Book Image

Mastering Machine Learning with R, Second Edition - Second Edition

Book Image

Mastering Machine Learning with R, Second Edition - Second Edition

Overview of this book

This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more. You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you’ll understand how these concepts work and what they do. With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets.
Table of Contents (23 chapters)
Title Page
Credits
About the Author
About the Reviewers
Packt Upsell
Customer Feedback
Preface
16
Sources

Data understanding and preparation


To start, we will load these four packages. The data is in the MASS package:

    > library(caret)
    > library(MASS)
    > library(neuralnet)
    > 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/ 4 levels "Light","Medium",..: 1 2 4 1 2 4 1        
       2 4 1 ...
 $ vis      : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1        
       ...
 ...