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[BUG] classifiers failing on multiclass scenario due to _get_train_probs
#6376
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fkiraly
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module:classification
classification module: time series classification
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May 2, 2024
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[BUG] classifiers failing on multiclass scenario
[BUG] classifiers failing on multiclass scenario due to May 2, 2024
_get_train_probs
Update: this is due to the |
fkiraly
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May 29, 2024
…data type (#6377) This PR fixes #6376 by ensuring the `_get_train_probs` method - with undocumented contract - accepts `X_train` of any permissible panel input type. The fix is adding an input converter to numpy3D, which all currently implemented instances seem to assume. This approach should fix the failing `test_classifier_output` test for the new scenario, without removing functionality (even if private), or degrading efficiency in a case where the input is numpy already. Depends on #6374 for testing.
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The following classifiers fail on a scenario with three classes, see #6374 for the scenario:
Arsenal
BOSSEnsemble
ContractableBOSS
DrCIF
FreshPRINCE
ShapeletTransformClassifier
TemporalDictionaryEnsemble
TSCGridSearchCV
- reason is not the classifier, but folds being too small in the scenarioWeightedEnsembleClassifier
A quick solution could be to introduce a
capability:multiclass
tag, but perhaps the bug is easy to fix.The text was updated successfully, but these errors were encountered: