learner : Learner
Any trainable node that shall be run on the dataset folds.
generator : Node
Generator used to resample the input dataset into multiple instances
(i.e. partitioning it). The number of datasets yielded by this
generator determines the number of cross-validation folds.
IMPORTANT: The space of this generator determines the attribute
that will be used to split all generated datasets into training and
testing sets.
errorfx : Node or callable
Custom implementation of an error function. The callable needs to
accept two arguments (1. predicted values, 2. target values). If not
a Node, it gets wrapped into a BinaryFxNode.
splitter : Splitter or None
A Splitter instance to split the dataset into training and testing
part. The first split will be used for training and the second for
testing – all other splits will be ignored. If None, a default
splitter is auto-generated using the space setting of the
generator. The default splitter is configured to return the
1-labeled partition of the input dataset at first, and the
2-labeled partition second. This behavior corresponds to most
Partitioners that label the taken-out portion 2 and the remainder
with 1.
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
node : Node
Node or Measure implementing the procedure that is supposed to be run
multiple times.
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.
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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