Closed jtzhang17 closed 2 years ago
The saved PMML model was loaded using the pypmml-spark package
The PyPMML library suppresses secondary result fields by default. I have zero control over this behaviour.
Can someone share me an example that the saved model from pyspark2pmml can produce the probability column in the model evaluation results?
Please take your issue to the PyPMML project. It does not belong to here.
Could you please explain a little bit more about PyPMML library suppresses secondary result fields by default
? You mean the probability
column is a secondary
result? What does secondary result
mean? Thanks!
The saved PMML model was loaded using the pypmml-spark package
The PyPMML library suppresses secondary result fields by default. I have zero control over this behaviour.
I have a PySpark XGBoost pipelineModel, and it was saved as PMML in the following way:
The saved PMML model was loaded using the
pypmml-spark
package, and a testing data set was applied to the loaded model. However, the final results always contain oneprediction
column, but never include theprobability
orrawPrediction
columns.Can someone share me an example that the saved model from
pyspark2pmml
can produce theprobability
column in the model evaluation results?