node : Node
Node or Measure implementing the procedure that is supposed to be run
multiple times.
generator : Node
Generator to yield a dataset for each measure run. The number of
datasets returned by the node determines the number of runs.
callback : functor
Optional callback to extract information from inside the main loop of
the measure. The callback is called with the input ‘data’, the ‘node’
instance that is evaluated repeatedly and the ‘result’ of a single
evaluation – passed as named arguments (see labels in quotes) for
every iteration, directly after evaluating the node.
concat_as : {‘samples’, ‘features’}
Along which axis to concatenate result dataset from all iterations.
By default, results are ‘vstacked’ as multiple samples in the output
dataset. Setting this argument to ‘features’ will change this to
‘hstacking’ along the feature axis.
enable_ca : None or list of str
Names of the conditional attributes which should be enabled in addition
to the default ones
disable_ca : None or list of str
Names of the conditional attributes which should be disabled
null_dist : instance of distribution estimator
The estimated distribution is used to assign a probability for a
certain value of the computed measure.
auto_train : bool
Flag whether the learner will automatically train itself on the input
dataset when called untrained.
force_train : bool
Flag whether the learner will enforce training on the input dataset
upon every call.
space: str, optional :
Name of the ‘processing space’. The actual meaning of this argument
heavily depends on the sub-class implementation. In general, this is
a trigger that tells the node to compute and store information about
the input data that is “interesting” in the context of the
corresponding processing in the output dataset.
postproc : Node instance, optional
Node to perform post-processing of results. This node is applied
in __call__() to perform a final processing step on the to be
result dataset. If None, nothing is done.
descr : str
Description of the instance
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