Allows to use arbitrary regression for classification.
Possible usecases:
- Binary Classification
- Any regression could easily be extended for binary classification. For instance using labels -1 and +1, regression results are quantized into labels depending on their signs
- Multiclass Classification
- Although most of the time classes are not ordered and do not have a corresponding distance matrix among them it might often be the case that there is a hypothesis that classes could be well separated in a projection to single dimension (non-linear manifold, or just linear projection). For such use regression might provide necessary means of classification
Notes
Available conditional attributes:
(Conditional attributes enabled by default suffixed with +)
Parameters : | clf : Classifier XXX Should become learner
centroids : None or dict of (float or iterable)
distance_measure : function or None
enable_ca : None or list of str
disable_ca : None or list of str
auto_train : bool
force_train : bool
space: str, optional :
postproc : Node instance, optional
descr : str
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