A KNNClassifier consists of a data matrix, associated labels in the same order as the matrix, and a distance function. The accepted distance functions at this time are 'euclidean' and 'manhattan'. Optimisations only occur when things are identically group into identical AttributeGroups, which don't include the class variable, in the same order.

KNNClassifier is referenced in 1 repository