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IIT M IMPROVEMENT AN EXAM QDD4 01 - 01 Sep 2024

MLT End Term30 questions50 marks14 MCQ · 14 SA · 2 MSQImprovement / afternoon / QDD4
Elapsed 00:00
Q1MCQ2 marks
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Q2MCQ3 marks
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  1. 1
  2. 2
  3. 3
  4. 6
Q3MCQ3 marks
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Q4MCQ3 marks
Match the characteristics given below with the appropriate ensemble technique. 1. Decision stumps 2. Deep decision trees 3. Parallel execution 4. Sequential execution
  1. Bagging → (2), (3); Boosting → (1), (4)
  2. Bagging → (1), (4); Boosting → (2), (3)
  3. Bagging → (1), (3); Boosting → (2), (4)
  4. Bagging → (2), (4); Boosting → (1), (3)
Q5MCQ3 marks
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Q6MCQ3 marks
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Q7MCQ3 marks
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Q8MCQ3 marks
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  1. 0.75
  2. 0.25
  3. 0.5
  4. 0.15
Q9MSQ3 marksselect all that apply
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Q10MSQ3 marksselect all that apply
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  1. The dataset is linearly separable with a positive margin.
  2. The perceptron algorithm will terminate after a finite number of iterationswhen trained on this dataset.
  3. The dataset is linearly separable, but the margin may be zero.
  4. The dataset is not linearly separable.
Q11SA3 marks
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Q12SA3 marks
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Q13SA3 marks
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Comprehension passage - the questions below it refer to this
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Q14MCQ1 mark
The weight vector output by the perceptron algorithm can be expressed as a linear combination of the data-points where the coefficients of the linear combination are integers.
  1. TRUE
  2. FALSE
Q15MCQ1 mark
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  1. TRUE
  2. FALSE
Q16MCQ1 mark
If a soft-margin SVM is trained on this dataset, the optimal weight vector it returns will be the same as the one returned by a hard-margin SVM, irrespective of the value of the hyperparameter C.
  1. TRUE
  2. FALSE
Comprehension passage - the questions below it refer to this
Based on the above data, answer the given subquestions. question figure
Q17SA0.5 marks
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Q18SA0.5 marks
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Q19SA0.5 marks
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Q20SA0.5 marks
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Q21SA0.5 marks
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Q22SA0.5 marks
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Comprehension passage - the questions below it refer to this
Based on the above data, answer the given subquestions. question figure
Q23SA0.5 marks
Enter the number corresponding to the logistic loss.
Q24SA0.5 marks
Enter the number corresponding to the (SVM) hinge loss.
Q25SA0.5 marks
Enter the number corresponding to the perceptron loss.
Q26SA0.5 marks
Enter the number corresponding to the squared loss.
Q27MCQ1 mark
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Comprehension passage - the questions below it refer to this
Based on the above data, answer the given subquestions. question figure
Q28SA1 mark
How many learnable parameters does this network have? Ignore the biases in the computation.
Q29MCQ1 mark
What is the most appropriate choice of activation function for the output layer if the binary cross-entropy loss is used?
  1. Sigmoid
  2. Linear
  3. ReLU
Q30MCQ1 mark
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  1. ReLU
  2. Sigmoid

Answer key

  1. Q1 (MCQ, 2 marks): A
  2. Q2 (MCQ, 3 marks): C
  3. Q3 (MCQ, 3 marks): A
  4. Q4 (MCQ, 3 marks): A
  5. Q5 (MCQ, 3 marks): B
  6. Q6 (MCQ, 3 marks): A
  7. Q7 (MCQ, 3 marks): A
  8. Q8 (MCQ, 3 marks): A
  9. Q9 (MSQ, 3 marks): A, B
  10. Q10 (MSQ, 3 marks): A, B
  11. Q11 (SA, 3 marks): 10
  12. Q12 (SA, 3 marks): 0.75
  13. Q13 (SA, 3 marks): 0.50 to 0.60
  14. Q14 (MCQ, 1 marks): A
  15. Q15 (MCQ, 1 marks): B
  16. Q16 (MCQ, 1 marks): B
  17. Q17 (SA, 0.5 marks): 0
  18. Q18 (SA, 0.5 marks): 1
  19. Q19 (SA, 0.5 marks): 2
  20. Q20 (SA, 0.5 marks): 0
  21. Q21 (SA, 0.5 marks): 5
  22. Q22 (SA, 0.5 marks): -1
  23. Q23 (SA, 0.5 marks): 3
  24. Q24 (SA, 0.5 marks): 2
  25. Q25 (SA, 0.5 marks): 4
  26. Q26 (SA, 0.5 marks): 1
  27. Q27 (MCQ, 1 marks): A
  28. Q28 (SA, 1 marks): 205
  29. Q29 (MCQ, 1 marks): A
  30. Q30 (MCQ, 1 marks): A