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Handling Missing Data while constructing Decision Trees. We assume that data is given as some feature r E Rd, in which some attributes/entries are unknown. For this problem, we will use the train dataset D with 5 datapoints, binary label, and d = 3 attributes as given in Table 4. Example a1 x2 T3 Label y |0 0 |0 1 1 1 3 1 4 1 1 1 1 1 1 1 Table 1: Dataset for Problem 4 – Decision Tree by Hand. (a) Learn a decision tree for the given dataset D using Information gain as the splitting criterion.

Handling Missing Data while constructing Decision Trees. We assume that data is given as some feature r E Rd, in which some attributes/entries are unknown. For this problem, we will use the train dataset D with 5 datapoints, binary label, and d = 3 attributes as given in Table 4. Example a1 x2 T3 Label y |0 0 |0 1 1 1 3 1 4 1 1 1 1 1 1 1 Table 1: Dataset for Problem 4 – Decision Tree by Hand. (a) Learn a decision tree for the given dataset D using Information gain as the splitting criterion.

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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Handling Missing Data while constructing Decision Trees. We assume that data is given as some feature r E Rd, in which some attributes/entries are unknown. For this problem, we will use the train dataset D with 5 datapoints, binary label, and d = 3 attributes as given in Table 4. Example a1 x2 T3 Label y |0 0 |0 1 1 1 3 1 4 1 1 1 1 1 1 1 Table 1: Dataset for Problem 4 – Decision Tree by Hand. (a) Learn a decision tree for the given dataset D using Information gain as the splitting criterion.
Transcribed Image Text:Handling Missing Data while constructing Decision Trees. We assume that data is given as some feature r E Rd, in which some attributes/entries are unknown. For this problem, we will use the train dataset D with 5 datapoints, binary label, and d = 3 attributes as given in Table 4. Example a1 x2 T3 Label y |0 0 |0 1 1 1 3 1 4 1 1 1 1 1 1 1 Table 1: Dataset for Problem 4 – Decision Tree by Hand. (a) Learn a decision tree for the given dataset D using Information gain as the splitting criterion.
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