AI engineering glossary · Feature engineering data prep
What is missing value imputation?
Missing value imputation (also called Imputation): Filling in missing entries with estimated values so that models needing complete data can be used. Simple rules use the mean, median or most frequent value; advanced ones predict the value from other columns. The choice can bias results.
Missing value imputation: an example
Missing ages in a customer table are replaced with the median age of 34.
Related terms
More Feature engineering data prep terms
- Mode imputation
- Multiple imputation
- Mutual information
- Nominal variable
- One-hot encoding
- Ordinal encoding
- Ordinal variable
- Outlier treatment
Where you learn it
Missing value 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.