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Mastering Machine Learning with R, Second Edition - Second Edition

You're reading from Mastering Machine Learning with R, Second Edition - Second Edition

Product type Book

Published in Apr 2017

Publisher Packt

ISBN-13 9781787287471

Pages 420 pages

Edition 2nd Edition

Languages

Concepts

Machine Learning

Table of Contents (23) Chapters

Title Page

Credits

About the Author

About the Reviewers

Packt Upsell

Customer Feedback

Preface

1. A Process for Success

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

Ensembles

The quote at the beginning of this chapter mentions using ensembles to win machine learning competitions. However, they do have practical applications. I've provided a definition of what ensemble modeling is, but why does it work? To demonstrate this, I've co-opted an example, from the following blog, which goes into depth at a number of ensemble methods:http://mlwave.com/kaggle-ensembling-guide/.

As I write this chapter, we are only a couple of days away from Super Bowl 51, the Atlanta Falcons versus the New England Patriots. Let's say we want to review our probability of winning a friendly wager where we want to take the Patriots minus the points (3 points as of this writing). Assume that we have been following three expert prognosticators that all have the same probability of predicting that the Patriots will cover the spread (60%). Now, if we favor any one of the so-called experts, it is clear we have a 60% chance to win. However, let's see what creating an ensemble of their...

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