Indian Institute of Technology Madras
BS Degree Programme · Examination & Study Paper
Machine Learning Techniques
Bagging, boosting & random forests
Official Paper
Examination Instructions & Guidelines
- Solve on notebook/paper first before checking solutions.
- MCQ: Exactly 1 option is correct. Negative marking applies.
- MSQ: One or more options correct. Select all to score.
- NAT: Numerical response. No negative marking.
- Scientific calculator allowed for all calculations.
- Official answer key is sealed at the end of this sheet.
End Term · 31 Aug 2025 (FN)End Term
- Q1NAT4 Marks·Write the number
Your answer:
End Term · 31 Aug 2025 (AN)End Term
- Q2NAT3 Marks·Write the number
Your answer:
End Term · 13 Apr 2025 (FN)End Term
- Q3MCQ4 Marks·Choose one option
- A
- B
- C
- D
- A
- Q4MSQ2 Marks·Choose all correct options
Which of the following statements are true for bagging?
- AThe final model has lesser variance than the individual learners.
- BThe final model has a higher variance than the individual learners.
- CEstimators in bagging can be trained parallely.
- DIf the number of data points is large, typically two-third of the data points remain unselected in bags.
End Term · 13 Apr 2025 (AN)End Term
- Q5MCQ1 Mark·Choose one option
Weak learners used in the ensemble method typically perform slightly better than random guessing.
- ATRUE
- BFALSE
Read the passage below, then answer Q6–Q7
Based on the above data, answer the given subquestions.
Q6MCQ2 Marks·Choose one optionWhich classifier has the lower training error?
- A
- B
- A
- Q7MCQ3 Marks·Choose one option
- A
- B
- C
- D
- A
End Term · 22 Dec 2024 (FN)End Term
- Q8MCQ2 Marks·Choose one option
In each round of AdaBoost, the weight for a particular training observation is decreased from round t to round t + 1 if the observation was:
- Aclassified incorrectly by the weak learner trained in the round.
- Bclassified correctly by the weak learner trained in the round.
- Cclassified incorrectly by a majority of the weak learners trained up to the round.
- Dclassified correctly by a majority of the weak learners trained up to the round.
End Term · 22 Dec 2024 (AN)End Term
- Q9MCQ2 Marks·Choose one option
In each round of AdaBoost, the weight for a particular training observation is decreased from round t to round t + 1 if the observation was:
- Aclassified incorrectly by the weak learner trained in the round.
- Bclassified correctly by the weak learner trained in the round.
- Cclassified incorrectly by a majority of the weak learners trained up to the round.
- Dclassified correctly by a majority of the weak learners trained up to the round.