Machine Learning Question With Answers Module 2

17CS73 Machine Learning Question With Answers Module 2

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:

  1. A && ~ B
  2. A V [B && C]
  3. A XOR B
  4. [A&&B] V [C&&D]

7. Give Decision trees for the following set of training examples

DayOutlookTemperatureHumidityWindPlayTennis
D1SunnyHotHighWeakNo
D2SunnyHotHighStrongNo
D3OvercastHotHighWeakYes
D4RainMildHighWeakYes
D5RainCoolNormalWeakYes
D6RainCoolNormalStrongNo
D7OvercastCoolNormalStrongYes
D8SunnyMildHighWeakNo
D9SunnyCoolNormalWeakYes
D10RainMildNormalWeakYes
D11SunnyMildNormalStrongYes
D12OvercastMildHighStrongYes
D13OvercastHotNormalWeakYes
D14RainMildHighStrongNo

8. Consider the following set of training examples.

  • What is the entropy of this collection of training examples with respect to the target function classification?
  • What is the information gain of a2 relative to these training examples?
InstanceClassificationa1a2
1+TT
2+TT
3TF
4+FF
5FT
6FT

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

GenderCar ownershipTravel costIncome LevelTransportation (Class)
MaleZeroCheapLowBus
MaleOneCheapMediumBus
FemaleOneCheapMediumTrain
FemaleZeroCheapLowBus
MaleOneCheapMediumBus
MaleZeroStandardMediumTrain
FemaleOneStandardMediumTrain
FemaleOneExpensiveHighCar
MaleTwoExpensiveMediumCar
FemaleTwoExpensiveHighCar

10. Construct the decision tree for the following tree using ID3 Algorithm,

Instancea1a2a3Classification
1TrueHotHighNo
2TrueHotHighNo
3FalseHotHighYes
4FalseCoolNormalYes
5FalseCoolNormalYes
6TrueCoolHighNo
7TrueHotHighNo
8TrueHotNormalYes
9FalseCoolNormalYes
10FalseCoolHighYes

11. Discuss Hypothesis Space Search in Decision tree Learning.

12. Discuss Inductive Bias in Decision Tree Learning.

13. What are Restriction Biases and Preference Biases and differentiate between them.

14. Write a note on Occam’s razor and minimum description principal.

15. What are issues in learning decision trees

Summary:

Her you find the Machine Learning Question With Answers Module 2.

2 thoughts on “Machine Learning Question With Answers Module 2”

Leave a Comment

Your email address will not be published.