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Decision Tree Learning Guide

This document outlines topics related to decision tree learning, including: - What decision trees are and how they are used for classification problems - How to represent decision trees using examples - The ID3 algorithm for generating decision trees from training data - Concepts of entropy and information gain used in decision tree learning - How to construct decision trees from sample training data involving classification of activities like playing tennis based on weather conditions.

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Manjunath KV Assistant Professor (CSE)
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297 views2 pages

Decision Tree Learning Guide

This document outlines topics related to decision tree learning, including: - What decision trees are and how they are used for classification problems - How to represent decision trees using examples - The ID3 algorithm for generating decision trees from training data - Concepts of entropy and information gain used in decision tree learning - How to construct decision trees from sample training data involving classification of activities like playing tennis based on weather conditions.

Uploaded by

Manjunath KV Assistant Professor (CSE)
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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Machine Learning 15CS73

MODULE 2 – DECISION TREE LEARNING

1. What is decision tree and decision tree learning?


2. Explain representation of decision tree with example.
3. What are appropriate problems for Decision tree learning?
4. Explain the concepts of Entropy and Information gain.
5. Describe the ID3 algorithm for decision tree learning with example
6. Give Decision trees to represent the Boolean Functions:
a) A && ~ B
b) A V [B && C]
c) A XOR B
d) [A&&B] V [C&&D]
7. Give Decision trees for the following set of training examples
Day Outlook Temperature Humidity Wind PlayTennis
D1 Sunny Hot High Weak No
D2 Sunny Hot High Strong No
D3 Overcast Hot High Weak Yes
D4 Rain Mild High Weak Yes
D5 Rain Cool Normal Weak Yes
D6 Rain Cool Normal Strong No
D7 Overcast Cool Normal Strong Yes
D8 Sunny Mild High Weak No
D9 Sunny Cool Normal Weak Yes
D10 Rain Mild Normal Weak Yes
D11 Sunny Mild Normal Strong Yes
D12 Overcast Mild High Strong Yes
D13 Overcast Hot Normal Weak Yes
D14 Rain Mild High Strong No

8. Consider the following set of training examples.


a) What is the entropy of this collection of training example with respect to the target
function classification?
b) What is the information gain of a2 relative to these training examples?

1 Deepak D, Asst. Prof., Dept. of CS&E, Canara Engineering College, Mangaluru


Machine Learning 15CS73

Instance Classification a1 a2
1 + T T
2 + T T
3 - T F
4 + F F
5 - F T
6 - F T

9. Identify the entropy, information gain and draw the decision trees for the following set
of training examples

Car Travel cost Income Transportation


Gender
ownership Level (Class)
Male 0 Cheap Low Bus
Male 1 Cheap Medium Bus
Female 1 Cheap Medium Train
Female 0 Cheap Low Bus
Male 1 Cheap Medium Bus
Male 0 Standard Medium Train
Female 1 Standard Medium Train
Female 1 Expensive High Car
Male 2 Expensive Medium Car
Female 2 Expensive High Car

10. Discuss Hypothesis Space Search in Decision tree Learning.


11. Discuss Inductive Bias in Decision Tree Learning.
12. What are Restriction Biases and Preference Biases and differentiate between them.
13. Write a note on Occam’s razor and minimum description principal.
14. What are issues in learning decision trees

2 Deepak D, Asst. Prof., Dept. of CS&E, Canara Engineering College, Mangaluru

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