clf : Classifier
classifier based on which multiple classifiers are created
for multiclass
bclf_type :
“1-vs-1” or “1-vs-all”, determines the way to generate binary
classifiers
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
clfs : list of Classifier
list of classifier instances to use
combiner : PredictionsCombiner
callable which takes care about combining multiple
results into a single one (e.g. maximal vote for
classification, MeanPrediction for regression))
propagate_ca : bool
either to propagate enabled ca into slave classifiers.
It is in effect only when slaves get assigned - so if state
is enabled not during construction, it would not necessarily
propagate into slaves
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
harvest_attribs : list of (str or dict)
What attributes of call to store and return within
harvested conditional attribute. If an item is a dictionary,
following keys are used [‘name’, ‘copy’].
copy_attribs : None or str, optional
Default copying. If None – no copying, ‘copy’
- shallow copying, ‘deepcopy’ – deepcopying.
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