An imbalanced dataset occurs when the classes in a dataset are not represented equally. For example, in a dataset used for binary classification, one class may have significantly more samples than the other. This imbalance can lead to biased models that perform well on the majority class but poorly on the minority class, making it challenging to accurately predict outcomes. Imbalanced datasets are common in various fields, such as fraud detection and medical diagnosis, where the occurrence of the target event is rare. Addressing this issue often involves techniques like resampling, synthetic data generation, or using specialized algorithms to improve model performance on the minority class.