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.
publication date
2023-11-30
what you will learn
Use imbalanced data in your machine learning models effectively
Explore the metrics used when classes are imbalanced
Understand how and when to apply various sampling methods such as over-sampling and under-sampling
Apply data-based, algorithm-based, and hybrid approaches to deal with class imbalance
Combine and choose from various options for data balancing while avoiding common pitfalls
Understand the concepts of model calibration and threshold adjustment in the context of dealing with imbalanced datasets
key features
Understand how to use modern machine learning frameworks with detailed explanations, illustrations, and code samples * Learn cutting-edge deep learning techniques to overcome data imbalance * Explore different methods for dealing with skewed data in ML and DL applications * Purchase of the print or Kindle book includes a free eBook in the PDF format
approach
The book is written for machine learning practitioners. We dive deep into code examples where many methods have been implemented from scratch. The illustrative diagrams and plots tell the story behind various concepts and methods. With a complementary GitHub repository and end-of-chapter exercises, readers can effectively reinforce their understanding of the material covered in each chapter.
audience
This book is for machine learning practitioners who want to effectively address the challenges of imbalanced datasets in their projects. Data scientists, machine learning engineers/scientists, research scientists/engineers, and data scientists/engineers will find this book helpful. Though complete beginners are welcome to read this book, some familiarity with core machine learning concepts will help readers maximize the benefits and insights gained from this comprehensive resource.
short description
This book will help you master the art of predicting outcomes when datasets are imbalanced and where the most important class is under-represented. You’ll learn the skills you need to overcome challenges when dealing with imbalanced datasets for both traditional machine learning and deep learning models.
subtitle
Tackle imbalanced datasets using machine learning and deep learning techniques
keywords
Machine learning book, Data analysis, Data analytics, Machine learning python, Machine learning algorithms, Data analysis books
Product ISBN
9781801070836
