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Evaluating Learning Algorithms
A Classification Perspective

  • Date Published: June 2014
  • availability: Available
  • format: Paperback
  • isbn: 9781107653115

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About the Authors
  • 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.

    • Each component of machine learning evaluation is discussed separately and in great detail, before being integrated within the overall process
    • For each component of machine learning evaluation, a plethora of relevant techniques are presented, that span all the techniques discussed in the machine learning literature and beyond
    • All the techniques presented are illustrated using R and WEKA tools, and so the user will be able to easily adapt these illustrations to his/her own needs
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    Reviews & endorsements

    "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

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    Product details

    • Date Published: June 2014
    • format: Paperback
    • isbn: 9781107653115
    • length: 424 pages
    • dimensions: 234 x 156 x 22 mm
    • weight: 0.59kg
    • contains: 40 b/w illus. 45 tables
    • availability: Available
  • Table of Contents

    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.

  • Authors

    Nathalie Japkowicz, University of Ottawa
    Nathalie Japkowicz is an Associate Professor at the School of Information Technology and Engineering of the University of Ottawa. 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, McGill University, Montréal
    Mohak Shah is a Postdoctoral Fellow at the Centre for Intelligent Machines at McGill University. He is a former CIHR Postdoctoral Fellow at the CHUL Genomics research centre and Laval University in Quebec. He has been named the Arnold Smith Commonwealth Scholar in 2002 and a National Scholar in India in 1995. Shah has served on program committees of various conferences and symposiums in addition to reviewing for major journals and conferences in the field.

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