Comments (1)
As you correctly point out, the JPMML-SkLearn only supports the conversion of Apache Spark ML models and pipelines.
There are no plans to start supporting Apache Spark MLlib. It is my understanding that the Apache Spark team has decided to gradually phase out MLlib functionality, so it would be pointless to spend any resources on it.
I would personally recommend you to re-train your GBDT model using Apache Spark ML. Alternatively, you might want to open discussion with Apache Spark team, and see if they are willing to apply the PMMLExportable
trait to the GBDT model type. This trait doesn't seem to cover decision tree model types at the moment, so prospect is not so good.
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Related Issues (20)
- MultilayerPerceptronClassificationModel IllegalArgumentException("Expected 3 target categories, got 2 target categories"); HOT 1
- How to import the training data schema in libsvm format HOT 15
- Wrong code path for multinomial logistic regression model HOT 1
- Probability column not being found when using it in a stacked model HOT 6
- StringIndexerModelConverter gives java.lang.IllegalArgumentException HOT 4
- java.lang.ClassNotFoundException: org.jpmml.converter.BaseNFeature HOT 5
- Support for custom Java-backed models (eg. factorization machine) HOT 1
- Why One-Hot-Encoding is not visible in PMML? HOT 1
- py4j.protocol.Py4JError: org.jpmml.sparkml.PMMLBuilder does not exist in the JVM HOT 1
- Error with LightGBMClassificationModel HOT 5
- Support for `XGBoostRegressor.missing` property HOT 6
- Troubleshooting XGBoost model performance HOT 17
- Support for Apache Spark 3.3.X HOT 2
- 2.x jars missing from Maven Central HOT 3
- Support for `replace` SQL function HOT 6
- Exception in thread "main" java.lang.NoClassDefFoundError: com/microsoft/azure/synapse/ml/codegen/Wrappable
- java.lang.NoSuchMethodError: org.jpmml.sparkml.SparkMLEncoder.getDataField HOT 1
- Databricks Install HOT 1
- Version v4 is not supported HOT 2
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