Cloudera
DS-200 · Question #13
You are building a k-nearest neighbor classifier (k-NN) on a labeled set of points in a high- dimensional space. You determine that the classifier has a large error on the training data. What is the…
The correct answer is B. k-NN compotation does not coverage in high dimensions. See the full explanation below for the reasoning.
Question
You are building a k-nearest neighbor classifier (k-NN) on a labeled set of points in a high- dimensional space. You determine that the classifier has a large error on the training data. What is the most likely problem?
Options
- AHigh-dimensional spaces effectively make local neighborhoods global
- Bk-NN compotation does not coverage in high dimensions
- Ck was too small
- DThe VC-dimension of a k-NN classifier is too high
How the community answered
(69 responses)- A7% (5)
- B71% (49)
- C4% (3)
- D17% (12)
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