__init__(self,
transerror,
splitter=None,
combiner='mean',
expose_testdataset=False,
harvest_attribs=None,
copy_attribs='copy',
**kwargs)
(Constructor)
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:Parameters:
transerror: TransferError instance
Provides the classifier used for cross-validation.
splitter: Splitter | None
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. If `None` (default) an instance of
:class:`~mvpa.datasets.splitters.NoneSplitter` is used.
combiner: Functor | 'mean'
Used to aggregate the error values of all cross-validation
folds. If 'mean' (default) the grand mean of the transfer
errors is computed.
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 | basestr
Force copying values of attributes on harvesting
**kwargs:
All additional arguments are passed to the
:class:`~mvpa.measures.base.DatasetMeasure` base class.
- Parameters:
transformer , Functor - This functor is called in __call__() to perform a final
processing step on the to be returned dataset measure. If None,
nothing is called
null_dist , instance , of , distribution , estimator - The estimated distribution is used to assign a probability for a
certain value of the computed measure.
- Overrides:
object.__init__
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