Question 18
UnclassifiedWhich advanced imputation method models each feature as a function of the others using iterative regression?
Correct answer: B
Explanation
MICE stands for "Multiple Imputation by Chained Equations" and uses iterative regression to model each feature as a function of the others. It cycles through the variables, imputing missing values one at a time until the estimates stabilize.
Why each option is right or wrong
A. KNN imputation
B. MICE (Multiple Imputation by Chained Equations)
MICE is the standard advanced imputation approach that fits a separate regression model for each variable with missing data, using the remaining variables as predictors, then cycles through those models repeatedly until convergence. In practice, this chained-equations procedure is what distinguishes it from single-pass methods like mean/median imputation or simple joint-model approaches, because each feature is updated iteratively based on the others.
C. Mean imputation
D. Constant imputation