4. Following is a data set that contains two attributes, X and Y, and two class labels, "+ and "-". Each attribute can take three different values: 0, 1, or 2. The concept for the+ class is Y = 1 and the concept for the "-" class is X = 0 VX = 2 (a) Build a decision tree on the data set. Does the tree capture and the concepts? (b) What are the accuracy, precision, recall, and Fi-measure of the decision tree? (Note that precision, recall, and Fi-measure are defined with respect to the+ class.) (c) Build a new decision tree with the following cost function: - Number of-instance Number of- instance (Hint: only the leaves of the old decision tree need to be changed.) Does the decision tree captures the"+"concept? (d) What are the accuracy) precision, recall, and Fi-measure of the new decision tree? 4. Following is a data set that contains two attributes, X and Y, and two class labels, "+ and "-". Each attribute can take three different values: 0, 1, or 2. The concept for the+ class is Y = 1 and the concept for the "-" class is X = 0 VX = 2 (a) Build a decision tree on the data set. Does the tree capture and the concepts? (b) What are the accuracy, precision, recall, and Fi-measure of the decision tree? (Note that precision, recall, and Fi-measure are defined with respect to the+ class.) (c) Build a new decision tree with the following cost function: - Number of-instance Number of- instance (Hint: only the leaves of the old decision tree need to be changed.) Does the decision tree captures the"+"concept? (d) What are the accuracy) precision, recall, and Fi-measure of the new decision tree