3. Consider the decision tree shown in Figure 2a, and the corresponding training and test sets shown in the table below. The decision tree in Figure 2b is a pruned version of the original decision tree. 2 2 B 1 Figure 2a Figure 2b

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Need help with question a, b.  (Data Mining ~decision trees, generalization error, error rate)

### Decision Trees and Generalization Error Rate

#### Decision Trees Overview

The provided figures demonstrate two decision trees used in machine learning to classify data: Figure 2a and Figure 2b.

- **Figure 2a**: This is an unpruned decision tree. It begins with a root node labeled **A** with three branches (labeled 0, 1, and 2), leading to further decisions or terminal nodes. The tree further splits on attribute **B** where applicable, with binary branches labeled 0 and 1, leading to classification outcomes of `+` (positive) or `-` (negative).

- **Figure 2b**: This tree is a pruned version of Figure 2a. The root node **A** also has three branches. However, it simplifies the decision process. For branch 0, it directly classifies as `+`. For branch 1, it splits with attribute **B** leading to `+` or `-` outcomes. Branch 2 directly classifies as `-`.

#### Training and Test Set Data

The training and test sets are presented in a table, identifying combinations of values for attributes A and B and the number of instances classified as positive or negative.

##### Training Set

| A | B | # of (+) instances | # of (-) instances |
|---|---|--------------------|--------------------|
| 0 | 0 | 5                  | 3                  |
| 0 | 1 | 3                  | 4                  |
| 1 | 0 | 22                 | 7                  |
| 1 | 1 | 7                  | 32                 |
| 2 | 0 | 2                  | 5                  |
| 2 | 1 | 6                  | 4                  |

##### Test Set

| A | B | # of (+) instances | # of (-) instances |
|---|---|--------------------|--------------------|
| 0 | 0 | 4                  | 1                  |
| 0 | 1 | 3                  | 1                  |
| 1 | 0 | 6                  | 3                  |
| 1 | 1 | 12                 | 5                  |
| 2 | 0 | 2                  | 3                  |
| 2 | 1 | 3                  | 5                  |

#### Tasks
Transcribed Image Text:### Decision Trees and Generalization Error Rate #### Decision Trees Overview The provided figures demonstrate two decision trees used in machine learning to classify data: Figure 2a and Figure 2b. - **Figure 2a**: This is an unpruned decision tree. It begins with a root node labeled **A** with three branches (labeled 0, 1, and 2), leading to further decisions or terminal nodes. The tree further splits on attribute **B** where applicable, with binary branches labeled 0 and 1, leading to classification outcomes of `+` (positive) or `-` (negative). - **Figure 2b**: This tree is a pruned version of Figure 2a. The root node **A** also has three branches. However, it simplifies the decision process. For branch 0, it directly classifies as `+`. For branch 1, it splits with attribute **B** leading to `+` or `-` outcomes. Branch 2 directly classifies as `-`. #### Training and Test Set Data The training and test sets are presented in a table, identifying combinations of values for attributes A and B and the number of instances classified as positive or negative. ##### Training Set | A | B | # of (+) instances | # of (-) instances | |---|---|--------------------|--------------------| | 0 | 0 | 5 | 3 | | 0 | 1 | 3 | 4 | | 1 | 0 | 22 | 7 | | 1 | 1 | 7 | 32 | | 2 | 0 | 2 | 5 | | 2 | 1 | 6 | 4 | ##### Test Set | A | B | # of (+) instances | # of (-) instances | |---|---|--------------------|--------------------| | 0 | 0 | 4 | 1 | | 0 | 1 | 3 | 1 | | 1 | 0 | 6 | 3 | | 1 | 1 | 12 | 5 | | 2 | 0 | 2 | 3 | | 2 | 1 | 3 | 5 | #### Tasks
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