Compute a two-level decision tree using the greedy approach described in this chapter. Use the classification error rate as the criterion for splitting. What is the overall error rate of the induced tree?
Consider the following set of training examples.
Splitting Attribute at Level 1.
To determine the test condition at the root node, we need to com-
pute the error rates for attributes X, Y , and Z. For attribute X, the
corresponding counts are:
Therefore, the error rate using attribute X is (60 + 40)/200 = 0.5.
For attribute Y , the corresponding counts are:
Therefore, the error rate using attribute Y is (30 + 30)/200 = 0.3.
Therefore, the error rate using attribute Y is (40 + 40)/200 = 0.4.
For attribute Z, the corresponding counts are:
Therefore, the error rate using attribute Y is (30 + 30)/200 = 0.3.
Since Z gives the lowest error rate, it is chosen as the splitting attribute
at level 1.
Computer Science & Information Technology
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