NPTEL Introduction to Machine Learning Week 8 Assignment Answers 2025
1. Which of these statements is/are True about Random Forests?
- The goal of random forests is to increase the correlation between the trees.
- The goal of random forests is to decrease the correlation between the trees.
- In Random Forests, each decision tree fits the residuals from the previous one; thus, the correlation between the trees won’t matter.
- None of these
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2. Consider the two statements:
Statement 1: Gradient Boosted Decision Trees can overfit easily.
Statement 2: It is easy to parallelize Gradient Boosted Decision Trees.
Which of these are true?
- Both the statements are True.
- Statement 1 is true, and statement 2 is false.
- Statement 1 is false, and statement 2 is true.
- Both the statements are false.
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3. A dataset with two classes is plotted below.
Does the data satisfy the Naive Bayes assumption?
- Yes
- No
- The given data is insufficient
- None of these
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4. Consider the below dataset:
Suppose you have to classify a test example “The ball won the race to the boundary” and are asked to compute P(Cricket |“The ball won the race to the boundary”), what is an issue that you will face if you are using Naive Bayes Classifier, and how will you work around it? Assume you are using word frequencies to estimate all the probabilities.
- There won’t be a problem, and the probability of P(Cricket |“The ball won the race to the boundary”) will be equal to 1.
- Problem: A few words that appear at test time do not appear in the dataset. Solution: Smoothing.
- Problem: A few words that appear at test time appear more than once in the dataset. Solution: Remove those words from the dataset.
- None of these
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5. A company hires you to look at their classification system for whether a given customer would potentially buy their product. When you check the existing classifier on different folds of the training set, you find that it manages a low accuracy of usually around 60%. Sometimes, it’s barely above 50%.
With this information in mind, and without using additional classifiers, which of the following ensemble methods would you use to increase the classification accuracy effectively?
- Committee Machine
- AdaBoost
- Bagging
- Stacking
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6. Suppose you have a 6 class classification problem with one input variable. You decide to use logistic regression to build a predictive model. What is the minimum number of (β0,β
) parameter pairs that need to be estimated?
- 6
- 12
- 5
- 10
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7. The figure below shows a Bayesian Network with 9 variables, all of which are binary.
Which of the following is/are always true for the above Bayesian Network?
- P(A, B|G) = P(A|G)P(B|G)
- P(A, I) = P(A)P(I)
- P(B, H|E, G) = P(B|E, G)P(H|E, G)
- P(C|B, F) = P(C|F)
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8. Consider the following data for 20 budget phones, 30 mid-range phones, and 20 high-end phones:
Consider a phone with 2 SIM card slots and NFC but no 5G compatibility. Calculate the probabilities of this phone being a budget phone, a mid-range phone, and a high-end phone using the Naive Bayes method. The correct ordering of the phone type from the highest to the lowest probability is?
- Budget, Mid-Range, High End
- Budget, High End, Mid-Range
- Mid-Range, High End, Budget
- High End, Mid-Range, Budget
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