K Nearest Neigbour - Practical Use

Practical Considerations

1. Always scale features before using KNN
2. Start with k=5 and tune via cross-validation
3. Use odd k for binary classification to avoid ties
4. Try distance weighting for better performance
5. Consider computational cost for large datasets
6. Remove irrelevant features to avoid curse of dimensionality
7. Handle class imbalance with weighted voting or resampling

When to Use

Use KNN when:

• Small to medium-sized datasets
• Few features (low dimensionality)
• Non-linear decision boundaries
• Need simple baseline model
• Real-time training updates needed

Avoid KNN when:

• Very large datasets (slow predictions)
• High-dimensional data (>20 features)
• Real-time predictions critical
• Many irrelevant features
• Memory is constrained