wek 8 discusion

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Austin Peay State University *

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Course

5080

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Computer Science

Date

Jan 9, 2024

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docx

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Uploaded by JusticeGalaxyHorse20

Why is tree pruning useful in decision tree induction? Decision trees are the most susceptible out of all the machine learning algorithms to overfitting and effective pruning can reduce this likelihood. A decision tree is a structure that includes a root node, branches, and leaf nodes. Each internal node denotes a test on an attribute, each branch denotes the outcome of a test, and each leaf node holds a class label. The topmost node in the tree is the root node. The importance of tree pruning includes:  Tree pruning is performed to remove anomalies in the training data due to noise or outliers.  Pruning reduces the size of decision trees by removing parts of the tree that do not provide power to classify instances. Converting a decision tree to rules before pruning has three main advantages: 1. Converting to rules allows distinguishing among the different contexts in which a decision node is used. 2. Converting to rules removes the distinction between attribute tests that occur near the root of the tree and those that occur near the leaves. 3. Converting to rules improves readability. What is a drawback of using a separate set of tuples to evaluate pruning? The drawback of using a separate set of tuples to evaluate pruning is that it may not be representative of the training tuples used to create the original decision tree. Also, it tends to be smaller and less complex and, thus, easier to comprehend. Pruned trees tend to be more compact than their unpruned counterparts. Given a decision tree, you have the option of (a) converting the decision tree to rules and then pruning the resulting rules, or (b) pruning the decision tree and then converting the pruned tree to rules. Which option would you choose and why? I would go with option (b); i.e. pruning the decision tree and then converting the prunes tree to rules. I chose (b) because I will bear in mind Cost Complexity & Computation Power.

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