c) = 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. 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?

Let's revisit our first problem, where we want to set up a series of chess matches so we can rank six players in our class. As we did before, we will assume that everyone keeps their chess rating a private secret; however, when two players have a chess match, the person with the higher rating wins 100% of the time. But this time, we are only interested in identifying the BEST of these six players and the WORST of these six players. (We don't care about the relative ordering or ranking of the middle four players.) Your goal is to devise a comparison-based algorithm that is guaranteed to identify the player with the highest rating and the player with the lowest rating. Because you are very strong at Algorithm Design, you know how to do this in the most efficient way. Here are five statements. A. There exists an algorithm to solve this problem using 6 matches, but there does not exist an algorithm using only 5 matches. B. There exists an algorithm to solve this problem using 7 matches,…

solve 6a,b,c Please include all the steps and reasonings and kindly make sure the answer is correct and properly formatted. Thank you

Classify the 1’s, 2’s, 3’s for the zip code data in R. (a) Use the k-nearest neighbor classification with k = 1, 3, 5, 7, 15. Report both the training and test errors for each choice. (b) Implement the LDA method and report its training and testing errors. Note: Before carrying out the LDA analysis, consider deleting variable 16 first from the data, since it takes constant values and may cause the singularity of the covariance matrix. In general, a constant variable does not have a discriminating power to separate two classes.