Use of Random Forest algorithm in PySpark for imputation I am wondering how to use Random Forest algorithm for imputing missing values in a dataset. It is supposed to work well with missing values but I am not sure how those missing values are dealt with and how RF imputation works in PySpark.
You can do the following: use all the other features as input and the missing data as the label.

Train using all the rows that have the column filled with data and classify the others that don't. Use the values predicted by the Random Forest as the value of that field on the subsequent models and transformations.