Machine Learning Evaluation
Machine Learning Evaluation
Zois Boukouvalas, American University, Washington DC
£59.99
GBPAs 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.
- Illustrates all methods using Python and sklearn, allowing readers to reuse code in their own studies
- Covers evaluation methods for a wide range of learning paradigms, providing readers with specific discussions of their approach of interest
- Goes beyond experimental science, covering topics related to industry and ethics
Reviews & endorsements
'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
Product details
November 2024Hardback
9781316518861
426 pages
250 × 175 × 28 mm
0.86kg
Available
Table of Contents
- 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.
