skl_learner :
Existing instance of a learner from skl. It should
implement fit and predict. If predict_proba is
available in the interface, then conditional attribute
probabilities becomes available as well
tags : list of string
What additional tags to attach to this learner. Tags are
used in the queries to classifier or regression warehouses.
enforce_dim : None or int, optional
If not None, it would enforce given dimensionality for
predict call, if all other trailing dimensions are
degenerate.
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
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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