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Mastering Machine Learning with R

You're reading from   Mastering Machine Learning with R Master machine learning techniques with R to deliver insights for complex projects

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Product type Paperback
Published in Oct 2015
Publisher
ISBN-13 9781783984527
Length 400 pages
Edition 1st Edition
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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 (15) 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 8. Cluster Analysis 9. Principal Components Analysis 10. Market Basket Analysis and Recommendation Engines 11. Time Series and Causality 12. Text Mining A. R Fundamentals Index

Business understanding and recommendations


This business case is a joke, literally. Maybe it is more appropriate to say a bunch of jokes as we will use the Jester5k data from the recommenderlab package. This data consists of 5,000 ratings on 100 jokes sampled from the Jester Online Joke Recommender System. It was collected between April 1999 and May 2003, and all the users have rated at least 36 jokes (Goldberg, Roeder, Gupta, and Perkins, 2001). Our goal is to compare the recommendation algorithms and select the best one.

As such, I believe it is important to lead off with a statistical joke to put one in the proper frame of mind. I'm not sure of how to properly provide attribution for this one, but it is popular all over the Internet.

A statistician's wife had twins. He was delighted. He rang the minister who was also delighted. "Bring them to church on Sunday and we'll baptize them", said the minister. "No", replied the statistician. "Baptize one. We'll keep the other as a control."

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