Do I need to install anything?

No. It runs in any modern browser, free, with no account required.

For Educators · k-Nearest Neighbors

k-Nearest Neighbors for a hash table

Built for educators teaching it to a class. Drop a live demo into a lecture or assign it as a shareable link — no lab installs. Simulate a hash table live below — adjust the inputs and watch it respond, right in your browser.

k-Nearest NeighborsLive

classify by your neighbors

Controls

Presets

Neighbors k

k-NN classifies a point by majority vote of its k nearest labeled neighbors. Small k gives jagged, overfit boundaries that chase noise; large k smooths them out but can blur real structure. It is the simplest possible classifier — no training at all. Educational tool.

▶ Run in Python

Data Inspector

Neighbors5

Boundarybalanced

Governing equation

\overset{y}{^} = mode {y_{i} : x_{i} \in N_{5} (x)}, d (x, x_{i}) = ∥ x - x_{i} ∥

Reading this result: k = 5 sits in the sweet spot: enough neighbors to shrug off noise, few enough to keep the true boundary shape.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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Frequently asked questions

Is this good for educators?: Yes — this version of "k-Nearest Neighbors for a hash table" is framed for educators teaching it to a class. Drop a live demo into a lecture or assign it as a shareable link — no lab installs.
Do I need to install anything?: No. It runs in any modern browser, free, with no account required.