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Hierarchical Classification

Synopsis​

Builds a hierarchical classification model due to the specified class taxonomy.

Description​

This meta learner builds a hierarchical classification model due to a class taxonomy. This class taxonomy has to be specified within the hierarchy parameter list. Each list entry represents an edge in the class hierarchy which in fact represents a parent-child class relationship. You need to specify one root node and assign each other node to one father.

Input​

training set​

This input port expects an ExampleSet holding the training data.

Output​

model​

The meta model is delivered from this output port which can now be applied on unseen data sets for prediction of the label attribute.

example set​

The ExampleSet that was given as input is passed without changing to the output through this port.

Parameters​

hierarchy​

This parameter is used for specifying the class hierarchy. See the tutorial process for further explanation.

use local random seed​

This parameter indicates if a local random seed should be used for randomization. Using the same value of local random seed will produce the same sample. Changing the value of this parameter changes the way examples are randomized, thus the sample will have a different set of values.

local random seed​

This parameter specifies the local random seed. This parameter is only available if the use local random seed parameter is set to true.