How does the curse of dimensionality impact the K-Nearest Neighbors algorithm, and what are some ways to address this issue?
- Enhances speed, addressed by increasing data size
- Improves accuracy, addressed by adding more dimensions
- Makes distance measures less meaningful, addressed by dimension reduction
- Reduces accuracy, addressed by increasing K
The curse of dimensionality can make distance measures less meaningful in KNN, and this issue can be addressed through dimensionality reduction techniques like PCA.
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