(AI Studio Core)
Synopsis
This operator creates a performance vector containing the attribute count of the input ExampleSet.Description
This is a very simple operator. It takes an ExampleSet as input and returns a performance vector that has the count of attributes in the given ExampleSet. Optionally, a performance vector can be provided as input as well. In that case the 'number of attributes' criteria is appended to the given performance vector.
Input
example set (Data table)This input port expects an ExampleSet. It is the output of the Subprocess operator in the attached Example Process.
performance (Performance Vector)This optional port expects a performance vector. A performance vector is a list of performance criteria values.
Output
- performance (Performance Vector)
The performance vector containing the 'number of attributes' criteria is returned through this port.
- example set (Data table)
ExampleSet that was given as input is passed without any modifications to the output through this port. This is usually used to reuse the same ExampleSet in further operators or to view the ExampleSet in the Results Workspace.
Parameters
- optimization directionThis is an expert parameter. It indicates if the fitness should be maximal for the maximal or the minimal number of features.
Tutorial Processes
Generating a performance vector with the 'number of attributes' criteria
This Example Process starts with the Subprocess operator. The subprocess delivers an ExampleSet and a performance vector. A breakpoint is inserted here so that you can have a look at the ExampleSet. You can see that the ExampleSet has four regular attributes. This ExampleSet is provided as input to the Performance (Attribute Count) operator which returns a performance vector with the 'number of attributes' criteria. As there were four attributes in the given ExampleSet, the 'number of attributes' criteria has value 4. Now connect the second output port of the Subprocess operator to the performance input port of the Performance (Attribute Count) operator. Run the process again, you will see that this time the 'number of attributes' parameter is appended to the given performance vector.
