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Suppose you are running gradient descent to fit a logistic regression model with e E R+1, Which of the following is a reasonable way to make sure the learning rate c is set properly and that gradient descent is running correctly? Plot J(0) as a function of 0 and make sure it is convex. O b. Plot J(8) as a function of 0 and make sure it is decreasing on every iteration. O* Plot /(0) = -E [y®logho(x") + (1 – y®) log (1 – ħø(x"))] a a function of the number of iterations and make sure J(0) is decreasing on every iteration. O d. Plot (8) = (he(x®) – y®)² as a function of the number of iterations (i.e. the horizontal axis is the iteration number) and make sure J(0) is decreasing on every iteration.

Suppose you are running gradient descent to fit a logistic regression model with e E R+1, Which of the following is a reasonable way to make sure the learning rate c is set properly and that gradient descent is running correctly? Plot J(0) as a function of 0 and make sure it is convex. O b. Plot J(8) as a function of 0 and make sure it is decreasing on every iteration. O* Plot /(0) = -E [y®logho(x") + (1 – y®) log (1 – ħø(x"))] a a function of the number of iterations and make sure J(0) is decreasing on every iteration. O d. Plot (8) = (he(x®) – y®)² as a function of the number of iterations (i.e. the horizontal axis is the iteration number) and make sure J(0) is decreasing on every iteration.

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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Suppose you are running gradient descent to fit a logistic regression model with 0 E R+1, Which of the following is a reasonable way to make sure the learning rate c is set properly and that gradient descent is running correctly? Plot J(0) as a function of 0 and make sure it is convex. O b. Plot J(8) as a function of 0 and make sure it is decreasing on every iteration. O* Plot /(0) = -E [y®logho(x") + (1 – y®) log (1 – ħø(x"))] a a function of the number of iterations and make sure J(0) is decreasing on every iteration. O d. Plot J(8) =E(h,(x®) – y®)² as a function of the number of iterations (i.e. the horizontal axis is the iteration number) and make sure J(8) is decreasing on every iteration.
Transcribed Image Text:Suppose you are running gradient descent to fit a logistic regression model with 0 E R+1, Which of the following is a reasonable way to make sure the learning rate c is set properly and that gradient descent is running correctly? Plot J(0) as a function of 0 and make sure it is convex. O b. Plot J(8) as a function of 0 and make sure it is decreasing on every iteration. O* Plot /(0) = -E [y®logho(x") + (1 – y®) log (1 – ħø(x"))] a a function of the number of iterations and make sure J(0) is decreasing on every iteration. O d. Plot J(8) =E(h,(x®) – y®)² as a function of the number of iterations (i.e. the horizontal axis is the iteration number) and make sure J(8) is decreasing on every iteration.
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