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Mastering Machine Learning with R, Second Edition

You're reading from   Mastering Machine Learning with R, Second Edition Advanced prediction, algorithms, and learning methods with R 3.x

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781787287471
Length 420 pages
Edition 2nd Edition
Languages
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Author (1):
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Cory Lesmeister Cory Lesmeister
Author Profile Icon Cory Lesmeister
Cory Lesmeister
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Table of Contents (17) Chapters Close

Preface 1. A Process for Success FREE CHAPTER 2. Linear Regression - The Blocking and Tackling of Machine Learning 3. Logistic Regression and Discriminant Analysis 4. Advanced Feature Selection in Linear Models 5. More Classification Techniques - K-Nearest Neighbors and Support Vector Machines 6. Classification and Regression Trees 7. Neural Networks and Deep Learning 8. Cluster Analysis 9. Principal Components Analysis 10. Market Basket Analysis, Recommendation Engines, and Sequential Analysis 11. Creating Ensembles and Multiclass Classification 12. Time Series and Causality 13. Text Mining 14. R on the Cloud 15. R Fundamentals 16. Sources

Summary


In this chapter, we took a second stab at unsupervised learning techniques by exploring PCA, examining what it is, and applying it in a practical fashion. We explored how it can be used to reduce the dimensionality and improve the understanding of the dataset when confronted with numerous highly correlated variables. Then, we applied it to real data from the National Hockey League, using the resulting principal components in a regression analysis to predict total team points. Additionally, we explored ways to visualize the data and the principal components.

As an unsupervised learning technique, it requires some judgment along with trial and error to arrive at an optimal solution that is acceptable to business partners. Nevertheless, it is a powerful tool to extract latent insights and to support supervised learning.

We will next look at using unsupervised learning to develop market basket analyses and recommendation engines in which PCA can play an important role.

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