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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 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 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

Multivariate linear regression

You may be asking yourself the question if in the real world you would ever have just one predictor variable; that is, indeed, fair. 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. Lately, the severe drought in Southern California has caused much alarm. Even the Governor, Jerry Brown, has begun to take action with a call to citizens to reduce water usage by 20 percent. For this exercise, let's say we have been commissioned by the state of California to predict water availability. The data provided to us contains 43 years of snow precipitation, measured at six different sites in the Owens Valley...

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