AI engineering glossary · Feature engineering data prep
What is MCAR (missing completely at random)?
Missing completely at random (also called MCAR): A missingness pattern where the chance a value is missing is unrelated to any data, observed or unobserved. Dropping such rows only reduces sample size without adding bias, but this assumption is often unrealistic.
Missing completely at random: an example
A sensor logs nothing on random days because of a power glitch unrelated to the readings, so those gaps are missing completely at random.
Related terms
More Feature engineering data prep terms
- Missing indicator
- Missing not at random
- Missing value imputation
- Mode imputation
- Multiple imputation
- Mutual information
- Nominal variable
- One-hot encoding
Where you learn it
Missing completely 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.