### Decision Tree Analysis#### Decision Trees Overview- **Figure 2a** illustrates a complex decision tree. The root node is labeled 'A' with branches splitting into various decision paths based on the values 0, 1, and 2. Each branch leads to a node labeled 'B', splitting further based on values, ultimately leading to terminal nodes representing classifications ('+' or '-').- **Figure 2b** is a pruned version of the tree shown in Figure 2a. It simplifies decision-making by directly linking the root node 'A' to terminal classifications, reducing complexity while maintaining essential decision paths.#### Training and Test SetsThe table below shows the training and test sets with different feature combinations and their respective positive and negative instance counts.| A | B | Training Set (+) | Training Set (-) | Test Set (+) | Test Set (-) ||---|---|------------------|-----------------|--------------|-------------|| 0 | 0 | 5 | 3 | 4 | 1 || 0 | 1 | 3 | 4 | 3 | 1 || 1 | 0 | 22 | 7 | 6 | 3 || 1 | 1 | 7 | 32 | 3 | 15 || 2 | 0 | 2 | 5 | 5 | 2 || 2 | 1 | 6 | 4 | 2 | 5 |#### Questions**c. Pruning Decision**- Determine if the original tree should be pruned based on the pessimistic error estimate.- **Pessimistic Error Estimate**: Evaluate error by accounting for possible overfitting through considering tree complexity and test data error rates.**d. Identifying Issues and Error Estimation**- Compare differences such as overfitting in the more detailed tree (Figure 2a) versus the pruned tree (Figure 2b).- **Error Estimation**: The pessimistic approach aids in balancing accuracy with complexity, promoting a model that generalizes better by reducing overfitting.

The first two answers is given please submit the last two question separate we will answer that…

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 Figure 2a Figure 2b Training set Test set A. B # of (+) instances # of (-) instances # of (+) instances # of (-) instances 5 3 4 1 1 3 4 3 1 22 7 1. 7 32 3 15 2 5 2 1 6 2 5 c. Based on the pessimistic estimate of the error rate, determine whether the original tree should be pruned and briefly explain. d. What is the issue that you can identify in the decision tree of Figure 2a but not in the tree of Figure 2b? Explain how the pessimistic estimate of the classification error tries to capture that.

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 Figure 2a Figure 2b Training set Test set A. B # of (+) instances # of (-) instances # of (+) instances # of (-) instances 5 3 4 1 1 3 4 3 1 22 7 1. 7 32 3 15 2 5 2 1 6 2 5 c. Based on the pessimistic estimate of the error rate, determine whether the original tree should be pruned and briefly explain. d. What is the issue that you can identify in the decision tree of Figure 2a but not in the tree of Figure 2b? Explain how the pessimistic estimate of the classification error tries to capture that.

Computer Networking: A Top-Down Approach (7th Edition)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

Section: Chapter Questions

Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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Question

### Decision Tree Analysis

#### Decision Trees Overview

- **Figure 2a** illustrates a complex decision tree. The root node is labeled 'A' with branches splitting into various decision paths based on the values 0, 1, and 2. Each branch leads to a node labeled 'B', splitting further based on values, ultimately leading to terminal nodes representing classifications ('+' or '-').

- **Figure 2b** is a pruned version of the tree shown in Figure 2a. It simplifies decision-making by directly linking the root node 'A' to terminal classifications, reducing complexity while maintaining essential decision paths.

#### Training and Test Sets

The table below shows the training and test sets with different feature combinations and their respective positive and negative instance counts.

| A | B | Training Set (+) | Training Set (-) | Test Set (+) | Test Set (-) |
|---|---|------------------|-----------------|--------------|-------------|
| 0 | 0 | 5 | 3 | 4 | 1 |
| 0 | 1 | 3 | 4 | 3 | 1 |
| 1 | 0 | 22 | 7 | 6 | 3 |
| 1 | 1 | 7 | 32 | 3 | 15 |
| 2 | 0 | 2 | 5 | 5 | 2 |
| 2 | 1 | 6 | 4 | 2 | 5 |

#### Questions

**c. Pruning Decision**
- Determine if the original tree should be pruned based on the pessimistic error estimate.
- **Pessimistic Error Estimate**: Evaluate error by accounting for possible overfitting through considering tree complexity and test data error rates.

**d. Identifying Issues and Error Estimation**
- Compare differences such as overfitting in the more detailed tree (Figure 2a) versus the pruned tree (Figure 2b).
- **Error Estimation**: The pessimistic approach aids in balancing accuracy with complexity, promoting a model that generalizes better by reducing overfitting.

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