data science world works hard, and throws around files in GB, and in even higher formats. Thus, we need more capabilities, and the h2o package provides just that. We simply load the h2o package and have a peek:

> library(h2o)

----------------------------------------------------------------------

Your next step is to start H2O:
    > h2o.init()

For H2O package documentation, ask for help:
    > ??h2o

After starting H2O, you can use the Web UI at http://localhost:54321
For more information visit http://docs.h2o.ai

----------------------------------------------------------------------


Attaching package: 'h2o...
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