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For Students · k-Nearest Neighbors

k-Nearest Neighbors for a Q-learning agent

Built for students learning it for a class or exam. See the concept move instead of memorizing formulas — and check your homework intuition. Simulate a Q-learning agent live below — adjust the inputs and watch it respond, right in your browser.

k-Nearest NeighborsLive

classify by your neighbors

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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.

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Frequently asked questions

Is this good for students?: Yes — this version of "k-Nearest Neighbors for a Q-learning agent" is framed for students learning it for a class or exam. See the concept move instead of memorizing formulas — and check your homework intuition.
Do I need to install anything?: No. It runs in any modern browser, free, with no account required.