In this recipe, we mentioned how to impute missing values. Approaches such as replacing the missing value with one large value or the mean/median/mode are called single imputation approaches, as they replace missing values with one specific value. However, there are also multiple imputation approaches, and one of those is Multiple Imputation by Chained Equations (MICE). In short, the algorithm runs multiple regression models, and each missing value is determined conditionally on the basis of the non-missing data points. A potential benefit of using an ML-based approach to imputation is the reduction of bias introduced by single imputation.

The MICE algorithm is available in scikit-learn, under the name of IterativeImputer, in the impute module.