Impact on Employment

Imbalanced data in machine learning refers to datasets where certain classes or target variables are much more common than others. This imbalance can lead models to favor the majority class, resulting in poor performance on minority classes. Addressing this issue often involves techniques like resampling, synthetic data generation, or choosing appropriate evaluation metrics to ensure balanced learning and accurate predictions across all classes.