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Mastering Predictive Analytics with R, Second Edition

You're reading from   Mastering Predictive Analytics with R, Second Edition Machine learning techniques for advanced models

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781787121393
Length 448 pages
Edition 2nd Edition
Languages
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Authors (2):
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James D. Miller James D. Miller
Author Profile Icon James D. Miller
James D. Miller
Rui Miguel Forte Rui Miguel Forte
Author Profile Icon Rui Miguel Forte
Rui Miguel Forte
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Table of Contents (16) Chapters Close

Preface 1. Gearing Up for Predictive Modeling FREE CHAPTER 2. Tidying Data and Measuring Performance 3. Linear Regression 4. Generalized Linear Models 5. Neural Networks 6. Support Vector Machines 7. Tree-Based Methods 8. Dimensionality Reduction 9. Ensemble Methods 10. Probabilistic Graphical Models 11. Topic Modeling 12. Recommendation Systems 13. Scaling Up 14. Deep Learning Index

Exploring the data

Before building and evaluating recommender systems using the two datasets we have loaded, it is a good idea to get a feel for the data. For one thing, we can make use of the getRatings() function to retrieve the ratings from a rating matrix. This is useful in order to construct a histogram of item ratings. Additionally, we can also normalize the ratings with respect to each user, as we discussed earlier. The following code snippet shows how we can compute ratings and normalized ratings for the jester data. We can then do the same for the MovieLens data and produce histograms for the ratings:

>jester_ratings<- getRatings(jester_rrm)
>jester_normalized_ratings<- getRatings(normalize(jester_rrm, 
                                          method = "Z-score"))

The following plot shows the different histograms:

Exploring the data

In the Jester data, we can see that ratings above zero are more prominent than ratings below zero, and the most common rating is 10, the maximum rating...

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