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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

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

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
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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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