For a given classifier report an error based on internally computed error measure (given by some ConfusionMatrix stored in some conditional attribute of Classifier).
This way we can perform feature selection taking as the error criterion either learning error, or transfer to splits error in the case of SplitClassifier
Notes
Available conditional attributes:
(Conditional attributes enabled by default suffixed with +)
Methods
reset() | |
untrain() | Untrain the *Error which relies on the classifier |
Initialization.
Parameters: | clf : Classifier
confusion_state :
labels : list
train : bool
descr : str
enable_ca : None or list of str
disable_ca : None or list of str
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Methods
reset() | |
untrain() | Untrain the *Error which relies on the classifier |