Adaptive Synthetic Sampling (ADASYN) is a technique used in the field of Epidemiology and Machine Learning to address the problem of class imbalance. Class imbalance occurs when one class in a dataset is significantly underrepresented compared to other classes, which can lead to biased model performance. ADASYN generates synthetic data points for the minority class to balance the dataset and improve the accuracy of predictive models.
