Evaluating Learning Algorithms
A Classification Perspective
Published on 6. March 2014
Book
Paperback/Softback
424 pages
978-1-107-65311-5 (ISBN)
Description
The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA, facilitating better practical insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for conducting performance evaluations of algorithms in practical settings.
Reviews / Votes
"This treasure-trove of a book covers the important topic of performance evaluation of machine learning algorithms in a very comprehensive and lucid fashion. As Japkowicz and Shah point out, performance evaluation is too often a formulaic affair in machine learning, with scant appreciation of the appropriateness of the evaluation methods used or the interpretation of the results obtained. This book makes significant steps in rectifying this situation by providing a reasoned catalogue of evaluation measures and methods, written specifically for a machine learning audience and accompanied by concrete machine learning examples and implementations in R. This is truly a book to be savoured by machine learning professionals, and required reading for Ph.D students."Peter A. Flach, University of Bristol "This book has the merit of organizing most of the material about the evaluation of learning algorithms into a homogeneous description, covering both theoretical aspects and pragmatic issues. It is a useful resource for researchers in machine learning, and provides adequate material for graduate courses in machine learning and related fields."
Corrado Mencar, Computing Reviews
More details
Language
English
Place of publication
Cambridge
United Kingdom
Target group
Professional and scholarly
College/higher education
Product notice
Paperback (trade)
Illustrations
45 Tables, unspecified; 3 Halftones, unspecified; 37 Line drawings, unspecified
Dimensions
Height: 234 mm
Width: 156 mm
Thickness: 23 mm
Weight
641 gr
ISBN-13
978-1-107-65311-5 (9781107653115)
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
E-Book
05/2011
1st Edition
Cambridge University Press
€52.99
Available for download
Book
01/2011
Cambridge University Press
€174.80
Shipment within 15-20 days
Persons
Nathalie Japkowicz is Professor of Computer Science at American University. She is a former assistant professor at Dalhousie University and lecturer at Ohio State University. Japkowicz co-organized numerous workshops on classifier evaluation and the class imbalance problem at AAAI and ICML. She has published many articles in peer-reviewed journals and conference proceedings. Mohak Shah is an AI and technology executive with extensive experience in bringing data and AI products to market. He has held several senior leadership roles in large enterprises and startups driving both large-scale AI transformation initiatives and zero-to-one product journeys. He is the founder and Managing Director of Praescivi Advisors, a strategic AI advisory practice. As a research scientist, Mohak has published extensively in theoretical and applied machine learning areas.
Content
1. Introduction; 2. Machine learning and statistics overview; 3. Performance measures I; 4. Performance measures II; 5. Error estimation; 6. Statistical significance testing; 7. Data sets and experimental framework; 8. Recent developments; 9. Conclusion; Appendix A: statistical tables; Appendix B: additional information on the data; Appendix C: two case studies.