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22. Choose the claim about the ID3 decision tree construction algorithm that is true. (a) Increasing the number of training samples given to the algorithm can never increase its true error over the validation set. (b) Increasing the number of nodes that the algorithm can add to the tree can never increase its true error over the validation set. (c) If the ID3 algorithm is modified so that the attribute for each node is chosen randomly from the remaining attributes of the current branch, instead of choosing the attribute that produces the highest local gain, every node of the result tree will still have a nonnegative information gain. (d) When building a decision tree with ID3 algorithm until the tree classifies the entire training set without errors, the policy of always choosing for the current subtree root the attribute with the highest local information gain will produce the theoretically smallest possible such decision tree.

22. Choose the claim about the ID3 decision tree construction algorithm that is true. (a) Increasing the number of training samples given to the algorithm can never increase its true error over the validation set. (b) Increasing the number of nodes that the algorithm can add to the tree can never increase its true error over the validation set. (c) If the ID3 algorithm is modified so that the attribute for each node is chosen randomly from the remaining attributes of the current branch, instead of choosing the attribute that produces the highest local gain, every node of the result tree will still have a nonnegative information gain. (d) When building a decision tree with ID3 algorithm until the tree classifies the entire training set without errors, the policy of always choosing for the current subtree root the attribute with the highest local information gain will produce the theoretically smallest possible such decision tree.

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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22. Choose the claim about the ID3 decision tree construetion algorithm that is true. (a) Increasing the number of training samples given to the algorithm can never increase its true error over the validation set. (b) Increasing the number of nodes that the algorithm can add to the tree can never increase its true error over the validation set. (c) If the ID3 algorithm is modified so that the attribute for each node is chosen randomly from the remaining attributes of the current branch, instead of choosing the attribute that produces the highest local gain, every node of the result tree will still have a nonnegative information gain. (d) When building a decision tree with ID3 algorithm until the tree classifies the entire training set without errors, the policy of always choosing for the current subtree root the attribute with the highest local information gain will produce the theoretically smallest possible such decision tree.
Transcribed Image Text:22. Choose the claim about the ID3 decision tree construetion algorithm that is true. (a) Increasing the number of training samples given to the algorithm can never increase its true error over the validation set. (b) Increasing the number of nodes that the algorithm can add to the tree can never increase its true error over the validation set. (c) If the ID3 algorithm is modified so that the attribute for each node is chosen randomly from the remaining attributes of the current branch, instead of choosing the attribute that produces the highest local gain, every node of the result tree will still have a nonnegative information gain. (d) When building a decision tree with ID3 algorithm until the tree classifies the entire training set without errors, the policy of always choosing for the current subtree root the attribute with the highest local information gain will produce the theoretically smallest possible such decision tree.
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