Classification Space
Class 0
Class 1
Query Point
Nearest Neighbors Detail
d(x, x') = √Σ(xᵢ - x'ᵢ)²
Euclidean distance
Decision Boundary vs K
Distance Distribution
Add Data Points
Click on the classification canvas to add points
K Parameter
3
Neighbors
Small K → complex boundary, Large K → smooth boundary
Distance Weighting
vote = 1
All neighbors vote equally
Model Statistics
Accuracy
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Total Points
0
Class 0
0
Class 1
0
Hover to probe a point
Query:
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Prediction:
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Votes (C0:C1):
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Quick Examples
How it works: K-NN classifies a point by finding its K nearest neighbors in the training data and taking a majority vote. It's a "lazy learner" — no training phase, all computation happens at prediction time.