Much of the Solution Written by Afshin Rostami and Umar Syed
نویسندگان
چکیده
1. Implement AdaBoost with boosting stumps and apply the algorithm to the spambase data set of HW2 with the same training and test sets. Plot the average cross-validation error plus or minus one standard deviation as a function of the number of rounds of boosting T by selecting the value of this parameter out of {10, 102, . . . , 10k} for a suitable value of k, as in HW2. Let T ∗ be the best value found for the parameter. Plot the error on the training and test set as a function of the number of rounds of boosting for t ∈ [1, T ∗]. Compare your results with those obtained using SVMs in HW2.
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