Model compatibility : XGBoost4J-Spark and XGBoost python


I wanted to know if the below is possible ?

Can I train my model using sklearn XGboost -->save my model–> load the saved model in spark–> predict using XGboost in mllib

I can thing of one obvious issue of the difference in data representation in spark (vector assembled format) being an issue. Is there a way to overcome this ?


Handling of missing value can be quite tricky. We have a tutorial: