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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 2. Linear Regression - The Blocking and Tackling of Machine Learning FREE CHAPTER 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

Data understanding, preparation, and recommendations

The one library that we will need for this exercise is recommenderlab. The package was developed by the Southern Methodist University's Lyle Engineering Lab, and they have an excellent website with supporting documentation at https://lyle.smu.edu/IDA/recommenderlab/:

    > library(recommenderlab)

> data(Jester5k)

> Jester5k
5000 x 100 rating matrix of class 'realRatingMatrix' with
362106 ratings.

The rating matrix contains 362106 total ratings. It is quite easy to get a list of a user's ratings. Let's look at user number 10. The following output is abbreviated for the first five jokes:

    > as(Jester5k[10,], "list")
$u12843
j1 j2 j3 j4 j5 ...
-1.99 -6.89 2.09 -4.42 -4.90 ...

You can also look at the mean rating for a user (user 10) and/or the mean rating for a specific...

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