Class imbalance is a binary classification problem in which the labels of the two classes have a large gap in their frequencies.

For example, in a disease dataset, 0.0001 samples have positive class labels and 0.9999 samples have negative class labels, which is a classification imbalance problem; but in a football match predictor, 0.51 samples have the label that one team wins and 0.49 samples have the label that the other team wins, which is not a classification imbalance problem.