This 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.ĮxampleSet 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. 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. Tutorial Processes Applying the Performance (Ranking) operator on the Golf data set ranking costsTable defining the costs when the real label isn't the one with the highest confidence.This concept can be easily understood by studying the attached Example Process. 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.
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