What if a model didn’t need any training? It could just remember every example it ever saw, and when a new case comes, ask: “Which of my memories are most like this one?” That’s the idea behind nearest-neighbor methods. In this chapter, we’ll see how this simple idea builds powerful classifiers, and how your choices about similarity, the number of neighbors, and how much weight each neighbor gets can make the method succeed or fail.