Multi-class Classification

This chapter provides a comprehensive overview of multi-class classification, beginning with the basics of binary classification and expanding into the nuances of multi-class classification, highlighting their pitfalls and diverse applications. We delve into the two main types of multi-class classification approaches—One-vs-One and One-vs-All—discussing their benefits and limitations through real-world case studies. The chapter also addresses the challenges of performing multi-class classification with imbalanced datasets, offering practical insights and hands-on coding examples to equip readers with the skills needed to implement effective multi-class classification solutions in real-world scenarios.