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Machine Learning with R Cookbook, Second Edition

You're reading from   Machine Learning with R Cookbook, Second Edition Analyze data and build predictive models

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781787284395
Length 572 pages
Edition 2nd Edition
Languages
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Authors (2):
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Ashish Bhatia Ashish Bhatia
Author Profile Icon Ashish Bhatia
Ashish Bhatia
Yu-Wei, Chiu (David Chiu) Yu-Wei, Chiu (David Chiu)
Author Profile Icon Yu-Wei, Chiu (David Chiu)
Yu-Wei, Chiu (David Chiu)
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Table of Contents (15) Chapters Close

Preface 1. Practical Machine Learning with R FREE CHAPTER 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)

Dimension Reduction

In this chapter, we will cover the following topics:

  • Why reduce dimension?
  • Performing feature selection with FSelector
  • Performing dimension reduction with PCA
  • Determining the number of principal components using a scree test
  • Determining the number of principal components using the Kaiser method
  • Visualizing multivariate data using biplot
  • Performing dimension reduction with MDS
  • Reducing dimensions with SVD
  • Compressing images with SVD
  • Performing nonlinear dimension reduction with ISOMAP
  • Performing nonlinear dimension deduction with Local Linear Embedding
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