Imbalanced Data

Class imbalance isn’t a problem. Contrary to popular belief, imbalanced data does not

inherently harm model performance, poor methodology does. SMOTE, once a go-to solution,

is frequently misapplied, introducing bias rather than solving it. Similarly, the default 0.5

probability threshold persists despite its misalignment with real-world costs and decision

requirements.

This book challenges outdated practices and provides rigorous, data-driven alternatives. We

focus not on balancing datasets for the sake of it, but on selecting the right tools, threshold

tuning, cost-sensitive learning, and strategic evaluation metrics, to build models that work in

practice, not just in theory.

Ready to learn from mistakes, move beyond myths and master modern solutions? Let’s

begin.