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K-Nearest Neighbors Pro
Advanced KNN classifier with multiple distance metrics, interactive visualization, and comprehensive feature analysis
Configuration
Test Point
Must be between 1 and 9
Add Training Point
Results
Prediction
B
Confidence: 66.7%
3 Nearest Neighbors
#1: Label "A"
Point: (4.00, 5.00)
1.4142
distance
#2: Label "B"
Point: (6.00, 8.00)
2.2361
distance
#3: Label "B"
Point: (7.00, 8.00)
2.8284
distance
Vote Distribution
"A"
1/3
"B"
2/3
Training Data (9 points)
3 unique classes
| # | X | Y | Label | Action |
|---|---|---|---|---|
| 1 | 2.00 | 3.00 | A | |
| 2 | 3.00 | 3.00 | A | |
| 3 | 4.00 | 5.00 | A | |
| 4 | 6.00 | 8.00 | B | |
| 5 | 7.00 | 8.00 | B | |
| 6 | 8.00 | 9.00 | B | |
| 7 | 1.00 | 2.00 | C | |
| 8 | 2.00 | 1.00 | C | |
| 9 | 1.00 | 1.00 | C |
💡 Quick Tips
- • Adjust K to see how it affects the prediction
- • Try different distance metrics to understand their behavior
- • Add more training points to improve classification accuracy
- • K should typically be odd for binary classification to avoid ties
- • Larger K values make the decision boundary smoother
Related Topics & Algorithms
Explore these related algorithms and concepts to deepen your understanding and discover complementary techniques.
Classification
SVM
Margin-based classification alternative
Clustering
K-Means
Clustering cousin of KNN algorithm
Classification
Logistic Regression
Parametric classification approach
Classification
Decision Tree
Tree-based classification method
Preprocessing
Feature Engineering
Feature scaling critical for distance metrics
Dimensionality Reduction
PCA
Reduce dimensions for efficient KNN