When to Use K-Nearest
- When the instances are points in Euclidean space.
- When there are few attributes (why?)
- When there is a lot of training data
- Advantages
- Training is very fast
- Learn complex target functions
- Don't lose information
- Disadvantages
- Slow at query time
- Easily fooled by irrelevant attributes
José M. Vidal
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