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Consider a classroom with n number of students. The students are divided into three groups (A, B, and C) based on their height and weight, separately. Each student's information is also associated with a prediction that whether the student is selected for NCC or not. You need to use Naïve Bayes classifier. The likelihood table is given in the table below. You need to find the posterior probability for data point (A,C), i.e., P(NY|H = A,W = C) and P(NN|H = A,W = C). Notation- NY: Selected for NCC, NN: Not selected for NCC. %3D %3D NY NN NY NN A 0.2 Height B A 0.3 A 0,4 0.1 Weight B0.5 C 0.1 0.4 0.6 0.3 C 0.4 0.1 0.6

Consider a classroom with n number of students. The students are divided into three groups (A, B, and C) based on their height and weight, separately. Each student's information is also associated with a prediction that whether the student is selected for NCC or not. You need to use Naïve Bayes classifier. The likelihood table is given in the table below. You need to find the posterior probability for data point (A,C), i.e., P(NY|H = A,W = C) and P(NN|H = A,W = C). Notation- NY: Selected for NCC, NN: Not selected for NCC. %3D %3D NY NN NY NN A 0.2 Height B A 0.3 A 0,4 0.1 Weight B0.5 C 0.1 0.4 0.6 0.3 C 0.4 0.1 0.6

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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• Consider a classroom with n number of students. The students are divided into three groups (A, B, and C) based on their height and weight, separately. Each student's information is also associated with a prediction that whether the student is selected for NCC or not. You need to use Naïve Bayes classifier. The likelihood table is given in the table below. You need to find the posterior probability for data point (A,C), i.e., P(NY|H = A, W = C) and P(NN|H = A, W = C). Notation- NY: Selected for NCC, NN: Not selected for NCC. NY NN A 0.2 Height B NY NN A 0.4 0.3 0.1 Weight B0.5 C 0.1 0.4 0.6 0.3 C 0.4 0.1 0.6
Transcribed Image Text:• Consider a classroom with n number of students. The students are divided into three groups (A, B, and C) based on their height and weight, separately. Each student's information is also associated with a prediction that whether the student is selected for NCC or not. You need to use Naïve Bayes classifier. The likelihood table is given in the table below. You need to find the posterior probability for data point (A,C), i.e., P(NY|H = A, W = C) and P(NN|H = A, W = C). Notation- NY: Selected for NCC, NN: Not selected for NCC. NY NN A 0.2 Height B NY NN A 0.4 0.3 0.1 Weight B0.5 C 0.1 0.4 0.6 0.3 C 0.4 0.1 0.6
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