AI engineering glossary · Feature engineering data prep
What is oversampling?
Oversampling (also called Random oversampling): Balancing classes by increasing the number of minority-class examples, either by copying existing ones or generating new ones. It keeps all data but can cause overfitting when copies are exact, and must be applied only to training data.
Oversampling: an example
Duplicating the 200 fraud cases 10 times gives 2,000 fraud examples in the training set.
Related terms
More Feature engineering data prep terms
- Polynomial features
- Power transform
- Quantile transform
- Rare category grouping
- Ratio feature
- Recursive feature elimination
- Robust scaling
- Rolling statistics
Where you learn it
Oversampling is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.