+ ilu + + + In this problem, you are going to run the AdaBoost algorithm for 2 iterations. You will work on 9 data points belonging to two-dimensional space. The data points along with their labels are reported in the following table: No. X-coordinate Y-coordinate Label 1 1.5 3.5 2 1.5 5.5 3 2.5 4.5 4 3.5 5.5 5 2.5 2.5 6 2.5 6.5 7 4.5 3.5 8 4.5 4.5 9 5.5 2.5 For each iteration, the weak classifier is a decision stump defined as follows: Iteration 1: x>=3 predict - otherwise + Iteration 2: y>=4 predict + otherwise - a In each iteration, you have to compute the following: The weighted error The classification coefficient The updated weights for each sample After running the AdaBoost for 2 iterations, predict the label for test sample (x=3.5, y=3.5) + ilu + + + In this problem, you are going to run the AdaBoost algorithm for 2 iterations. You will work on 9 data points belonging to two-dimensional space. The data points along with their labels are reported in the following table: No. X-coordinate Y-coordinate Label 1 1.5 3.5 2 1.5 5.5 3 2.5 4.5 4 3.5 5.5 5 2.5 2.5 6 2.5 6.5 7 4.5 3.5 8 4.5 4.5 9 5.5 2.5 For each iteration, the weak classifier is a decision stump defined as follows: Iteration 1: x>=3 predict - otherwise + Iteration 2: y>=4 predict + otherwise - a In each iteration, you have to compute the following: The weighted error The classification coefficient The updated weights for each sample After running the AdaBoost for 2 iterations, predict the label for test sample (x=3.5, y=3.5)