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Consider the following loss function L(x, y; C) for the binary classifier C, the input value x, and the output value y that outputs only 0 or 1. L(x, y; C) = LOI (y = 0, C(x) = 1) + L11 (y = 1, C(x) = 0) When C is a classifier that minimizes the expected risk function R(C) = E[L(X,Y;C)], C((x) = 1(P(Y = 1|X) > x)]. In this case, find c and explain why.

Consider the following loss function L(x, y; C) for the binary classifier C, the input value x, and the output value y that outputs only 0 or 1. L(x, y; C) = LOI (y = 0, C(x) = 1) + L11 (y = 1, C(x) = 0) When C is a classifier that minimizes the expected risk function R(C) = E[L(X,Y;C)], C((x) = 1(P(Y = 1|X) > x)]. In this case, find c and explain why.

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 the following loss function L(x, y; C) for the binary classifier C, the input value x, and the output value y that outputs only 0 or 1. L(x,y; C) = LOI (y = 0, C(x) = 1) + L11 (y = 1, C(x) = 0) When C is a classifier that minimizes the expected risk function R(C) = E[L(X,Y;C)], C((x) = I(P(Y=1|X) > x)]. In this case, find c and explain why.
Transcribed Image Text:Consider the following loss function L(x, y; C) for the binary classifier C, the input value x, and the output value y that outputs only 0 or 1. L(x,y; C) = LOI (y = 0, C(x) = 1) + L11 (y = 1, C(x) = 0) When C is a classifier that minimizes the expected risk function R(C) = E[L(X,Y;C)], C((x) = I(P(Y=1|X) > x)]. In this case, find c and explain why.
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