AI engineering glossary · Feature engineering data prep
What is MAR (missing at random)?
Missing at random (also called MAR): A missingness pattern where the chance of a value being missing depends on other observed variables but not on the missing value itself. Imputation using the observed columns can then give unbiased results.
Missing at random: an example
Younger respondents skip an income question more often; since age is recorded, income is missing at random given age.
Related terms
More Feature engineering data prep terms
- Missing completely at random
- Missing indicator
- Missing not at random
- Missing value imputation
- Mode imputation
- Multiple imputation
- Mutual information
- Nominal variable
Where you learn it
Missing 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.