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Problem 1 Consider the following data set comprised of three binary input attributes (A1, A2, and A3) and one binary output: Example A1 A2 A3 Output y X1 1 0 0 0 X2 1 0 1 0 X3 0 1 0 0 X4 1 1 1 1 X5 1 1 0 1 Use the Learn-Decision-Tree algorithm to learn (generate) a decision tree for these data. You must show the computations (of entropy, remainder, gain, etc) made to determine the attribute to split at each node. Problem 2 (Extra Credits) Prune your tree learned from Problem 1 by Information Gain. Explain reasons of your choices made while pruning.

          Problem 1
Consider the following data set comprised of three binary input attributes (A1, A2, and A3) and one
binary output:
Example A1 A2 A3 Output y
X1 1 0 0 0
X2 1 0 1 0
X3 0 1 0 0
X4 1 1 1 1
X5 1 1 0 1
Use the Learn-Decision-Tree algorithm to learn (generate) a decision tree for these data. You must
show the computations (of entropy, remainder, gain, etc) made to determine the attribute to split at
each node.
Problem 2 (Extra Credits)
Prune your tree learned from Problem 1 by Information Gain. Explain reasons of your choices made
while pruning.

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Problem 1
Consider the following data set comprised of three binary input attributes (A1, A2, and A3) and one
binary output:
Example A1 A2 A3 Output y
X1 1 0 0 0
X2 1 0 1 0
X3 0 1 0 0
X4 1 1 1 1
X5 1 1 0 1
Use the Learn-Decision-Tree algorithm to learn (generate) a decision tree for these data. You must
show the computations (of entropy, remainder, gain, etc) made to determine the attribute to split at
each node.
Problem 2 (Extra Credits)
Prune your tree learned from Problem 1 by Information Gain. Explain reasons of your choices made
while pruning.

Added by Jessica W.

Computer Science and Information Technology

Trishna Knowledge Systems 2018 Edition

Instant Answer

Solved by Expert Nick Johnson

Step 1

However, the output values are missing in the provided data. For the purpose of this exercise, let's assume the output values are known for each example (X1 to X5). Without output values, we cannot calculate entropy or information gain. Assuming hypothetical Show more…

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Texts: Problem 1 Consider the following data set comprised of three binary input attributes (A1, A2, and A3) and one binary output: Example A1 A2 A3 Output y X1 0 0 0 X2 0 X3 0 X4 1 X5 1 Use the Learn-Decision-Tree algorithm to learn (generate) a decision tree for these data. You must show the computations (of entropy, remainder, gain, etc.) made to determine the attribute to split at each node. Problem 2 (Extra Credits) Prune your tree learned from Problem 1 by Information Gain. Explain reasons for your choices made while pruning.

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