Comments (1)
Class VotingClasifier
can be represented in PMML without problems:
- The
hard
voting scheme maps to(WEIGHTED_)MAJORITY_VOTE
multiple model method. - The
soft
voting scheme maps to(WEIGHTED_)AVERAGE
multiple model method.
It's a matter of priorities.
from jpmml-sklearn.
Related Issues (20)
- Support for Scikit-Survival models that is compatible with Sklearn? HOT 4
- Support for PyCaret transformers HOT 3
- Support for transformer-only pipelines HOT 2
- Is there a way to output a custom score instead of the default probability HOT 3
- Ability to suppress the (default-) `Output` element HOT 2
- Change of output probability in PMML HOT 3
- Adding node id to output field HOT 1
- Disambiguating the output fields of multi-output models (example: `The value for field "probability(0)" has already been defined`) HOT 7
- Customizing the name and data type of `DataField` elements HOT 5
- Test failures during install on MacOS Monterey 12.6.5 HOT 2
- Enforce up-to-date checks HOT 1
- scikit-learn v1.3.X support HOT 1
- MultiDomain Expression Transformer default value HOT 1
- Maven `jpmml-sklearn` import error. Cannot resolve `org.jpmml:jpmml-sklearn:x.x.x` HOT 1
- Support for QuantileTransformer HOT 2
- Bad scoping of target field(s) in stacking estimators HOT 19
- scikit-learn version HOT 1
- Discrete domain decorators should support `pandas.CategoricalDtype` (aka "category")
- Adding `scikit-learn~=1.4.x` support
- Adding support for InterpretML EBM models
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