svmnode properties

Last updated: Jan 18, 2024

svmnode properties

SVM node icon The Support Vector Machine (SVM) node enables you to classify data into one of two groups without overfitting. SVM works well with wide data sets, such as those with a very large number of input fields.

Example

node = stream.create("svm", "My node")
# Expert tab
node.setPropertyValue("mode", "Expert")
node.setPropertyValue("all_probabilities", True)
node.setPropertyValue("kernel", "Polynomial")
node.setPropertyValue("gamma", 1.5)

Table 1. svmnode properties
`svmnode` Properties	Values	Property description
`all_probabilities`	flag
`stopping_criteria`	`1.0E-1` `1.0E-2` `1.0E-3` `1.0E-4` `1.0E-5` `1.0E-6`	Determines when to stop the optimization algorithm.
`regularization`	number	Also known as the C parameter.
`precision`	number	Used only if measurement level of target field is `Continuous`.
`kernel`	`RBF` `Polynomial` `Sigmoid` `Linear`	Type of kernel function used for the transformation. `RBF` is the default.
`rbf_gamma`	number	Used only if `kernel` is `RBF`.
`gamma`	number	Used only if `kernel` is `Polynomial` or `Sigmoid`.
`bias`	number
`degree`	number	Used only if `kernel` is `Polynomial`.
`calculate_variable_importance`	flag
`calculate_raw_propensities`	flag
`calculate_adjusted_propensities`	flag
`adjusted_propensity_partition`	`Test` `Validation`