Machine Learning Evaluation

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.

'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

• 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.