Class imbalance typically refers to a problem in classification tasks where there is a substantial difference in the number of samples in each class of the training data. For example, in a training dataset for the task of detecting lung cancer from X-ray images, 99.99% of the X-rays might be of normal lungs, and only 0.01% might contain cancerous cells.
