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Nearest-Neighbor Un-embedding
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A data scientist is developing a model to classify images of animals into different categories: cats, dogs, and birds. They decide to use nearest-neighbor un-embedding to improve the accuracy of their classifications. If they have embedded the classes as vectors, what should they primarily focus on to effectively classify a new image?

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A

Calculating the signed distances from the new image vector to each class vector

B

Using the average color of the images to classify

C

Counting the number of pixels in each category

D

Randomly selecting a class for the new image

Understanding the Answer

Let's break down why this is correct

The goal is to find the class whose vector is closest to the new image vector. Other options are incorrect because Using only the average color ignores the vector representation of the classes; Counting pixels does not measure similarity in the vector space.

Key Concepts

Nearest-neighbor un-embedding
Multi-class classification
Vector representation
Topic

Nearest-Neighbor Un-embedding

Difficulty

easy level question

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understand

Deep Dive: Nearest-Neighbor Un-embedding

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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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