Your training set is made up of the following 2-dimensional points: A: 0), (1,3), (3,0), (0,-2), (-2,0)}, where a', a², and a3 belong to class 1, and a“, a5 to Plot all samples into a 2-dimensional Cartesian axis system Calculate the Manhattan distance 1 between the test sample b=(0,C training set Use the K- Nearest Neighbor algorithm with K=1 to assign a class to Classify 'b' using K=5. Explain anhattan(a, b) = Ei la* – b'l, Vi = 1,2

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
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Your training set is made up of the following 2-dimensional points: A={a', a², a³, a², a³}
{(1,0), (1,3), (3,0), (0,-2), (-2,0)}, where a', a², and a³ belong to class 1, and at, a5 to class 2.
(a) Plot all samples into a 2-dimensional Cartesian axis system
Calculate the Manhattan distance 1 between the test sample b=(0,0) and every a of the
(b)
training set
(c)
Use the K- Nearest Neighbor algorithm with K=1 to assign a class to 'b'. Explain
(d)
Classify 'b' using K=5. Explain
'Manhattan(a, b) = E; la² – b' , Vi = 1,2
Transcribed Image Text:Your training set is made up of the following 2-dimensional points: A={a', a², a³, a², a³} {(1,0), (1,3), (3,0), (0,-2), (-2,0)}, where a', a², and a³ belong to class 1, and at, a5 to class 2. (a) Plot all samples into a 2-dimensional Cartesian axis system Calculate the Manhattan distance 1 between the test sample b=(0,0) and every a of the (b) training set (c) Use the K- Nearest Neighbor algorithm with K=1 to assign a class to 'b'. Explain (d) Classify 'b' using K=5. Explain 'Manhattan(a, b) = E; la² – b' , Vi = 1,2
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