AI engineering glossary · Feature engineering data prep
What is MNAR (missing not at random)?
Missing not at random (also called MNAR): A missingness pattern where whether a value is missing depends on the missing value itself. It is the hardest case, as bias cannot be fixed with the data alone and needs domain reasoning or a model of the missingness.
Missing not at random: an example
High earners leave the income field blank because it is high, so the blanks hide exactly the values that matter.
Related terms
More Feature engineering data prep terms
- Missing value imputation
- Mode imputation
- Multiple imputation
- Mutual information
- Nominal variable
- One-hot encoding
- Ordinal encoding
- Ordinal variable
Where you learn it
Missing not at random is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.