[ENH] Support for discrete output distributions and probabilistic classification#1029
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Ironankit525 wants to merge 2 commits intosktime:mainfrom
Open
[ENH] Support for discrete output distributions and probabilistic classification#1029Ironankit525 wants to merge 2 commits intosktime:mainfrom
Ironankit525 wants to merge 2 commits intosktime:mainfrom
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…sification (sktime#1003) - Introduce skpro.classification module with BaseProbaClassifier - Expose scikit-learn adapter SklearnClassifierAdapter - Add Discrete distribution with correct mode() - Fix tag parent-mapping in skpro registry to support BaseProbaClassifier
Ironankit525
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felipeangelimvieira and
fkiraly
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Fixes #1003.
This PR introduces a native classification API to
skpro, making semantic probabilistic classification natively supported returning distribution objects (e.g., discrete classes) rather than raw numpy arrays, maintaining consistency withskpro.regression.Changes Made:
skpro.classificationmodule withBaseProbaClassifierbase framework.Discretebaseline distribution insideskpro.distributionsfor predicting probability arrays over class labels.SklearnClassifierAdapterallowing users to easily adaptscikit-learnprobabilistic classifiers._tags.pyto support theclassifier_probaobject_type seamlessly.