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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

Product type Paperback

Published in Apr 2017

Publisher Packt

ISBN-13 9781787287471

Length 420 pages

Edition 2nd Edition

Languages

R

Concepts

Machine Learning

Author (1):

Cory Lesmeister

View More author details

Table of Contents (17) Chapters

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

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        
       ...
 ...

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Authors (1)

Lesmeister

Lesmeister

Cory Lesmeister has over fourteen years of quantitative experience and is currently a senior data scientist for the advanced analytics team at Cummins, Inc. in Columbus, Indiana. He has spent 16 years at Eli Lilly and Company in sales, market research, Lean Six Sigma, marketing analytics, and new product forecasting. He also has several years of experience in the insurance and banking industries, both as a consultant and as a manager of marketing analytics. A former US Army active duty and reserve officer, Cory was stationed in Baghdad, Iraq, in 2009. Here, he served as the strategic advisor to the 29,000-person Iraqi Oil Police, succeeding where others failed by acquiring and delivering promised equipment to help the country secure and protect its oil infrastructure. He has a BBA in aviation administration from the University of North Dakota and a commercial helicopter license.

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