
Just Enough R!
An Interactive Approach to Machine Learning and Analytics
Richard J. Roiger(Author)
Chapman & Hall/CRC (Publisher)
1st Edition
Published on 8. June 2020
Book
Hardback
346 pages
978-0-367-44320-7 (ISBN)
Description
Just Enough R! An Interactive Approach to Machine Learning and Analytics presents just enough of the R language, machine learning algorithms, statistical methodology, and analytics for the reader to learn how to find interesting structure in data. The approach might be called "seeing then doing" as it first gives step-by-step explanations using simple, understandable examples of how the various machine learning algorithms work independent of any programming language. This is followed by detailed scripts written in R that apply the algorithms to solve nontrivial problems with real data. The script code is provided, allowing the reader to execute the scripts as they study the explanations given in the text.
Features
Gets you quickly using R as a problem-solving tool
Uses RStudio's integrated development environment
Shows how to interface R with SQLite
Includes examples using R's Rattle graphical user interface
Requires no prior knowledge of R, machine learning, or computer programming
Offers over 50 scripts written in R, including several problem-solving templates that, with slight modification, can be used again and again
Covers the most popular machine learning techniques, including ensemble-based methods and logistic regression
Includes end-of-chapter exercises, many of which can be solved by modifying existing scripts
Includes datasets from several areas, including business, health and medicine, and science
About the Author
Richard J. Roiger is a professor emeritus at Minnesota State University, Mankato, where he taught and performed research in the Computer and Information Science Department for over 30 years.
Features
Gets you quickly using R as a problem-solving tool
Uses RStudio's integrated development environment
Shows how to interface R with SQLite
Includes examples using R's Rattle graphical user interface
Requires no prior knowledge of R, machine learning, or computer programming
Offers over 50 scripts written in R, including several problem-solving templates that, with slight modification, can be used again and again
Covers the most popular machine learning techniques, including ensemble-based methods and logistic regression
Includes end-of-chapter exercises, many of which can be solved by modifying existing scripts
Includes datasets from several areas, including business, health and medicine, and science
About the Author
Richard J. Roiger is a professor emeritus at Minnesota State University, Mankato, where he taught and performed research in the Computer and Information Science Department for over 30 years.
More details
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Illustrations
72 s/w Abbildungen, 33 s/w Tabellen
33 Tables, black and white; 72 Illustrations, black and white
Dimensions
Height: 260 mm
Width: 183 mm
Thickness: 24 mm
Weight
883 gr
ISBN-13
978-0-367-44320-7 (9780367443207)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Book
06/2020
1st Edition
Chapman & Hall/CRC
€65.60
Shipment within 15-20 days

E-Book
05/2020
1st Edition
Chapman & Hall/CRC
€60.49
Available for download

E-Book
05/2020
1st Edition
Chapman & Hall/CRC
€60.49
Available for download
Person
Richard J. Roiger is a professor emeritus at Minnesota State University, Mankato where he taught and performed research in the Computer & Information Science Department for 27 years. Dr. Roiger's Ph.D. degree is in Computer & Information Sciences from the University of Minnesota. Dr. Roiger continues to serve as a part-time faculty member teaching courses in data mining, artificial intelligence and research methods. Richard enjoys interacting with his grandchildren, traveling, writing and pursuing his musical talents.
Content
Preface. Acknowledgment. Author. Introduction to Machine Learning. Introduction to R. Data Structures and Manipulation. Preparing the Data. Supervised Statistical Techniques. Tree-Based Methods. Rule-Based Techniques. Neural Networks. Formal Evaluation Techniques. Support Vector Machines. Unsupervised Clustering Techniques. A Case Study in Predicting Treatment Outcome. Bibliography. Appendix A: Supplementary Materials and More Datasets. Appendix B: Statistics for Performance Evaluation. Subject Index. Index of R Functions. Script Index.