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?
GitHub
see attachment for details DAT 260 Module Eight Journal Guidelines and Rubric Overview As you have learned in the module resources, GitHub is more than just a place to store code. It is a dynamic community of practice and a living portfolio of your coding skills. In this journal entry,