AI engineering glossary · Feature engineering data prep
What is multiple imputation?
Multiple imputation: A statistical method that creates several completed copies of a dataset with different plausible fills for missing values, analyses each and combines the results. It reflects the uncertainty due to missing data instead of pretending the fill is known.
Multiple imputation: an example
Five imputed datasets are created, a regression is fitted on each, and the coefficients are pooled using Rubin's rules.
Related terms
More Feature engineering data prep terms
- Mutual information
- Nominal variable
- One-hot encoding
- Ordinal encoding
- Ordinal variable
- Outlier treatment
- Oversampling
- Polynomial features
Where you learn it
Multiple 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.