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Nearest-neighbor un-embedding can be used to improve classification accuracy by utilizing distance metrics.
The signed distances calculated in nearest-neighbor un-embedding represent the probability of class membership.
Nearest-neighbor un-embedding relies solely on Euclidean distance to determine the closest class vector.
It is essential to normalize the class vectors before applying nearest-neighbor un-embedding for accurate results.
Nearest-neighbor un-embedding is only applicable in binary classification scenarios.
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Nearest-Neighbor Un-embedding
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