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

Machine Learning Evaluation

• Gebundenes Buch

Produktbeschreibung

This accessible, comprehensive guide is aimed at students, practitioners, engineers, and users. The emphasis is on building robust, responsible machine learning products incorporating meaningful metrics, rigorous statistical analysis, fair training sets, and explainability. Implementations in Python and sklearn are available on the book's website.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 426
• Erscheinungstermin: 14. Oktober 2024
• Englisch
• Abmessung: 250mm x 175mm x 27mm
• Gewicht: 904g
• ISBN-13: 9781316518861
• ISBN-10: 1316518868
• Artikelnr.: 70162698

Autorenporträt

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.

Inhaltsangabe

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.