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.