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Download file found at bottom of page in that link, and run with count:poisson: pipe = Pipeline([("scaler", StandardScaler()), ("xgb", XGBRegressor(n_estimators=3, objective='count:poisson'))])
Comparing results before and after serializing/deserializing:
predict [0.9388053 0.9388053 0.9388053 0.9388053 0.9388053]
predict [1.13 1.13 1.13 1.13 1.13]
Additional info:
Python 3.10, all versions below are latest from pip, except xgboost which doesn't work at all per #651 (comment)_
http://onnx.ai/sklearn-onnx/auto_examples/plot_pipeline_xgboost.html
Download file found at bottom of page in that link, and run with count:poisson:
pipe = Pipeline([("scaler", StandardScaler()), ("xgb", XGBRegressor(n_estimators=3, objective='count:poisson'))])
Comparing results before and after serializing/deserializing:
predict [0.9388053 0.9388053 0.9388053 0.9388053 0.9388053]
predict [1.13 1.13 1.13 1.13 1.13]
Additional info:
Python 3.10, all versions below are latest from pip, except xgboost which doesn't work at all per #651 (comment)_
xgboost==1.7.0
skl2onnx==1.15.0
onnx==1.14.1
onnxconverter-common==1.14.0
onnxmltools==1.11.2
onnxruntime==1.16.0
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