Techniques used to handle imbalanced datasets (where one class is much more frequent than others). Undersampling reduces the number of examples in the majority class, while oversampling increases the number of examples in the minority class (often by duplication or synthesizing new examples).
Standard practices in data preprocessing for classification tasks.
Essential for applications like fraud detection, medical diagnosis, and rare event prediction.