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Machine Learning Evaluation

Towards Reliable and Responsible AI
BuchGebunden
420 Seiten
Englisch
Cambridge University Presserscheint am30.09.20242nd Revised edition
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.mehr

Produkt

KlappentextAs 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.
Details
ISBN/GTIN978-1-316-51886-1
ProduktartBuch
EinbandartGebunden
FormatGenäht
Erscheinungsjahr2024
Erscheinungsdatum30.09.2024
Auflage2nd Revised edition
Seiten420 Seiten
SpracheEnglisch
Artikel-Nr.61446425

Inhalt/Kritik

Inhaltsverzeichnis
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.mehr

Autor

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.
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