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Book Overview & Buying
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Table Of Contents
Machine Learning for Imbalanced Data
By :
Machine Learning for Imbalanced Data
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Overview of this book
As machine learning practitioners, we often encounter imbalanced datasets in which one class has considerably fewer instances than the other. Many machine learning algorithms assume an equilibrium between majority and minority classes, leading to suboptimal performance on imbalanced data. This comprehensive guide helps you address this class imbalance to significantly improve model performance.
Machine Learning for Imbalanced Data begins by introducing you to the challenges posed by imbalanced datasets and the importance of addressing these issues. It then guides you through techniques that enhance the performance of classical machine learning models when using imbalanced data, including various sampling and cost-sensitive learning methods.
As you progress, you’ll delve into similar and more advanced techniques for deep learning models, employing PyTorch as the primary framework. Throughout the book, hands-on examples will provide working and reproducible code that’ll demonstrate the practical implementation of each technique.
By the end of this book, you’ll be adept at identifying and addressing class imbalances and confidently applying various techniques, including sampling, cost-sensitive techniques, and threshold adjustment, while using traditional machine learning or deep learning models.
Table of Contents (15 chapters)
Preface
Free Chapter
Chapter 1: Introduction to Data Imbalance in Machine Learning
Chapter 2: Oversampling Methods
Chapter 3: Undersampling Methods
Chapter 4: Ensemble Methods
Chapter 5: Cost-Sensitive Learning
Chapter 6: Data Imbalance in Deep Learning
Chapter 7: Data-Level Deep Learning Methods
Chapter 8: Algorithm-Level Deep Learning Techniques
Chapter 9: Hybrid Deep Learning Methods
Chapter 10: Model Calibration
Assessments
Index
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Appendix: Machine Learning Pipeline in Production
