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Vote (RapidMiner Studio Core)
SynopsisThis operator uses a majority vote (for classification) or the average (for regression) on top of the predictions of the inner learners (i.e. learning operators in its subprocess).
The Vote operator is a nested operator i.e. it has a subprocess. The subprocess must have at least two learners, called base learners. This operator builds a classification model or regression model depending upon the ExampleSet and learners. This operator uses a majority vote (for classification) or the average (for regression) on top of the predictions of the base learners 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. All the operator chains in the subprocess must accept an ExampleSet and return a model.
In case of a classification task, all the operators in the subprocess of the Vote operator accept the given ExampleSet and generate a classification model. For prediction of an unknown example, the Vote operator applies all the classification models from its subprocess and assigns the predicted class with maximum votes to the unknown example. Similarly, In case of a regression task, all the operators in the subprocess of the Vote operator accept the given ExampleSet and generate a regression model. For prediction of an unknown example, the Vote operator applies all the regression models from its subprocess and assigns the average of all predicted values to the unknown example.
- training set (IOObject)
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 (Majority Vote Model)
The simple vote model for classification or regression is delivered from this output port. This model can now be applied on unseen data sets for prediction of the label attribute.
Using the Vote 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 model. The Vote operator is applied in the training subprocess of the Split Validation operator. Three learners are applied in the subprocess of the Vote operator. These base learners are: Decision Tree, Neural Net and SVM. The Vote operator uses the vote of each learner for classification of an example, the prediction with maximum votes is assigned to the unknown example. In other words it uses the predictions of the three base learners to make a combined prediction (using simple voting). The Apply Model operator is used in the testing subprocess of the Split Validation operator for applying the model generated by the Vote operator. The resultant labeled ExampleSet is used by the Performance operator for measuring the performance of the model. The Vote model and its performance vector is connected to the output and it can be seen in the Results Workspace.