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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.
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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 (default)
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(default)
Polynomial
Sigmoid
Linear |
Type of kernel function used for the transformation. |
rbf_gamma |
number |
Used only if kernel is RBF. |
gamma |
number |
Used only if kernel is Polynomial or Sigmoid. |
bias |
number |
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degree |
number |
Used only if kernel is Polynomial. |
calculate_variable_importance |
flag |
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calculate_raw_propensities |
flag |
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calculate_adjusted_
propensities
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flag |
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adjusted_propensity_partition |
Test
Validation |
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