KNN Visualizer

Explore the K-Nearest Neighbors algorithm with interactive animations and gamification features.

Enhanced ModeDecision BoundaryAnimate DistancesGame Mode

🧠 How KNN Works

K-Nearest Neighbors classifies points by finding the 3 closest neighbors and using majority voting. Watch the animated connectionsto see this process in action!

Click anywhere to test classification

Higher confidence = more certain prediction

Try different K values for comparison

Test boundary regions for insights

Controls

K Value: 3

115

Distance Metric

Use Weighted KNN

Show Confidence

Dataset

Two clearly separated classes

Click on the canvas to add test points.

Legend

Class A

Class B

Class C

Class D

Class E

Selected Neighbor

Distance Line

🎯Performance Dashboard

Total Score

Test Points

Avg Confidence

Achievements

🔒 First Step

Place your first test point

🔒 Explorer

Place 10 test points

🔒 Confident

Achieve 90% average confidence

🔒 Pattern Master

Test all datasets

💡 Tips for Higher Scores:

Test boundary regions between classes
Try different K values for the same point
Explore areas where classes overlap

Understanding the Visualization

Training points are shown as colored dots, representing different classes.
Test points appear when you click on the canvas. They are classified based on their K nearest neighbors.
Dashed lines connect test points to their nearest neighbors.
The K value determines how many neighbors influence the classification. A higher K is often more robust to noise but may miss important patterns.
Distance metrics affect how the algorithm measures similarity:
- Euclidean: Straight-line distance (" as the crow flies")
- Manhattan: Sum of absolute differences ("city block" distance)
Weighted KNN gives closer neighbors more influence in the classification, which can improve accuracy when distance is a strong indicator of similarity.
Confidence shows how certain the algorithm is about the classification (displayed as a percentage and partial ring around test points).