Vote

Synopsis

This 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).

Description

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 theSubprocessoperator 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.

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 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 thelabelattribute.