Default Model (RapidMiner Studio Core)

Synopsis

This operator generates a model that provides the specified default value as prediction.

Description

The Default Model operator generates a model that predicts the specified default value for the label in all examples. The method to use for generating a default value can be selected through the method parameter. For a numeric label, the default value can be median or average of the label values or a constant default value can be specified through the constant parameter. For nominal values the mode of the labels can be used. Values of an attribute can be used as predictions; the attribute can be selected through the attribute parameter. This operator should not be used for 'actual' prediction tasks, but it can be used for comparing the results of 'actual' learning schemes with guessing.

Input

training set (Data Table)
This input port expects an ExampleSet. It is the output of the Retrieve operator in the attached Example Process. The output of other operators can also be used as input.

Output

model (Model)
The default model is delivered from this output port. This model can now be applied on unseen data sets for the prediction of the label attribute. This model should not be used for 'actual' prediction tasks, but it can be used for comparing the results of 'actual' learning schemes with guessing.
example set (Data Table)
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

methodThis parameter specifies the method for computing the default values. For a numeric label, the default value can be median or average of the label values or a constant default value can be specified through the constant parameter. For nominal values the mode of the labels can be used. Values of an attribute can be used as predictions; the attribute can be selected through the attribute parameter. Range: selection
constantThis parameter is only available when the method parameter is set to 'constant'. This parameter specifies a constant default value for a numeric label. Range: real
attributeThis parameter is only available when the method parameter is set to 'attribute'. This parameter specifies the attribute to get the predicted values from. If applied on a nominal label, it should be made sure that the selected attribute has the same set of possible values as the label. Range: string

Tutorial Processes

Using the Default Model operator with 'mode' method

The 'Sonar' data set is loaded using the Retrieve operator. A breakpoint is inserted here so that you can have a look at the ExampleSet. You can see that there are two possible label values i.e. 'Rock' and 'Mine'. The most frequently occurring label value is 'Mine'. The Split Validation operator is applied on this ExampleSet for training and testing a classification model. The Default Model operator is applied in the training subprocess of the Split Validation operator. The method parameter of the Default Model operator is set to 'mode', thus the most frequently occurring label value (i.e. 'Mine') will be used as prediction in all examples. The Apply Model operator is used in the testing subprocess for applying the model generated by the Default Model operator. A breakpoint is inserted here so that you can have a look at the labeled ExampleSet. You can see that all examples have been predicted as 'Mine'. This labeled ExampleSet is used by the Performance operator for measuring the performance of the model. The default model and its performance vector are connected to the output and they can be seen in the Results Workspace.