Confusion matrix

Part A: (2.5 Marks)

  1. Use the training data in Table 1 to construct a confusion matrix for the decision tree shown in Figure 1 (Class Label = Life Insurance Promotion).
  2. Write the production rules for the decision tree shown in Figure 2.
  3. Can you simplify any of your rules without compromising rule accuracy (tree pruning)?

Table 1

Figure 1

Figure 2

Part B: (2.5 Marks)

Given the training set D in Table 1, apply the decision tree induction algorithm for classification to construct the decision tree to predict the class label (Life Insurance Promotion (yes/no)). Explain the steps you have taken to construct the decision tree in detail.

Part C (2.5 Marks)

Describe each of the following clustering algorithms in terms of the following criteria: (1) shapes of cluster that can be determined; (2) input parameters that must be specified; and (3) limitations and advantages.

  1. K-means
  2. K-medoids
  3. Agglomerative Clustering

Sample Solution