How does the curse of dimensionality relate to feature selection?
- It can cause overfitting
- It can make visualizing data difficult
- It increases computational complexity
- It reduces the effectiveness of distance-based methods
The curse of dimensionality refers to the various problems that arise when dealing with high-dimensional data. In the context of feature selection, high dimensionality can reduce the effectiveness of distance-based methods, as distances in high-dimensional space become less meaningful.
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