AI engineering glossary · Feature engineering data prep
What is outlier treatment?
Outlier treatment: The set of choices for handling extreme values: keep them, cap them, transform them or remove them. The right choice depends on whether they are errors or real rare events, so it needs domain judgement.
Outlier treatment: an example
A recorded age of 250 is a data-entry error to fix, while a Rs 5 crore purchase in an e-commerce dataset may be real and worth keeping.
Related terms
More Feature engineering data prep terms
- Oversampling
- Polynomial features
- Power transform
- Quantile transform
- Rare category grouping
- Ratio feature
- Recursive feature elimination
- Robust scaling
Where you learn it
Outlier treatment is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.