Kernel object. (Default: None)
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
Epsilon in epsilon-insensitive loss function of epsilon-SVM
regression (SVR). (Default: 0.01)
Trade-off parameter between width of the margin and number of
support vectors. Higher C – more rigid margin SVM. In linear
kernel, negative values provide automatic scaling of their value
according to the norm of the data. (Default: -1.0)
Custom weights per label. (Default: )
Flag to signal either probability estimate is obtained within
LIBSVM. (Default: 0)
Tolerance of termination criteria. (For nu-SVM default is 0.001).
To be used in conjunction with weight for custom per-label weight.
Either shrinking is to be conducted. (Default: 1)
Fraction of datapoints within the margin. (Default: 0.5)
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