- Fraud detection
- Outlier detection
- Medical diagnostic tests

University of Michigan - Ann Arbor

Imbalanced Classification refers to classification tasks where the number of examples in each class is unequally distributed. Typically, imbalanced classification tasks are binary classification tasks where the majority of examples in the training dataset belong to the normal class and a minority of examples belong to the abnormal class.

Balanced Classification is the opposite. It is primarily equally distributed and commonly occurs in multi-label or multi-class classification. 

Imbalanced Classification vs. Balanced Classification

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