Comments (3)
RandomForestRegressor has a built-in support for multiple outputs (no need to use MultiOutputRegressor).
I think it is more important to add support for that case, before implementing the MultiOutputRegressor meta-regressor.
from sklearn2pmml.
Here is my environment:
print(sklearn.version)
0.18.2
print(sklearn.externals.joblib.version)
0.10.3
print(sklearn_pandas.version)
1.5.0
print(sklearn2pmml.version)
0.20.4
Python 2.7.13
from sklearn2pmml.
Exception in thread "main" java.lang.IllegalArgumentException
at sklearn2pmml.PMMLPipeline.encodePMML(PMMLPipeline.java:74)
This exception means that PMMLPipeline does not recognize the enclosed estimator object.
Class MultiOutputRegressor
is currently not supported (for a list of supported estimator and transformer classes, see the README file of the JPMML-SkLearn project). According to the Scikit-Learn documentation, it can be implemented fairly easily, because "under the hood" it is just constructing and fitting multiple copies of the argument estimator type.
Is there any plan to support this function?
Sure. But the timeline is unclear, as I have more important things to do in the near future.
from sklearn2pmml.
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from sklearn2pmml.