Oversampling

Oversampling is a technique used in data analysis, especially when dealing with imbalanced datasets where one class (group) has fewer examples than others. To improve the accuracy of models, oversampling increases the number of examples in the smaller class by replicating existing data or creating new synthetic data points. This helps the model better learn the characteristics of the minority class, leading to improved predictions and fairness. Essentially, oversampling balances the dataset, making it easier for machine learning algorithms to recognize and accurately classify all classes.