Consider a binary classification problem with an ensemble learning algorithm that uses simple majority voting among K learned hypotheses. Suppose that each hypothesis has error E and that the errors made by each hypothesis are independent of the others. Calculate a formula for the error of the ensemble algorithm in terms of K and E, and evaluate it for the cases where K =5, 11, and 21 and E=0.1, 0.2, and 0.4. If the independence assumption is removed, is it possible for the ensemble error to be worse than E?
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2. Final Assignment – equivalent to 4,000 words The final module mark is based on two deliverables focused on the CarNow case study described below. – 50% of the final mark a. An advisory report – 50 % of the final mark Includes 5% (of the module grade) given for