Last updated: Jan 17, 2024
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) svmnode Properties |
Values | Property description |
|---|---|---|
all_probabilities |
flag | |
stopping_criteria |
1.0E-11.0E-21.0E-31.0E-41.0E-51.0E-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 |
RBFPolynomialSigmoidLinear |
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 |
TestValidation |