Machine Learning Evaluation: Towards Reliable and Responsible AI

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Nathalie Japkowicz (American University, Washington DC) Zois Boukouvalas (American University, Washington DC)

Machine Learning Evaluation

Towards Reliable and Responsible AI

Nathalie Japkowicz (American University, Washington DC), Zois Boukouvalas (American University, Washington DC), Mohak Shah (McGill University, Montréal)

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Hardback

Forthcoming
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English

Cambridge University Press
31 October 2024

Computing: general; Computer networking & communications; Artificial intelligence; Machine learning; Pattern recognition

As machine learning applications gain widespread adoption and integration in a variety of applications, including safety and mission-critical systems, the need for robust evaluation methods grows more urgent. This book compiles scattered information on the topic from research papers and blogs to provide a centralized resource that is accessible to students, practitioners, and researchers across the sciences. The book examines meaningful metrics for diverse types of learning paradigms and applications, unbiased estimation methods, rigorous statistical analysis, fair training sets, and meaningful explainability, all of which are essential to building robust and reliable machine learning products. In addition to standard classification, the book discusses unsupervised learning, regression, image segmentation, and anomaly detection. The book also covers topics such as industry-strength evaluation, fairness, and responsible AI. Implementations using Python and scikit-learn are available on the book's website.

By: Nathalie Japkowicz (American University Washington DC), Zois Boukouvalas (American University, Washington DC), Mohak Shah (McGill University, Montréal)
Imprint: Cambridge University Press
Country of Publication: United Kingdom
ISBN: 9781316518861
ISBN 10: 1316518868
Pages: 420
Publication Date: 31 October 2024
Audience: College/higher education , Professional and scholarly , Primary , Undergraduate
Format: Hardback
Publisher's Status: Forthcoming

Part I. Preliminary Considerations: 1. Introduction; 2. Statistics overview; 3. Machine learning preliminaries; 4. Traditional machine learning evaluation; Part II. Evaluation for Classification: 5. Metrics; 6. Re-sampling; 7. Statistical analysis; Part III. Evaluation for Other Settings: 8. Supervised settings other than simple classification; 9. Unsupervised learning; Part IV. Evaluation from a Practical Perspective: 10. Industrial-strength evaluation; 11. Responsible machine learning; 12. Conclusion; Appendices: A. Statistical tables; B. Advanced topics in classification metrics; References; Index.

Nathalie Japkowicz is Professor and Chair of the Department of Computer Science at American University, Washington DC. She previously taught at the University of Ottawa. Her current research focuses on lifelong anomaly detection and hate speech detection. In the past, she researched one-class learning and the class imbalance problem extensively. She has received numerous awards, including Test of Time and Distinguished Service awards. Zois Boukouvalas is Assistant Professor in the Department of Mathematics and Statistics at American University, Washington DC. His research focuses on the development of interpretable multi-modal machine learning algorithms, and he has been the lead principal investigator of several research grants. Through his research and teaching activities, he is creating environments that encourage and support the success of underrepresented students for entry into machine learning careers.

Reviews for Machine Learning Evaluation: Towards Reliable and Responsible AI

'By its nature, machine learning has always had evaluation at its heart. As the authors of this timely and important book note, the importance of doing evaluation properly is only increasing as we enter the age of machine learning deployment. The book showcases Japkowicz' and Boukouvalas' encyclopaedic knowledge of the subject as well as their accessible and lucid writing style. Quite simply required reading for machine learning researchers and professionals.' Peter Flach, University of Bristol