Explain 'Imbalanced Data' and its impact.

Explain 'Imbalanced Data' and its impact.

Approach: Describe a dataset where classes are not represented equally.

A situation in classification where one class (the minority) is much rarer than others. Accuracy becomes a misleading metric because a model can reach high accuracy by always predicting the majority class.

Follow-up: How do you address this (e.g., oversampling)?

Key concepts: smote, precision-recall, class weighting.