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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

Modeling and evaluation


As mentioned, the package that we will use is neuralnet. The function in neuralnet will call for the use of a formula as we used elsewhere, such as y~x1+x2+x3+x4, data = df. In the past, we used y~, to specify all the other variables in the data as inputs. However, neuralnet does not accommodate this at the time of writing. The way around this limitation is to use the as.formula() function. After first creating an object of the variable names, we will use this as an input in order to paste the variables properly on the right side of the equation:

> n <- names(shuttleTrain)
> form <- as.formula(paste("use ~", paste(n[!n %in% "use"], 
      collapse = " + ")))
> form
use ~ stability.xstab + error.MM + error.SS + error.XL + sign.pp +       
      wind.tail 
       + magn.Medium + magn.Out + magn.Strong + vis.yes

Keep this function in mind for your own use as it may come in quite handy. In the neuralnet package, the function that we will use is appropriately...

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