Comments (2)
Hi @wschin
Thanks for building this package. I'm very interested in using this package especially to convert sklearn models to ONNX. One of the key things as you know is being able to use FeatureUnions and Pipelines.
I was wondering if you have any ETA in mind for this feature being made available.
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@steadyfish You should be able to use FeatureUnions and Pipelines.
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Related Issues (20)
- Convert_xgboost for XGBRegressor with objective="reg:logistic" gives wrong results
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- XGBRegressor with objective='count:poisson' causes serialization to fail with no error message HOT 1
- Please help validate release candidate for ONNX 1.15.0rc1 HOT 1
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- `XGBRFClassifier` & `XGBRFRegressor` are not supported HOT 22
- `TreeEnsembleRegressor` introduces an additional batch dimension HOT 2
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- XGBClassifier conversion to onnx gives wrong predictions if dataset has only 2 categories (xgboost 2.0)
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- No module named 'onnxconverter_common' HOT 2
- onnx converted : xgboostRegressor multioutput model predicts 1 dimension instead of original 210 dimensions. HOT 4
- UNABLE_TO_INFER_SCHEMA on pyspark
- Integrate with ONNX 1.16.0 release branch HOT 1
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