k-Nearest Neighbours
Interactive lab
Try it: k-Nearest Neighbours
How a k-nearest-neighbours classifier labels a new point: measure its distance to every known point, take the K closest, and let them vote.
How it works
- Measure the straight-line (Euclidean) distance from the new point to every labelled point.
- Rank the points from nearest to farthest.
- Keep the K nearest.
- Count how many of them belong to each class; the class with the most votes is the prediction (a tie goes to the class of the nearest tied neighbour).
Default run (15 steps): 10 labelled points and a new query point at (5, 3.5). K = 3. … Prediction: class A — A has the most votes.
Simplified: Small 2-D educational dataset (at most 30 points, three classes). Real KNN uses many features and usually scales them first.
Educational simulation
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