attr : str
Typically the sample or feature attribute used to determine splits.
attr_values : tuple
If not None, this is a list of value of the attr used to determine
the splits. The order of values in this list defines the order of the
resulting splits. It is possible to specify a particular value
multiple times. All dataset samples with values that are not listed
are going to be ignored.
count : None or int
Desired number of generated splits. If None, all splits are output
(default), otherwise the number of splits is limited to the given
count or the maximum number of possible split (whatever is less).
noslicing : bool
If True, dataset splitting is not done by slicing (causing
shared data between source and split datasets) even if it would
be possible. By default slicing is performed whenever possible
to reduce the memory footprint.
reverse : bool
If True, the order of datasets in the split is reversed, e.g.
instead of (training, testing), (training, testing) will be spit
out.
ignore_values : tuple
If not None, this is a list of value of the attr the shall be
ignored when determining the splits. This settings also affects
any specified attr_values.
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
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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