Performance (Ranking)
(AI Studio Core)
Synopsis
This operator delivers a performance value representing costs for the confidence rank of the true label.Description
The Performance (Ranking) operator should be used for tasks, where it is not only important that the real class is selected, but also that it receives a comparably high confidence.
This operator will sort the confidences for each label and depending on the rank position of the real label, costs are generated. You can define these costs by the parameter ranking_costs. The costs are entered for whole intervals, so you don't have to enter a cost value for each rank. These intervals are defined by their start rank and range either until the start of the next interval or infinite. Everything before the first mentioned rank will receive costs of 0. The counting of rank starts with 0, so the most confident label is rank 0.
The costs are entered on the right side of the table.
For example, if you want to assign costs of zero if the true label is predicted with the highest confidence, 1 for the second place, 2 for the third and 10 for each following, you have to enter:
1 1
2 2
3 10
Input
labeled dataThis input port expects a labeled ExampleSet. The Apply Model operator is a good example of such operators that provide labeled data. Make sure that the ExampleSet has a label attribute and a prediction attribute. See the Set Role operator for more details regarding label and prediction roles of attributes.
Output
example set (Data table)ExampleSet that was given as input is passed without change to this output port. This is usually used to reuse the same ExampleSet in further operators or to view the ExampleSet in the Results Workspace.
- performance
This port delivers a Performance Vector (we call it output-performance-vector for now). The Performance Vector is a list of performance criteria values. The Performance vector is calculated on the basis of the label attribute and the prediction attribute of the input ExampleSet. The output-performance-vector contains performance criteria calculated by this Performance operator (we call it calculated-performance-vector here). If a Performance Vector was also fed at the performance input port (we call it input-performance-vector here), criteria of the input-performance-vector are also added in the output-performance-vector. If the input-performance-vector and the calculated-performance-vector both have the same criteria but with different values, the values of calculated-performance-vector are delivered through the output port. This concept can be easily understood by studying the attached Example Process.
Parameters
- ranking costsTable defining the costs when the real label isn't the one with the highest confidence Range: list
Tutorial Processes
Applying the Performance (Ranking) operator on the Golf data set
The 'Golf' data set is loaded using the Retrieve operator. The Decision Tree operator is applied on it with default values for all parameters. The Tree model generated by the Decision Tree operator is applied on the 'Golf-Testset' data set using the Apply Model operator. Labeled data from the Apply Model operator is provided to the Performance (Ranking) operator. The ranking costs parameter is configured as described above. As result you can see the costs of the prediction made by the Apply Model operator.
