5 points In our lecture about AdaBoost algorithm, we introduced the definition of weighted error in each round t 3. i-1 where D(i) is the weight of i-th training example, and h() is the prediction of the weak classifier learned round t. Note that both y and h) belong to 1,-1). Prove that equivalently, ifht i) 5 points In our lecture about AdaBoost algorithm, we introduced the definition of weighted error in each round t 3. i-1 where D(i) is the weight of i-th training example, and h() is the prediction of the weak classifier learned round t. Note that both y and h) belong to 1,-1). Prove that equivalently, ifht i)