Book contents
- Frontmatter
- Dedication
- Contents
- Abbreviations
- Preface
- Acknowledgments
- Part I Preliminary Considerations
- 1 Introduction
- 2 Statistics Overview
- 3 Machine Learning Preliminaries
- 4 Traditional Machine Learning Evaluation
- Part II Evaluation for Classification
- Part III Evaluation for Other Settings
- Part IV Evaluation from a Practical Perspective
- Appendices
- References
- Index
3 - Machine Learning Preliminaries
from Part I - Preliminary Considerations
Published online by Cambridge University Press: 07 November 2024
Book contents
- Frontmatter
- Dedication
- Contents
- Abbreviations
- Preface
- Acknowledgments
- Part I Preliminary Considerations
- 1 Introduction
- 2 Statistics Overview
- 3 Machine Learning Preliminaries
- 4 Traditional Machine Learning Evaluation
- Part II Evaluation for Classification
- Part III Evaluation for Other Settings
- Part IV Evaluation from a Practical Perspective
- Appendices
- References
- Index
Summary
Chapter 3 discusses the field of machine learning from a theoretical perspective. The review will advance the discussion of advanced metrics in Chapter 5 and error estimation methods in Chapter 6. The specific concepts surveyed in this chapter include loss functions, empirical risk, generalization error, empirical and structural risk minimization, regularization, and learning bias. The unsupervised learning paradigm is also reviewed and the chapter concludes with a discussion of the bias/variance tradeoff.
- Type
- Chapter
- Information
- Machine Learning EvaluationTowards Reliable and Responsible AI, pp. 33 - 50Publisher: Cambridge University PressPrint publication year: 2024