AI engineering glossary · Feature engineering data prep
What is undersampling?
Undersampling (also called Random undersampling): Balancing classes by removing examples from the majority class so the classes end up closer in size. It speeds training but throws away information, so it suits very large datasets.
Undersampling: an example
Randomly keeping 2,000 of 99,800 non-fraud cases beside 200 frauds gives a much more balanced training set.
Related terms
More Feature engineering data prep terms
- Unit vector scaling
- Unknown category handling
- Variance threshold
- Weight of evidence
- Window feature
- Winsorization
- Wrapper method
- Yeo-Johnson transform
Where you learn it
Undersampling is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.