Handling Imbalanced Data

Abstract:

In real-world machine learning tasks, class imbalance is a common challenge—where one class or data category (often the class of interest) is significantly rarer than others. For example, in medical diagnosis tasks such as cancer detection, positive cases may represent a small fraction of the dataset. Models trained on such imbalanced data tend to be biased toward the majority class, resulting in poor performance on the minority class, which may be critical in many applications.

Addressing this imbalance is essential, especially when correct classification of the minority class carries high importance. The choice of performance metrics should reflect this priority—metrics like precision, recall, F1-score, or area under the precision-recall curve are more informative than accuracy in these settings.