|  | ## Performance

#### Synopsis

This operator delivers a list of performance values automatically determined in order to fit the learning task type.

#### Description

In contrast to the other performance evaluation methods like for example Performance (Classification), Performance (Binominal Classification) or Performance (Regression), this operator can be used for all types of learning tasks. It will automatically determine the learning task type and will calculate the most common criteria for this type. For more sophisticated performance calculations, you should use the operators mentioned above. If none of them suits your need, you might write your own performance measure and calculate it with Performance (User-Based).

This operator expects a test ExampleSet as input, containing one attribute with the role label and one with the role prediction. See the Set Role operator for more details. On the basis of this two attributes a PerformanceVector is calculated, containing the values of the performance criteria. If a PerformanceVector was fed into performance input, it's values are kept if it does not already contain the new criteria. Otherwise the values are averaged over the old and the new values.

The following criterias are added for binominal classification tasks:

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

The following criterias are added for polynominal classification tasks:

- Accuracy
- Kappa statistic

The following criterias are added for regression tasks:

- Root Mean Squared Error
- Mean Squared Error

#### Input

- labelled data: expects: ExampleSet, expects: ExampleSetMetaData: #examples: = 0; #attributes: 0
- performance: optional: PerformanceVector

#### Output

- performance:
- example set:

#### Parameters

- use example weights: Indicated if example weights should be used for performance calculations if possible.

#### ExampleProcess