Job Saarnee

JOB SAARNEE

Top Most Platform for Job Updates O Level Results Answer keys AKTU MCQs

UNIT 2 Most Important of Machine Learning

  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]


 

  1. 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?


  1. 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

 

1 thought on “UNIT 2 Most Important of Machine Learning”

Leave a Comment

Your email address will not be published. Required fields are marked *

Shopping Cart