Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Basic Probability Inequalities for Sums of Independent Random Variables
- 3 Uniform Convergence and Generalization Analysis
- 4 Empirical Covering Number Analysis and Symmetrization
- 5 Covering Number Estimates
- 6 Rademacher Complexity and Concentration Inequalities
- 7 Algorithmic Stability Analysis
- 8 Model Selection
- 9 Analysis of Kernel Methods
- 10 Additive and Sparse Models
- 11 Analysis of Neural Networks
- 12 Lower Bounds and Minimax Analysis
- 13 Probability Inequalities for Sequential Random Variables
- 14 Basic Concepts of Online Learning
- 15 Online Aggregation and Second-Order Algorithms
- 16 Multiarmed Bandits
- 17 Contextual Bandits
- 18 Reinforcement Learning
- Appendix A Basics of Convex Analysis
- Appendix B f-divergence of Probability Measures
- References
- Author Index
- Subject Index
8 - Model Selection
Published online by Cambridge University Press: 20 July 2023
Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Basic Probability Inequalities for Sums of Independent Random Variables
- 3 Uniform Convergence and Generalization Analysis
- 4 Empirical Covering Number Analysis and Symmetrization
- 5 Covering Number Estimates
- 6 Rademacher Complexity and Concentration Inequalities
- 7 Algorithmic Stability Analysis
- 8 Model Selection
- 9 Analysis of Kernel Methods
- 10 Additive and Sparse Models
- 11 Analysis of Neural Networks
- 12 Lower Bounds and Minimax Analysis
- 13 Probability Inequalities for Sequential Random Variables
- 14 Basic Concepts of Online Learning
- 15 Online Aggregation and Second-Order Algorithms
- 16 Multiarmed Bandits
- 17 Contextual Bandits
- 18 Reinforcement Learning
- Appendix A Basics of Convex Analysis
- Appendix B f-divergence of Probability Measures
- References
- Author Index
- Subject Index
Summary
In practical applications, we often try many different model classes (such as SVM, neural networks, decision trees), and we want to select the best model to achieve the smallest test loss. This problem is referred to as model selection. This chapter studies techniques used to analyze model selection problems.
- Type
- Chapter
- Information
- Mathematical Analysis of Machine Learning Algorithms , pp. 136 - 150Publisher: Cambridge University PressPrint publication year: 2023