Create a decision tree with a data partition of 60% for training and 40% for validation. Set the Subtree assessment measure as "Misclassification" and run the tree to automatically generate the optimal tree. Answer the following questions based on the results of the generated tree.

1. How many leaves are in the optimal tree? Take a screenshot of the tree diagram showing all the nodes and paste it to the Word document.

2. What variables are included in the optimal tree? List them in the order of decreasing importance.

3. Identify the leaf with the highest predicted value for target value =1. For this node, write the decision rule. Do the same for the leaf with the highest predicted value for target value = 0.

4. Show the decision matrix (also called the classification table) in the format discussed in the class or SAS notes.

5.Compute the following from the decision matrix: a. Misclassification rate b. Positive predicted value c. Sensitivity d. Specificity.