Exercise

MICE imputation

The fancyimpute package offers various robust machine learning models for imputing missing values. You can explore the complete list of imputers from the detailed documentation. Here, we will use IterativeImputer or popularly called MICE for imputing missing values.

The IterativeImputer performs multiple regressions on random samples of the data and aggregates for imputing the missing values. You will use the diabetes DataFrame for performing this imputation.

Instructions

100 XP

Import IterativeImputer from fancyimpute.
Copy diabetes to diabetes_mice.
Create an IterativeImputer() object and assign it to mice_imputer.
Impute the diabetes DataFrame.

Take Hint (-30 XP)