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Support Vector Machine (RapidMiner Studio Core)
SynopsisThis operator is an SVM (Support Vector Machine) Learner. It is based on a minimal SVM implementation.
This learner uses a minimal SVM implementation. The model is built with only one positive and one negative example. Typically this operator is used in combination with a boosting method.
- training set (IOObject)
This input port expects an ExampleSet. This operator can only handle numerical attributes with only one positive and one negative example.
- model (Hyper Model)
The Hyper model is delivered from this output port. This model can now be applied on unseen data sets.
- example set (IOObject)
The ExampleSet that was given as input is passed without changing to the output through this port. This is usually used to reuse the same ExampleSet in further operators or to view the ExampleSet in the Results Workspace.
- use_local_random_seedThis parameter indicates if a local random seed should be used for randomization. Using the same value of local random seed will produce the same ExampleSet. Changing the value of this parameter changes the way examples are randomized, thus the ExampleSet will have a different set of values. Range: boolean
- local_random_seedThis parameter specifies the local random seed. This parameter is only available if the use local random seed parameter is set to true. Range: integer