Data Imbalance

Data imbalance occurs when one class or category in a dataset has significantly fewer examples than other classes. This skewed distribution can cause machine learning models to perform poorly on the minority class, as they tend to optimize for the more prevalent classes, making accurate predictions difficult for the underrepresented observations.