Consider the training data given in Table 2 for classification, where the two classes of interest are -' and '+'. We want to apply binary decision trees as our chosen algorithm for classifying this data. Y=5 1 Y=4 III + Y=3 Ti + + + + Y=2 . + + Y=1 + X=1 X=2 X=3 X=4 X=5 Table 2 a. 16 points Find a decision tree which uses minimum number of splits (decision boundaries at internal nodes) to perfectly classify each training data instance of Table 2. Hint: The minimum number of splits that you need to create a perfect classifier is 6. You are not required to compute the Information Gain at each split for constructing the decision tree, but to arrive at your solution by visually inspecting the data. Consider the training data given in Table 2 for classification, where the two classes of interest are -' and '+'. We want to apply binary decision trees as our chosen algorithm for classifying this data. Y=5 1 Y=4 III + Y=3 Ti + + + + Y=2 . + + Y=1 + X=1 X=2 X=3 X=4 X=5 Table 2 a. 16 points Find a decision tree which uses minimum number of splits (decision boundaries at internal nodes) to perfectly classify each training data instance of Table 2. Hint: The minimum number of splits that you need to create a perfect classifier is 6. You are not required to compute the Information Gain at each split for constructing the decision tree, but to arrive at your solution by visually inspecting the data