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Nearest-Neighbor Un-embedding
hard

In nearest-neighbor un-embedding, which factor is crucial for determining the effectiveness of classification?

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A

The method of vector embedding used

B

The signed distances to decision boundaries

C

The number of classes embedded

D

The dimensionality of the input space

Understanding the Answer

Let's break down why this is correct

Signed distances to decision boundaries show how close a point is to each class line. Other options are incorrect because The embedding method only creates the space; it does not decide which neighbor is chosen; The number of classes does not change how distances are measured.

Key Concepts

Nearest-neighbor un-embedding
Classification effectiveness
Decision boundaries
Topic

Nearest-Neighbor Un-embedding

Difficulty

hard level question

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understand

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Definition
Definition

Nearest-neighbor un-embedding involves embedding classes as vectors and determining the closest vector to a given prediction. It focuses on calculating signed distances to decision boundaries for effective classification.

Topic Definition

Nearest-neighbor un-embedding involves embedding classes as vectors and determining the closest vector to a given prediction. It focuses on calculating signed distances to decision boundaries for effective classification.

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