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Machine Learning with R Cookbook, Second Edition - Second Edition

You're reading from  Machine Learning with R Cookbook, Second Edition - Second Edition

Product type Book
Published in Oct 2017
Publisher Packt
ISBN-13 9781787284395
Pages 572 pages
Edition 2nd Edition
Languages
Author (1):
Yu-Wei, Chiu (David Chiu) Yu-Wei, Chiu (David Chiu)
Profile icon Yu-Wei, Chiu (David Chiu)
Toc

Table of Contents (21) Chapters close

Title Page
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Practical Machine Learning with R 2. Data Exploration with Air Quality Datasets 3. Analyzing Time Series Data 4. R and Statistics 5. Understanding Regression Analysis 6. Survival Analysis 7. Classification 1 - Tree, Lazy, and Probabilistic 8. Classification 2 - Neural Network and SVM 9. Model Evaluation 10. Ensemble Learning 11. Clustering 12. Association Analysis and Sequence Mining 13. Dimension Reduction 14. Big Data Analysis (R and Hadoop)

Conducting machine learning with RHadoop


In the previous chapters, we have demonstrated how powerful R is when used to solve machine learning problems. Also, we have shown that the use of Hadoop allows R to process big data in parallel. At this point, some may believe that the use of RHadoop can easily solve machine learning problems of big data via numerous existing machine learning packages. However, you cannot use most of these to solve machine learning problems as they cannot be executed in the MapReduce mode. In the following recipe, we will demonstrate how to implement a MapReduce version of linear regression and compare this version with the one using the lm function.

Getting ready

In this recipe, you should have completed the previous recipe by installing rmr2 into the R environment.

How to do it...

Perform the following steps to implement a linear regression in MapReduce:

  1. First, load the cats dataset from the MASS package:
> library(MASS)> data(cats)> X = matrix(cats$Bwt)&gt...
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