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Performance

Synopsis​

This operator is used for performance evaluation. It delivers a list of performance criteria values. These performance criteria are automatically determined in order to fit the learning task type.

Description​

In contrast to the other performance evaluation operators like the Performance (Classification) operator, the Performance (Binominal Classification) operator or the Performance (Regression) operator, this operator can be used for all types of learning tasks. It automatically determines the learning task type and calculates the most common criteria for that type. For more sophisticated performance calculations, you should use the operators mentioned above. If none of them meets your requirements, you can use Performance (User-Based) operator which allows you to write your own performance measure.

The following criteria are added for binominal classification tasks:

  • Accuracy
  • Precision
  • Recall
  • AUC (optimistic)
  • AUC (neutral)
  • AUC (pessimistic)

The following criteria are added for polynominal classification tasks:

  • Accuracy
  • Kappa statistic

The following criteria are added for regression tasks:

  • Root Mean Squared Error
  • Mean Squared Error

Input​

labelled data​

This input port expects a labelled ExampleSet. The Apply Model operator for example provides labeled data. Make sure that the ExampleSet has a label attribute and a prediction attribute. See the Set Role operator for more details.

performance​

This is an optional parameter. It requires a Performance Vector.

Output​

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 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 input port (we call it input-performance-vector here), the 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 the calculated-performance-vector are delivered through the output port. This concept can be easily understood by studying the attached Example Process.

example set​

The ExampleSet that was given as input is passed without changing 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​

Use example weights​

This parameter allows example weights to be used for performance calculations if possible. This parameter has no effect if no attribute has weight role. In order to consider weights of examples the ExampleSet should have an attribute with weight role. Several operators are available that assign weights e.g. the Generate Weights operator. Please study the Set Roles operator for more information regarding weight roles.