Classification by Regression
Synopsis
This operator builds a polynominal classification model through the given regression learner.
Description
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
Here is an explanation of how a classification model is built from a regression learner. For each classiof the given ExampleSet, a regression model is trained after setting the label to +1 if the label isiand 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.
Input
training set
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
The classification model is delivered from this output port. This classification model can now be applied on unseen data sets for prediction of thelabelattribute.
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.