Comments (2)
Many of the ML.NET classes and utilities that NimbusML relied on have been internalized, so we will need to refactor the parts of the bridge that use them. The steps to do so are described in dotnet/machinelearning#1959
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Work is broken down into the following tasks: #81 #82 #83 #84 #85
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Related Issues (20)
- Numerical categorical columns are not supported
- Documentation of Handler's "replace_with" parameter is misguiding
- No distribution found HOT 2
- Output of Label Column when applying ONNX model is not as expected HOT 1
- KMeansPlusPlus PredictedLabel Type
- OneHotVectorizer ONNX model returns 3D array HOT 1
- LogisticRegressionClassifier and FastLinearClassifier fail ONNX export test HOT 1
- Creating model from ml.net and using in nimbusml getting error HOT 3
- Mismatch in output of onnx exported CharTokenizer model HOT 1
- Onnx export of ColumnSelector doesn't drop input columns
- Support TreeFeaturizer transform
- Need proper call stack details on RuntimeException in python
- getting error which classification with text label
- 'PcaAnomalyDetector' meets error "System.FormatException: 'One of the identified items was in an invalid format.'"
- 'PcaAnomalyDetector' meets error "System.FormatException: 'One of the identified items was in an invalid format.'" in predicting HOT 1
- Is there any way of getting the topic_word distribution from LightLDA?
- Nimbusmil returned a BridgeRuntimeException with empty callstack property
- Quantile Regression using fast forest regressor
- Docs for Multiclass Classification Metrics are wrong.
- When Transform is called without calling Fit, a ValueError is thrown. Something similar to sklearn.exceptions.NotFittedError would be more appropriate?
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