Data imbalance occurs when the distribution of different categories in a dataset is uneven. For example, in a dataset used for training a machine learning model, one class may have significantly more examples than another. This can lead to biased predictions, as the model may become overly focused on the majority class and neglect the minority class. To address data imbalance, techniques such as oversampling, undersampling, or using synthetic data generation can be employed. These methods help create a more balanced dataset, allowing models to learn from all classes more effectively and improving overall performance in tasks like classification.