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 method is commonly applied in classification tasks where one class significantly outnumbers others, such as fraud detection or medical diagnosis.

Also known as: Downsampling, Majority class reduction, Class balancing via removal, Random undersampling, RUS

Why learn Undersampling?

Developers should learn and use undersampling when working with imbalanced datasets, as it helps prevent models from being biased toward the majority class, leading to poor recall or precision 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 applications where collecting more data for the minority class is impractical or costly. However, it should be applied cautiously to avoid losing valuable information from the majority class.

Learning Resources

• docs: Imbalanced-Learn Documentation
• tutorial: Handling Imbalanced Data: Undersampling Tutorial by Analytics Vidhya
• tutorial: Machine Learning Mastery: Undersampling Algorithms for Imbalanced Classification
• course: Coursera: Dealing with Imbalanced Data in Machine Learning