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The question is, what the desired strategy is to fix this error. (I haven't delved into merge_ebms to know what's going on.) Another option would be setting the parameters where all classifiers agree, and setting all other parameters to None. This avoids immediate AttributeErrors but might cause problems later, in case None is not a valid parameter.
The text was updated successfully, but these errors were encountered:
The result of
merge_ebm
is not a valid classifier, as attributes are missing. Therepr
raises anAttributeError
which makes it horrible to debug.The following is a minimal reproducible example:
Which results in the error
AttributeError: 'ExplainableBoostingClassifier' object has no attribute 'cyclic_progress'
.A hotfix is simply copying the attributes of the first classifier in the list:
The question is, what the desired strategy is to fix this error. (I haven't delved into
merge_ebms
to know what's going on.) Another option would be setting the parameters where all classifiers agree, and setting all other parameters to None. This avoids immediateAttributeError
s but might cause problems later, in caseNone
is not a valid parameter.The text was updated successfully, but these errors were encountered: