Machine Learning for Imbalanced Data: Tackle imbalanced datasets using machine learning and deep learning techniques

by Kumar Abhishek, Anshul Yadav

Raise your machine learning game and deal with imbalanced data using libraries, such as imbalanced-learn, PyTorch, scikit-learn, pandas, and NumPy, and squeeze better performance from machine learning models using this essential guide 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 a suboptimal performance on imbalanced data. Addressing class imbalance is crucial for significantly improving model performance. Machine Learning for Imbalanced Data begins by introducing the challenges posed by imbalanced datasets and the importance of addressing these issues. It then guides you through techniques that enhance performance on imbalanced data when using classical machine learning models, including various sampling and cost-sensitive learning methods. As you progress, the book delves into similar and more advanced techniques for deep learning models, employing PyTorch as the primary framework. Throughout the book, hands-on examples provide working, reproducible code that demonstrates the practical implementation of each technique. By the end of this book, you will be adept at identifying and addressing class imbalances, and confidently applying various techniques including sampling, cost-sensitive techniques, and threshold adjustment when using traditional machine learning or deep learning models. 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 ML concepts will help readers maximize the benefits and insights gained from this comprehensive resource.

344 pages, Paperback

Published November 30, 2023

Reviews

[Alice Holloway]

This book is a practical and insightful guide for anyone working with real-world datasets where class imbalance is a challenge. The book breaks down complex concepts into digestible techniques using both traditional machine learning and modern deep learning approaches. It's especially useful for data scientists looking to build more accurate, fair models in healthcare, fraud detection, or NLP tasks. For those interested in exploring curated datasets to practice the techniques discussed, Unidata https://unidata.pro/ offers a variety of resources that complement the book’s hands-on approach beautifully.

[Josua Naiborhu · Rating 5 out of 5]

The Author took different level of explaining imbalanced data in a unique way. Not only did the author walked through the content in a proper way, but also the hands-on instances along with narrative in comic made it easy to comprehend. Loved the book so much. I learned a lot through this insightful book.