In nearest-neighbor un-embedding, the process of determining the closest vector to a given prediction relies on calculating ____ to decision boundaries for effective classification.

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Seekh · Accepted Answer

In nearest‑neighbor un‑embedding, you find the prediction vector that is closest to the input by measuring how far it is from the decision boundaries that separate different classes. The algorithm calculates the distance from the prediction to each of these boundaries, usually using Euclidean distance or another metric, and picks the vector whose distance is smallest. This step lets the model decide which class the prediction most likely belongs to, because the closest boundary tells you which side of the decision line the point falls on. For example, if a prediction lies 0. 3 units from the boundary between class A and B, but 0.

In nearest-neighbor un-embedding, the process of determining the closest vector to a given prediction relies on calculating ____ to decision boundaries for effective classification.

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