KNN Curse of Dimensionality

Hard

machine-learning Microsoft Google Citadel

Your KNN classifier works well on a 10-feature dataset but degrades significantly when you expand to 500 features. The training set size remains the same. What is the most likely explanation?

Multiple Choice

KNN can only handle up to 100 features due to algorithmic limitations In high-dimensional spaces, distances become less meaningful and all points appear similarly far apart KNN requires feature scaling, which becomes impossible with 500 features The algorithm runs out of memory with high-dimensional data

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KNN Curse of Dimensionality

Multiple Choice

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