Multivariate Biomarker Discovery Data Science Methods for Efficient Analysis of High-Dimensional Biomedical Data
Book contents
- Multivariate Biomarker Discovery
- Multivariate Biomarker Discovery
- Copyright page
- Dedication
- Contents
- Preface
- Acknowledgments
- Part I Framework for Multivariate Biomarker Discovery
- Part II Regression Methods for Estimation
- 6 Basic Regression Methods
- 7 Regularized Regression Methods
- 8 Regression with Random Forests
- 9 Support Vector Regression
- Part III Classification Methods
- Part IV Biomarker Discovery via Multistage Signal Enhancement and Identification of Essential Patterns
- Part V Multivariate Biomarker Discovery Studies
- References
- Index
8 - Regression with Random Forests
from Part II - Regression Methods for Estimation
Published online by Cambridge University Press: 30 May 2024
Book contents
- Multivariate Biomarker Discovery
- Multivariate Biomarker Discovery
- Copyright page
- Dedication
- Contents
- Preface
- Acknowledgments
- Part I Framework for Multivariate Biomarker Discovery
- Part II Regression Methods for Estimation
- 6 Basic Regression Methods
- 7 Regularized Regression Methods
- 8 Regression with Random Forests
- 9 Support Vector Regression
- Part III Classification Methods
- Part IV Biomarker Discovery via Multistage Signal Enhancement and Identification of Essential Patterns
- Part V Multivariate Biomarker Discovery Studies
- References
- Index
Summary
Chapter 8 presents random forests for regression, which – at least in some situations – may outperform the least-squares-based regression methods. The chapter discusses bagging in the context of regression applications of random forests, the algorithm for splitting nodes in regression trees, and the variable importance metrics applicable to regression.
- Type
- Chapter
- Information
- Multivariate Biomarker DiscoveryData Science Methods for Efficient Analysis of High-Dimensional Biomedical Data, pp. 126 - 135Publisher: Cambridge University PressPrint publication year: 2024