HomeQuestionsComputer ScienceNearest-Neighbor Un-embedding
📚 Learning Guide
Nearest-Neighbor Un-embedding
easy

Given a set of points representing different classes in a multi-class classification problem, which method would you use to classify a new point based on its proximity to the existing classes?

Master this concept with our detailed explanation and step-by-step learning approach

Learning Path
Learning Path

Question & Answer
1
Understand Question
2
Review Options
3
Learn Explanation
4
Explore Topic

Choose the Best Answer

A

Nearest-Neighbor Un-embedding

B

Linear Regression

C

Decision Trees

D

Support Vector Machines

Understanding the Answer

Let's break down why this is correct

Answer

To classify a new point you would use the nearest‑neighbor rule, which assigns the point to the class of the closest training point. First, calculate the distance—usually Euclidean—between the new point and every point in the training set. Then find the training point with the smallest distance and give the new point the label of that nearest neighbor. For example, if a new point is 0. 2 units from a red training point and 0.

Detailed Explanation

The method looks at the distance between the new point and all known points. Other options are incorrect because Linear regression predicts a numeric value, not a class label; Decision trees split data on feature thresholds, not on distance.

Key Concepts

Nearest-Neighbor Un-embedding
Multi-class Classification
Distance Metrics
Topic

Nearest-Neighbor Un-embedding

Difficulty

easy level question

Cognitive Level

understand

Ready to Master More Topics?

Join thousands of students using Seekh's interactive learning platform to excel in their studies with personalized practice and detailed explanations.

Start Learning with SeekhExplore More Nearest-Neighbor Un-embedding Questions