AI engineering glossary · Feature engineering data prep
What is wrapper method?
Wrapper method: A feature selection approach that evaluates subsets of features by actually training a model on each and measuring performance. It accounts for interactions and suits the chosen model, but is computationally expensive.
Wrapper method: an example
You train a logistic regression on many candidate subsets and keep the one with the best cross-validated accuracy.
Related terms
More Feature engineering data prep terms
- Yeo-Johnson transform
- ADASYN
- Aggregation feature
- ANOVA F-test
- Automated feature engineering
- Backward elimination
- Bag of words
- Binary encoding
Where you learn it
Wrapper method is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.