Class imbalance occurs when the distribution of different categories in a dataset is uneven. For example, in a dataset used for a machine learning model, if 90% of the samples belong to one class and only 10% to another, the model may struggle to learn effectively from the minority class. This can lead to biased predictions, where the model favors the majority class. To address class imbalance, various techniques can be employed. These include oversampling the minority class, undersampling the majority class, or using synthetic data generation methods like SMOTE. These strategies help create a more balanced dataset, improving the model's performance on all classes.