Using the JDM API - Java Data Mining: Strategy, Standard, and Practice - page 217

Table 9-12 javax.datamining.supervised.classificaton.ClassificationModel

interfaces (continued)

Methods Description

getApplicationName(): String Returns the name of the application that generated this

model.

getVersion(): String Returns the JDM version by which the model was built.

getMajorVersion(): String Returns the major version of JDM by which the model was

built.

getMinorVersion(): String Returns the minor version of JDM by which the model was

built.

getProviderName(): String Returns the name of the JDM provider, i.e., the vendor, that

built the model.

getProviderVersion(): String Returns the version number of the JDM system provided by

a vendor that was used to build the model.

produced by different classification algorithms. Lines 16 to 26 show
retrieval of the TreeModelDetail object that encapsulates the decision
tree nodes; using its methods, applications can display the decision
tree. The TreeNode encapsulates a node's prediction value, the num-
ber of cases per node, the rule associated with the node, and the
node's child and parent node references.
In this example, we retrieve the first level node details. Lines 27
to 37 show retrieval of the NaiveBayesModelDetail object that encap-
sulates the target probability and pair probability details. We
retrieve the target probability associated with the target attrite
attribute value Attriter , pair probabilities associated with the capital
gains values, and the attrite value Non-attriter . Using these details
applications can show the target and attribute value correlations.
Lines 37 to 43 show the retrieval of the SVMClassificationModelDetail
object that encapsulates coefficients associated with the attribute
and target value pairs. In line 40, we retrieve the coefficients associ-
ated with the capital gains attribute values and the attrite value
Attriter . Lines 45 and 46 show the retrieval of the NeuralNetwork-
ModelDetail object that encapsulates the details of the weights associ-
ated with the neurons. In lines 47 to 58, we retrieve the weights
associated with each layer and the parent and child neuron possible
combinations.