Classification by Regression (RapidMiner Studio Core)


This operator builds a polynominal classification model through the given regression learner.


The Classification by Regression operator is a nested operator i.e. it has a subprocess. The subprocess must have a regression learner i.e. an operator that generates a regression model. This operator builds a classification model using the regression learner provided in its subprocess. You need to have a basic understanding of subprocesses in order to apply this operator. Please study the documentation of the Subprocess operator for basic understanding of subprocesses.

Here is an explanation of how a classification model is built from a regression learner. For each class i of the given ExampleSet, a regression model is trained after setting the label to +1 if the label is i and to -1 if it is not. Then the regression models are combined into a classification model. This model can be applied using the Apply Model operator. In order to determine the prediction for an unlabeled example, all regression models are applied and the class belonging to the regression model which predicts the greatest value is chosen.


  • 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.


  • model (Model)

    The classification model is delivered from this output port. This classification model can now be applied on unseen data sets for prediction of the label attribute.

  • 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.

Tutorial Processes

Using the Linear Regression operator for classification

The 'Sonar' data set is loaded using the Retrieve operator. The Split Validation operator is applied on it for training and testing a classification model. The Classification by Regression operator is applied in the training subprocess of the Split Validation operator. The Linear Regression operator is applied in the subprocess of the Classification by Regression operator. Although Linear Regression is a regression learner but it will be used by the Classification by Regression operator to train a classification model. The Apply Model operator is used in the testing subprocess to apply the model. The resultant labeled ExampleSet is used by the Performance (Classification) operator for measuring the performance of the model. The classification model and its performance vector is connected to the output and it can be seen in the Results Workspace.