__init__(self,
transerror,
splitter=NoneSplitter(),
combiner=GrandMean,
expose_testdataset=False,
harvest_attribs=None,
copy_attribs='copy',
**kwargs)
(Constructor)
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Cheap initialization.
- Parameters:
transerror (TransferError instance) - Provides the classifier used for cross-validation.
splitter (Splitter instance) - Used to split the dataset for cross-validation folds. By
convention the first dataset in the tuple returned by the
splitter is used to train the provided classifier. If the
first element is 'None' no training is performed. The second
dataset is used to generate predictions with the (trained)
classifier.
combiner (Functor) - Used to aggregate the error values of all cross-validation
folds.
expose_testdataset (bool) - In the proper pipeline, classifier must not know anything
about testing data, but in some cases it might lead only
to marginal harm, thus migth wanted to be enabled (provide
testdataset for RFE to determine stopping point).
harvest_attribs (list of basestr) - What attributes of call to store and return within
harvested state variable
copy_attribs (None or basestr) - Force copying values of attributes on harvesting
- Overrides:
object.__init__
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