For instance, a data sample might be a natural language sentence, and the output could be an annotated parse tree.
Training a classifier consists of showing many instances of ground truth sample-output pairs.
After training, the SkNN model is able to predict the corresponding output for new, unseen sample instances; that is, given a natural language sentence, the classifier can produce the most likely parse tree.
SkNN is based on idea of creating a graph, with each node representing a class label.
There is an edge between a pair of nodes if there is a sequence of two elements in the training set with corresponding classes.