How to classify a new instance (f1, f2) =(3,1), when k = 1 k Nearest Neighbors×××××××X₁X₂X3X4X5X6X7Attribute f₁1356276=Attribute f₂2344121ClassalaUUU~~EaabbbM

K-Nearest Neighbors (KNN) is a simple, machine learning algorithm used for both classification and regression tasks.We need to find the single nearest neighbor to the new instance (f1, f2) = (3, 1).Euclidean Distance formula: New Instance: (f1, f2) = (3, 1)Distances to New Instance:- Distance to x1: sqrt((1 - 3)^2 + (2 - 1)^2) = 2.236- Distance to x2: sqrt((3 - 3)^2 + (3 - 1)^2) = 2.0- Distance to x3: sqrt((5 - 3)^2 + (4 - 1)^2) = 3.606- Distance to x4: sqrt((6 - 3)^2 + (4 - 1)^2) = 4.243- Distance to x5: sqrt((2 - 3)^2 + (1 - 1)^2) = 1.0- Distance to x6: sqrt((7 - 3)^2 + (2 - 1)^2) = 4.123- Distance to x7: sqrt((6 - 3)^2 + (1 - 1)^2) = 3.0- Nearest Neighbor: x5- Class of Nearest Neighbor: b- Distance: 1.0Hence, the new instance (3, 1) is classified as class 'b' based on its nearest neighbor x5.

Answered: k Nearest Neighbors X₁ X₂ X3 X₁ X5 X6…

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

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How to classify a new instance (f1, f2) =(3,1), when k = 1

k Nearest Neighbors
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Attribute f₂
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4
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Class
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