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
- Part III Classification Methods
- 10 Classification with Random Forests
- 11 Classification with Support Vector Machines
- 12 Discriminant Analysis
- 13 Neural Networks and Deep Learning
- Part IV Biomarker Discovery via Multistage Signal Enhancement and Identification of Essential Patterns
- Part V Multivariate Biomarker Discovery Studies
- References
- Index
10 - Classification with Random Forests
from Part III - Classification Methods
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
- Part III Classification Methods
- 10 Classification with Random Forests
- 11 Classification with Support Vector Machines
- 12 Discriminant Analysis
- 13 Neural Networks and Deep Learning
- Part IV Biomarker Discovery via Multistage Signal Enhancement and Identification of Essential Patterns
- Part V Multivariate Biomarker Discovery Studies
- References
- Index
Summary
Chapter 10 covers the random forests algorithm for classification. Presented are also the impurity metrics applicable to splitting nodes in classification trees (Gini, entropy, and misclassification impurity), as well as permutation-based and impurity-based variable importance measures.
- Type
- Chapter
- Information
- Multivariate Biomarker DiscoveryData Science Methods for Efficient Analysis of High-Dimensional Biomedical Data, pp. 149 - 157Publisher: Cambridge University PressPrint publication year: 2024