Class Imbalance

Class imbalance refers to a situation in machine learning where the distribution of examples across different classes is not uniform. In many datasets, one class may have significantly more instances than others, leading to biased models that favor the majority class. This imbalance can hinder the model's ability to generalize and accurately predict outcomes for the minority class. Common use cases include medical diagnosis, fraud detection, and any domain where rare events are critical. Techniques to address class imbalance include resampling methods, cost-sensitive learning, and using specialized evaluation metrics.