I can perform distributed training and prediction successfully with SparkXGBRegressor, I can do cross validation using:
crossval = CrossValidator(estimator=estimator, estimatorParamMaps=parm_grid, evaluator=evaluator, numFolds=numFolds, parallelism = parallelism) cv_model = crossval.fit(data)
However after training I want to get the best hyperparameters and I am not sure how to do that since:= this is not working:
and returns something like this
Which does not make any sense. Can anyone explain how to return the best hyperparameters similar to Python which is like
I am sure there is a way to do this.