We propose to use the diagonal line with slope +1 and intercept +2 as a decision boundary, with positive examples above and negative examples below. However like any linear boundary for this training set, some examples are misclassified. We can measure the goodness of the boundary by computing all the slack variables that exceed 0, and then using them in one of several objective functions. In this problem, we shall only concern ourselves with computing the slack variables, not an objective function. To be specific, suppose the boundary is written in the form wrbe0, where w .x + b = 0, where w = (-1, 1) and b = -2. Note that we can scale the three numbers involved as we wish, and so doing changes the margin around the boundary. However, we want to consider this specific boundary and margin. Determine the slack for each of the 16 points. Then identify the correct statement in the list below: a) The slack for (3, 6) is 2. b) The slack for (7, 8) is 2. c) The slack for (5, 4) is 2. d) The slack for (3, 4) is 0. We propose to use the diagonal line with slope +1 and intercept +2 as a decision boundary, with positive examples above and negative examples below. However like any linear boundary for this training set, some examples are misclassified. We can measure the goodness of the boundary by computing all the slack variables that exceed 0, and then using them in one of several objective functions. In this problem, we shall only concern ourselves with computing the slack variables, not an objective function. To be specific, suppose the boundary is written in the form wrbe0, where w .x + b = 0, where w = (-1, 1) and b = -2. Note that we can scale the three numbers involved as we wish, and so doing changes the margin around the boundary. However, we want to consider this specific boundary and margin. Determine the slack for each of the 16 points. Then identify the correct statement in the list below: a) The slack for (3, 6) is 2. b) The slack for (7, 8) is 2. c) The slack for (5, 4) is 2. d) The slack for (3, 4) is 0