Undersampling
Undersampling is a data preprocessing technique used in machine learning to address class imbalance in datasets by reducing the number of instances in the majority class. It involves randomly or strategically removing samples from the overrepresented class to create a more balanced distribution, which can improve model performance on minority classes. This technique is commonly applied in classification problems where one class significantly outnumbers others, such as fraud detection or medical diagnosis.
Developers should learn undersampling when working with imbalanced datasets, as it helps prevent models from being biased toward the majority class and improves metrics like recall and F1-score for minority classes. It is particularly useful in scenarios like anomaly detection, where rare events (e.g., fraudulent transactions) are critical to identify, and in healthcare applications for detecting diseases with low prevalence. However, it should be used cautiously to avoid losing valuable data from the majority class.