NPTEL Introduction to Machine Learning Week 8 Assignment Answers 2024

📚 Course: NPTEL Introduction to Machine Learning 🗓️ Week: 8 📝 Assignment: Answers provided 📅 Year: 2024 🧠 Subject: Machine Learning 📑 ...

📚 Course: NPTEL Introduction to Machine Learning

🗓️ Week: 8

📝 Assignment: Answers provided

📅 Year: 2024

🧠 Subject: Machine Learning

📑 Format: Assignment completion

🔍 Source: NPTEL platform

Summary: Answers to the Week 8 assignment of NPTEL's Introduction to Machine Learning course for the year 2024

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1. Consider the Bayesian network given below. Which of the following statement(s) is/are correct?
A) B is independent of F, given D.
B) A is independent of E, given C.
C) E and F are not independent, given D.
D) A and B are not independent, given D.
Answer: B) A is independent of E, given C.
2.
Answer: [Answer not provided]
3. A decision tree classifier learned from a fixed training set achieves 100% accuracy. Which of the following models trained using the same training set will also achieve 100% accuracy? (Assume P(xi|c) as Gaussians)
I) Logistic Regressor.
II) A polynomial of degree one kernel SVM.
III) A linear discriminant function.
IV) Naive Bayes classifier.
A) I
B) I and II
C) IV
D) III
E) None of the above.
Answer: B) I and II
4. Which of the following points would Bayesians and frequentists disagree on?
A) The use of a non-Gaussian noise model in probabilistic regression.
B) The use of probabilistic modeling for regression.
C) The use of prior distributions on the parameters in a probabilistic model.
D) The use of class priors in Gaussian Discriminant Analysis.
E) The idea of assuming a probability distribution over models.
Answer: C) The use of prior distributions on the parameters in a probabilistic model.
5. Consider the following data for 500 instances of home, 600 instances of office and 700 instances of factory type buildings. Suppose a building has a balcony and power-backup but is not multi-storied. According to the Naive Bayes algorithm, it is of type
A) Home
B) Office
C) Factory
Answer: B) Office
6. In AdaBoost, we re-weight points giving points misclassified in previous iterations more weight. Suppose we introduced a limit or cap on the weight that any point can take (for example, say we introduce a restriction that prevents any point’s weight from exceeding a value of 10). Which among the following would be an effect of such a modification? (Multiple options may be correct)
A) We may observe the performance of the classifier reduce as the number of stages increase.
B) It makes the final classifier robust to outliers.
C) It may result in lower overall performance.
D) It will make the problem computationally infeasible.
Answer: A) We may observe the performance of the classifier reduce as the number of stages increase. C) It may result in lower overall performance.
7. While using Random Forests, if the input data is such that it contains a large number (> 80%) of irrelevant features (the target variable is independent of these features), which of the following statements are TRUE?
A) Random Forests have reduced performance as the fraction of irrelevant features increases.
B) Random forests have increased performance as the fraction of irrelevant features increases.
C) The fraction of irrelevant features doesn’t impact the performance of random forest.
Answer: A) Random Forests have reduced performance as the fraction of irrelevant features increases.
8. 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?
A) 6
B) 12
C) 5
D) 10
Answer: D) 10