Imbalanced Data

Imbalanced data occurs when one target class is much less frequent than another, which can bias models toward the majority class and reduce recall on rare but important outcomes. One common mitigation is oversampling, where additional minority-class examples are generated to rebalance training data; see Classification Oversampling Concept for multiple synthetic oversampling approaches (CVAE, SMOTENC, ADASYN).