SVM Learner

This Node Is Deprecated — This version of the node has been replaced with a new and improved version. The old version is kept for backwards-compatibility, but for all new workflows we suggest to use the version linked below.
Go to Suggested ReplacementSVM Learner

This node trains a support vector machine on the input data. It supports a number of different kernels (HyperTangent, Polynomial and RBF). The SVM learner supports multiple class problems as well (by computing the hyperplane between each class and the rest), but note that this will increase the runtime.

The SVM learning algorithm used is described in the following papers: Fast Training of Support Vector Machines using Sequential Minimal Optimization, by John C. Platt and Improvements to Platt's SMO Algorithm for SVM Classifier Design, by S. S. Keerthi et. al.

If the optional PMML inport is connected and contains preprocessing operations in the TransformationDictionary those are added to the learned model.


Class column
Choose the column that contains the nominal target variable.
Overlapping penalty
The overlapping penalty is useful in the case that the input data is not separable. It determines how much penalty is assigned to each point that is misclassified. A good value for it is 1.
Kernel type
There are a number of kernels to choose from. Each kernel has its own parameters, which appear in the configuration dialog just under the kernel.

Input Ports

Datatable with training data
Optional PMML port object containing preprocessing operations.

Output Ports

Trained Support Vector Machine

Popular Successors

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SVM View
Shows the trained Support Vector Machines for each class with their corresponding support vectors.


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Further Links


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