AI engineering glossary · Feature engineering data prep
What is MICE (iterative imputation)?
Iterative imputation (also called MICE, IterativeImputer): An approach that models each feature with missing values as a function of the other features, and repeatedly predicts and refills the blanks in rounds until estimates settle. It captures relationships among columns, unlike single-value fills.
Iterative imputation: an example
Missing income is predicted from age and education, then missing age from income and education, and the cycle repeats until stable.
Related terms
More Feature engineering data prep terms
- K-fold target encoding
- KNN imputation
- Kurtosis
- Label encoding
- Lag feature
- Last observation carried forward
- Leave-one-out encoding
- Lemmatization
Where you learn it
Iterative 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.