Machine Learning

UNIT 2
Important Questions

1. Give
   decision trees to represent the following Boolean functions

·        
A ˄˜B

·        
A V [B ˄ C]

·        
A XOR B

·        
[A ˄ B] v [C ˄ D]

 

2. Consider the following set
   of training examples:

 

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

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

3. NASA
   wants to be able to discriminate between Martians (M) and Humans (H) based
   on the following characteristics: Green ∈ {N, Y}, Legs ∈
   {2,3}, Height ∈ {S, T}, Smelly ∈ {N, Y}

Our available
training data is as follows:

a) Greedily learn a decision tree using the ID3
algorithm and draw the tree .

b) (i) Write the learned concept for Martian as a
set of conjunctive rules (e.g., if (green=Y and legs=2 and height=T and
smelly=N), then Martian; else if … then Martian; else Human).

(ii) The solution of part b) i) above uses up to 4
attributes in each conjunction. Find a set of conjunctive rules using only 2
attributes per conjunction that still results in zero error in the training
set. Can this simpler hypothesis be represented by a decision tree of depth 2?
Justify.

4.Discuss Entropy in ID3 algorithm with an example

5.Compare Entropy and Information Gain in ID3 with
an example.

6. Describe hypothesis Space search in ID3 and
contrast it with Candidate-Elimination algorithm.

7. Relate Inductive bias with respect to Decision
tree learning

8. Illustrate Occam’s razor and relate the
importance of Occam’s razor with respect to ID3 algorithm.

9. List the issues in Decision Tree Learning.
Interpret the algorithm with respect to Overfitting the data.

10. Discuss the effect of reduced Error pruning in
decision tree algorithm.

11. What type of problems are best suited for
decision tree learning

12. Write the steps of ID3Algorithm

13. What are the capabilities and limitations of
ID3

14. Define (a) Preference Bias (b) Restriction Bias

15. Explain the various issues in Decision tree
Learning

16. Describe Reduced Error Pruning

17. What are the alternative measures for selecting
attributes

18. What is Rule Post Pruning