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2. Decision tree classification: Consider the following instances. | Instance | a1 | a2 | a3 | Target Class | | :--- | :--- | :--- | :--- | :--- | | 1 | T | T | 1.0 | + | | 2 | T | T | 6.0 | + | | 3 | T | F | 5.0 | - | | 4 | F | F | 4.0 | + | | 5 | F | T | 7.0 | - | | 6 | F | T | 3.0 | - | | 7 | F | F | 8.0 | - | | 8 | T | F | 7.0 | + | | 9 | F | T | 5.0 | - | a. Consider the case of no branching. Which class will you predict? What are the misclassification error and the Gini index? b. Consider the cases of branching using attributes a1, a2, and a3. For each of the three cases, show the contingency table, the gains in misclassification error rate, and the gains in Gini index. (Hint: for a3, use the split of a3 < 5 and a3 ? 5.) c. According to the misclassification error rate, which attribute would be chosen as the first splitting attribute? d. According to the Gini index, which attribute would be chosen as the first splitting attribute? e. What are the precision and recall when splitting using attribute a3 with a3 < 5 and a3 ? 5.

          2. Decision tree classification: Consider the following instances.

| Instance | a1 | a2 | a3 | Target Class |
| :--- | :--- | :--- | :--- | :--- |
| 1 | T | T | 1.0 | + |
| 2 | T | T | 6.0 | + |
| 3 | T | F | 5.0 | - |
| 4 | F | F | 4.0 | + |
| 5 | F | T | 7.0 | - |
| 6 | F | T | 3.0 | - |
| 7 | F | F | 8.0 | - |
| 8 | T | F | 7.0 | + |
| 9 | F | T | 5.0 | - |

a. Consider the case of no branching. Which class will you predict? What are the misclassification error and the Gini index?
b. Consider the cases of branching using attributes a1, a2, and a3. For each of the three cases, show the contingency table, the gains in misclassification error rate, and the gains in Gini index. (Hint: for a3, use the split of a3 < 5 and a3 ? 5.)
c. According to the misclassification error rate, which attribute would be chosen as the first splitting attribute?
d. According to the Gini index, which attribute would be chosen as the first splitting attribute?
e. What are the precision and recall when splitting using attribute a3 with a3 < 5 and a3 ? 5.

2. Decision tree classification: Consider the following instances.

| Instance | a1 | a2 | a3 | Target Class |
| :— | :— | :— | :— | :— |
| 1 | T | T | 1.0 | + |
| 2 | T | T | 6.0 | + |
| 3 | T | F | 5.0 | - |
| 4 | F | F | 4.0 | + |
| 5 | F | T | 7.0 | - |
| 6 | F | T | 3.0 | - |
| 7 | F | F | 8.0 | - |
| 8 | T | F | 7.0 | + |
| 9 | F | T | 5.0 | - |

a. Consider the case of no branching. Which class will you predict? What are the misclassification error and the Gini index?
b. Consider the cases of branching using attributes a1, a2, and a3. For each of the three cases, show the contingency table, the gains in misclassification error rate, and the gains in Gini index. (Hint: for a3, use the split of a3 < 5 and a3 ? 5.)
c. According to the misclassification error rate, which attribute would be chosen as the first splitting attribute?
d. According to the Gini index, which attribute would be chosen as the first splitting attribute?
e. What are the precision and recall when splitting using attribute a3 with a3 < 5 and a3 ? 5.

Added by Regina A.

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