AI engineering glossary · Feature engineering data prep
What is constant imputation?
Constant imputation: Filling missing values with a fixed chosen value, such as 0, -1 or the label 'Missing'. It makes missingness explicit for tree models and categorical features, though a poorly chosen constant can mislead linear models.
Constant imputation: an example
Missing 'previous_loans' is set to 0, and a missing 'occupation' is set to the category 'Unknown'.
Related terms
More Feature engineering data prep terms
- Cook's distance
- Correlation filter
- Count vectorizer
- Cyclical encoding
- Datetime features
- Discretization
- Domain knowledge features
- Dummy variable
Where you learn it
Constant imputation is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.