What is iterative (MICE) imputation?
hardAnswer
- Model each feature with missing values as the target of a regression/classification against all other features; iteratively predict and impute round-robin until convergence.
- Handles arbitrary correlations, mixed types (with the right per-column estimator), and uncertainty estimates when combined with multiple imputation (average predictions from several draws). scikit-learn's IterativeImputer with BayesianRidge is a solid default; use RandomForest for non-linear relationships.
- Slower than single imputation but usually gives much better downstream models.
Check yourself — multiple choice
- MICE fills columns randomly
- Round-robin regression: predict each feature from the others, iterate to convergence
- Same as mean imputation
- MICE can only handle categorical data
MICE: iteratively regress each missing feature on the others until convergence.
#missing-data
Practise Supervised Learning
215 interview questions in this topic.