k-NN: Learning by Looking Around

k-nearest neighbors doesn't learn anything at training time — it just memorizes the dataset. To classify a new point, it finds the k closest training examples and takes a majority vote.

The decision boundary isn't drawn anywhere; it emerges from the neighborhoods. k = 1 gives jagged borders that trace every training point. Larger k smooths the vote into calmer shapes.