Terminology

• sample: a piece of data to be classified by the model, or a labelled piece of training/test/validation data.
• classes: the set of possible class labels which the model attempts to predict.
• voters: the individual sources of labelled data, such as human labellers. Each voter may supply a "vote" for a class label for a sample.
• votes: the matrix of votes corresponding to a set of data, whose rows correspond to the index of a sample in a set of data, whose columns correspond to voters, and whose values are the indices of class labels (i.e. numbers in 1:length(classes)). E.g. if 2 voters have voted on ten samples, then votes is a 10 by 2 matrix of integers. If a voter has not voted on a particular sample, any value outside 1:length(classes) may be supplied to indicate this.
• elected class: the class elected by the voters. By default in learn!, the elected class is chosen by a simple majority of the votes with ties broken randomly.
• predicted class: the class predicted by the model for a given input.