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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

Multivariate linear regression

You may be asking yourself whether you will ever have just one predictor variable in the real world. That is indeed a fair question and certainly a very rare case (time series can be a common exception). Most likely, several, if not many, predictor variables or features--as they are affectionately termed in machine learning--will have to be included in your model. And with that, let's move on to multivariate linear regression and a new business case.

Business understanding

In keeping with the water conservation/prediction theme, let's look at another dataset in the alr3 package, appropriately named water. During the writing of the first edition of this book, the severe drought in Southern California caused much alarm...

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