Question 6 [20 marks] You have collected three datasets with two dimensional points for binary classification. Your ultimate goal is to classify the point a* = (x1, x2) = (0, 2.5). You are provided with the following datasets; A, B, and C. . For dataset A: The positive observations are (2, 2), (2, 3), and (3, 2). The negative observations are (-1,1), (0,0), (1, -1), and (-1,0). . Dataset B contains all the data from dataset A, but has one extra positive observation at (2, -2). For each of the above datasets, answer the following questions. 1. Are the problems linearly separable in the original input space? You can use scatter plots to show these. 2. If the problem is not linearly separable, provide an example of simple feature transformation o such that the resulting problem is linearly separable with the lowest number of features possible. Otherwise, use the two dimensional feature o(21, x2) = (21, 22). 3. Write down the equation for the decision boundaries in the original input space. 4. Write down the coordinates of all support vector(s) for both classes based on your choice for the feature. 5. Does a* belong to the positive or negative class