What is the aim of oversampling in data sets?

Oversampling is a technique used in data analysis, particularly in the context of imbalanced datasets, where one class or group is significantly underrepresented compared to others. The primary aim of oversampling is to better represent nondominant groups to ensure that statistical models do not bias towards the majority class. By increasing the number of instances for these minority classes, analysts can improve the model's ability to learn from and make predictions about these less frequent occurrences, ultimately leading to more robust and reliable outcomes.

Representing nondominant groups accurately is crucial, especially in scenarios like fraud detection or disease diagnosis, where failing to recognize minority classes may lead to critical mispredictions. This strategic balance helps in achieving more equitable performance across all classes in predictive modeling.