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= .7, then Notice that Roi (h) can be interpreted as the misclassification rate. That is, if Ro1(h) : predicting h would result in the wrong answer for 70% of the data points. Given the data set {4, 2, 4, 1, 3, 4, 4, 3, 2, 5}, plot the empirical risk Ro1 (h) for h = [0, 5]. Hint: the function should have point discontinuities. c) Is gradient descent useful for minimizing the risk with zero-one loss? Why or why not? Make reference to your plot of the risk in your answer. Hint: the risk is indeed non-convex, but gradient descent can still be useful for minimizing non-convex functions. Is there some other reason?

= .7, then Notice that Roi (h) can be interpreted as the misclassification rate. That is, if Ro1(h) : predicting h would result in the wrong answer for 70% of the data points. Given the data set {4, 2, 4, 1, 3, 4, 4, 3, 2, 5}, plot the empirical risk Ro1 (h) for h = [0, 5]. Hint: the function should have point discontinuities. c) Is gradient descent useful for minimizing the risk with zero-one loss? Why or why not? Make reference to your plot of the risk in your answer. Hint: the risk is indeed non-convex, but gradient descent can still be useful for minimizing non-convex functions. Is there some other reason?

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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Notice that Ro1(h) can be interpreted as the misclassification rate. That is, if R01(h) .7, then predicting h would result in the wrong answer for 70% of the data points. Given the data set {4, 2, 4, 1, 3, 4, 4, 3, 2, 5}, plot the empirical risk Ro1(h) for h = [0, 5]. Hint: the function should have point discontinuities. = c) Is gradient descent useful for minimizing the risk with zero-one loss? Why or why not? Make reference to your plot of the risk in your answer. Hint: the risk is indeed non-convex, but gradient descent can still be useful for minimizing non-convex functions. Is there some other reason?
Transcribed Image Text:Notice that Ro1(h) can be interpreted as the misclassification rate. That is, if R01(h) .7, then predicting h would result in the wrong answer for 70% of the data points. Given the data set {4, 2, 4, 1, 3, 4, 4, 3, 2, 5}, plot the empirical risk Ro1(h) for h = [0, 5]. Hint: the function should have point discontinuities. = c) Is gradient descent useful for minimizing the risk with zero-one loss? Why or why not? Make reference to your plot of the risk in your answer. Hint: the risk is indeed non-convex, but gradient descent can still be useful for minimizing non-convex functions. Is there some other reason?
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