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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

Data understanding and preparation

To start, we will load these three packages. The data is in the MASS package, but this is a required dependent package loaded with neuralnet:

> library(caret)

> library(neuralnet)
Loading required package: grid
Loading required package: MASS

> library(vcd)

The neuralnet package will be used for the building of the model and caret for the data preparation. The vcd package will assist us in data visualization. Let's load the data and examine its structure:

> data(shuttle)

> str(shuttle)
'data.frame':256 obs. of  7 variables:
 $ stability: Factor w/ 2 levepicels "stab","xstab": 2 2 2 2 2 2 2 2 2 2 ...
 $ error    : Factor w/ 4 levels "LX","MM","SS",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ sign     : Factor w/ 2 levels "nn","pp": 2 2 2 2 2 2 1 1 1 1 ...
 $ wind     : Factor w/ 2 levels "head","tail": 1 1 1 2 2 2 1 1 1 2 ...
 $ magn     : Factor w...
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