ADASYN stands for Adaptive Synthetic Sampling. It is a technique used in machine learning to address the problem of class imbalance in datasets. When one class has significantly fewer samples than another, it can lead to biased models. ADASYN generates synthetic data points for the minority class, helping to balance the dataset and improve model performance. The method works by creating new samples based on the existing minority class instances. It focuses on generating more synthetic examples in regions where the minority class is underrepresented. This adaptive approach enhances the learning process, allowing algorithms to better recognize patterns in the data.