Introduction to Classifier Performance Analysis With R

This book provides an introductory account of commonly used CPA techniques for binary and multiclass problems, and use of the R software system to accomplish the analysis.

List of contents

Chapter 01 Introduction to Classification
Chapter 02 Classifier Performance Measures
Chapter 03 Classifier Performance Curves
Chapter 04 Comparative Analysis of Classifiers
Chapter 05 Multiclass CPA
Chapter 06 Additional Topics in CPA
Appendix
Bibliography
Index

About the author

Sutaip L. C. Saw holds a PhD from the The Wharton School, University of Pennsylvania. Prior to earning his PhD, he served as a statistician in the public sector. His subsequent career was spent as an academic with research interests and publications in engineering statistics and statistical computing, and he has significant teaching experience in statistical/mathematical subjects at undergraduate and postgraduate levels. Since leaving academia, he has been focused on applications of R to data mining and machine learning problems. Although his interest in classification problems and performance analysis of classifiers started while he was still an academic, it has intensified in recent years and this book is the result of time spent on the topic at hand.

Summary

This book provides an introductory account of commonly used CPA techniques for binary and multiclass problems, and use of the R software system to accomplish the analysis.

Product details

• Authors: Sutaip L.C. Saw
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Paperback / Softback
• Released: 03.12.2024
• EAN: 9781032850108
• ISBN: 978-1-0-3285010-8
• No. of pages: 202
• Series: Chapman & Hall/CRC Data Science Series
• Subjects: Natural sciences, medicine, IT, technology > Biology > Ecology; machine learning, TECHNOLOGY & ENGINEERING / Automation, TECHNOLOGY & ENGINEERING / Electrical, Electrical Engineering, Automatic control engineering, Databases, COMPUTERS / Data Science / Machine Learning, COMPUTERS / Database Administration & Management, Databases / Data management