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What are the various ways of tree trimming (in data mining)? Explain in detail?

What are the various ways of tree trimming (in data mining)? Explain in detail?

Computer Networking: A Top-Down Approach (7th Edition)
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: What are the various ways of tree trimming (in data mining)? Explain in detail?** Tree trimming, also known as pruning, is a crucial technique used in data mining to simplify decision trees. This process helps to avoid overfitting and improves the generalization ability of the model. There are several ways to perform tree trimming: 1. **Pre-pruning (Early Stopping):** - **Description:** Pre-pruning stops the tree construction early before it perfectly classifies the training set. The process halts the tree growth at a certain point if the addition of further nodes doesn’t significantly improve prediction accuracy. - **Method:** This can be achieved by setting a threshold for the node's information gain or setting a maximum depth for the tree. - **Advantages:** It prevents the tree from becoming too complex and saves computational resources. - **Disadvantages:** It might lead to underfitting if stopped too early. 2. **Post-pruning:** - **Description:** Post-pruning, also known as backward pruning, removes branches from a fully grown tree to improve its performance on unseen data. - **Method:** After the tree is fully grown, the least significant branches are removed by evaluating the performance of the pruned tree on a validation set. Common techniques include the Reduced Error Pruning and Cost Complexity Pruning. - **Advantages:** It tends to produce smaller, more generalized trees compared to pre-pruning. - **Disadvantages:** It can be computationally intensive as it requires growing the entire tree first. 3. **Reduced Error Pruning:** - **Description:** This method involves removing a node if the error rate on the validation set is not increased by pruning the node, starting from the leaf nodes and proceeding backwards. - **Method:** Each node is replaced with the most frequent class if this reduces the error on the validation set. - **Advantages:** It is straightforward and easy to implement. - **Disadvantages:** It might not produce the most optimal tree. 4. **Cost-Complexity Pruning (CCP):** - **Description:** CCP, also known as weakest link pruning, involves pruning the tree by considering a parameter called alpha which controls the complexity of the tree. - **Method:** Nodes are pruned by optimizing a trade-off between the tree complexity and its predictive accuracy on the training set. - **Advantages:** It provides a balance between underfitting and
Transcribed Image Text:**Question: What are the various ways of tree trimming (in data mining)? Explain in detail?** Tree trimming, also known as pruning, is a crucial technique used in data mining to simplify decision trees. This process helps to avoid overfitting and improves the generalization ability of the model. There are several ways to perform tree trimming: 1. **Pre-pruning (Early Stopping):** - **Description:** Pre-pruning stops the tree construction early before it perfectly classifies the training set. The process halts the tree growth at a certain point if the addition of further nodes doesn’t significantly improve prediction accuracy. - **Method:** This can be achieved by setting a threshold for the node's information gain or setting a maximum depth for the tree. - **Advantages:** It prevents the tree from becoming too complex and saves computational resources. - **Disadvantages:** It might lead to underfitting if stopped too early. 2. **Post-pruning:** - **Description:** Post-pruning, also known as backward pruning, removes branches from a fully grown tree to improve its performance on unseen data. - **Method:** After the tree is fully grown, the least significant branches are removed by evaluating the performance of the pruned tree on a validation set. Common techniques include the Reduced Error Pruning and Cost Complexity Pruning. - **Advantages:** It tends to produce smaller, more generalized trees compared to pre-pruning. - **Disadvantages:** It can be computationally intensive as it requires growing the entire tree first. 3. **Reduced Error Pruning:** - **Description:** This method involves removing a node if the error rate on the validation set is not increased by pruning the node, starting from the leaf nodes and proceeding backwards. - **Method:** Each node is replaced with the most frequent class if this reduces the error on the validation set. - **Advantages:** It is straightforward and easy to implement. - **Disadvantages:** It might not produce the most optimal tree. 4. **Cost-Complexity Pruning (CCP):** - **Description:** CCP, also known as weakest link pruning, involves pruning the tree by considering a parameter called alpha which controls the complexity of the tree. - **Method:** Nodes are pruned by optimizing a trade-off between the tree complexity and its predictive accuracy on the training set. - **Advantages:** It provides a balance between underfitting and
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