Resampling techniques are key to improving model performance and reliability in machine learning. This volume explores advanced resampling methods, including cross-validation, bootstrapping, and hyperparameter tuning, using R. Readers will learn how to apply these techniques to optimize model accuracy and prevent overfitting. Practical examples and case studies illustrate their real-world applications. This voulme is an essential resource for data scientists and machine learning enthusiasts aiming to master resampling strategies.
eBook - PDF
Statistics with R for Machine Learning: Volume 2 Advanced Resampling Techniques with R for Machine Learning
- 188 pages
- English
- PDF
- Available on iOS & Android
eBook - PDF
Statistics with R for Machine Learning: Volume 2 Advanced Resampling Techniques with R for Machine Learning
About this book
Table of contents
- Cover
- Title Page
- Copyright
- ABOUT THE AUTHOR
- TABLE OF CONTENTS
- List of Tables
- Preface
- Chapter 1 Stratified Resampling
- Chapter 2 Time-Based Resampling
- Bibliography
- Index
- Back Cover
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