Book Image

Machine Learning for Imbalanced Data

By : Kumar Abhishek, Dr. Mounir Abdelaziz
Book Image

Machine Learning for Imbalanced Data

By: Kumar Abhishek, Dr. Mounir Abdelaziz

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.

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Table of Contents (15 chapters)
Preface
Preface
Who this book is for
What this book covers
📌 Usage of techniques – In production tips
To get the most out of this book
Download the example code files
Conventions used
Get in touch
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Free Chapter
1
Chapter 1: Introduction to Data Imbalance in Machine Learning
Chapter 1: Introduction to Data Imbalance in Machine Learning
Technical requirements
Introduction to imbalanced datasets
Machine learning 101
Types of dataset and splits
Common evaluation metrics
Challenges and considerations when dealing with imbalanced data
When can we have an imbalance in datasets?
Why can imbalanced data be a challenge?
When to not worry about data imbalance
Introduction to the imbalanced-learn library
General rules to follow
Summary
Questions
References
2
Chapter 2: Oversampling Methods
Chapter 2: Oversampling Methods
Technical requirements
What is oversampling?
Random oversampling
SMOTE
SMOTE variants
ADASYN
Model performance comparison of various oversampling methods
Guidance for using various oversampling techniques
Oversampling in multi-class classification
Summary
Exercises
References
3
Chapter 3: Undersampling Methods
Chapter 3: Undersampling Methods
Technical requirements
Introducing undersampling
When to avoid undersampling the majority class
Removing examples uniformly
Strategies for removing noisy observations
Strategies for removing easy observations
Summary
Exercises
References
4
Chapter 4: Ensemble Methods
Chapter 4: Ensemble Methods
Technical requirements
Bagging techniques for imbalanced data
Boosting techniques for imbalanced data
Ensemble of ensembles
Model performance comparison
Summary
Questions
References
5
Chapter 5: Cost-Sensitive Learning
Chapter 5: Cost-Sensitive Learning
Technical requirements
The concept of Cost-Sensitive Learning
Understanding costs in practice
Cost-Sensitive Learning for logistic regression
Cost-Sensitive Learning for decision trees
Cost-Sensitive Learning using scikit-learn and XGBoost models
MetaCost – making any classification model cost-sensitive
Threshold adjustment
Summary
Questions
References
6
Chapter 6: Data Imbalance in Deep Learning
Chapter 6: Data Imbalance in Deep Learning
Technical requirements
A brief introduction to deep learning
Data imbalance in deep learning
Overview of deep learning techniques to handle data imbalance
Multi-label classification
Summary
Questions
References
7
Chapter 7: Data-Level Deep Learning Methods
Chapter 7: Data-Level Deep Learning Methods
Technical requirements
Preparing the data
Sampling techniques for deep learning models
Data-level techniques for text classification
Discussion of other data-level deep learning methods and their key ideas
Summary
Questions
References
8
Chapter 8: Algorithm-Level Deep Learning Techniques
Chapter 8: Algorithm-Level Deep Learning Techniques
Technical requirements
Motivation for algorithm-level techniques
Weighting techniques
Explicit loss function modification
Discussing other algorithm-based techniques
Summary
Questions
References
9
Chapter 9: Hybrid Deep Learning Methods
Chapter 9: Hybrid Deep Learning Methods
Technical requirements
Using graph machine learning for imbalanced data
Hard example mining
Minority class incremental rectification
Summary
Questions
References
10
Chapter 10: Model Calibration
Chapter 10: Model Calibration
Technical requirements
Introduction to model calibration
The influence of data balancing techniques on model calibration
Plotting calibration curves for a model trained on a real-world dataset
Model calibration techniques
The impact of calibration on a model’s performance
Summary
Questions
References
11
Assessments
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
12
Index
Index
Why subscribe?
13
Other Books You May Enjoy
Other Books You May Enjoy
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Appendix: Machine Learning Pipeline in Production
Appendix: Machine Learning Pipeline in Production
Machine learning training pipeline
Inferencing (online or batch)

Preface

Hello and welcome! Machine Learning (ML) enables computers to learn from data using algorithms to make informed decisions, automate tasks, and extract valuable insights. One particular aspect that often garners attention is imbalanced data, where certain classes may have considerably fewer samples than others.

This book provides an in-depth guide to understanding and navigating the intricacies of skewed data. You will gain insights into best practices for managing imbalanced datasets in ML contexts.

While imbalanced data can present challenges, it’s important to understand that the techniques to address this imbalance are not universally applicable. Their relevance and necessity depend on various factors such as the domain, the data distribution, the performance metrics you’re optimizing, and the business objectives. Before adopting any techniques, it’s essential to establish a baseline. Even if you don’t currently face issues with imbalanced data, it can be beneficial to be aware of the challenges and solutions discussed in this book. Familiarizing yourself with these techniques will provide you with a comprehensive toolkit, preparing you for scenarios that you may not yet know you’ll encounter. If you do find that model performance is lacking, especially for underrepresented (minority) classes, the insights and strategies covered in the book can be instrumental in guiding effective improvements.

As the domains of ML and artificial intelligence continue to grow, there will be an increasing demand for professionals who can adeptly handle various data challenges, including imbalance. This book aims to equip you with the knowledge and tools to be one of those sought-after experts.

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