Introduction To Machine Learning IIT-KGP Nptel Week 3 Assignment Answers
Are you looking for Nptel Introduction To Machine Learning IIT-KGP Week 3 Answers 2024? This guide offers comprehensive assignment solutions tailored to help you master key machine learning concepts such as supervised learning, regression, and classification.
Introduction To Machine Learning IIT-KGP Nptel Week 3 Assignment Answers
Introduction To Machine Learning IIT-KGP Week 3 Answers (July-Dec 2024)
Q1.What will be the class of a new data point x1=1 and x2=1 in 5-NN (k nearest neighbour with k=5) using euclidean distance measure? A. + Class B. – Class C. Cannot be determined
Answer: A. + Class
Q2.Imagine you are dealing with a 10 class classification problem. What is the maximum number of discriminant vectors that can be produced by LDA? A. 20 B. 14 C. 9 D. 10
These are Introduction To Machine Learning IIT-KGP Week 3 Answers
Q3.Fill in the blanks: K-Nearest Neighbor is a algorithm A. Non-parametric, eager B. Parametric, eager C. Non-parametric, lazy D. Parametric, lazy
Answer: C. Non-parametric, lazy
Q4.Which of the following statements is True about the KNN algorithm? A. KNN algorithm does more computation on test time rather than train time. B. KNN algorithm does lesser computation on test time rather than train time. C. KNN algorithm does an equal amount of computation on test time and train time. D. None of these.
Answer: A. KNN algorithm does more computation on test time rather than train time.
Limited computational resources. A. 1 only B. 2 only C. 1 and 2 only D. 1, 2 and 3
Answer: D. 1, 2 and 3
Q6 .When there is noise in data, which of the following options would improve the performance of the k-NN algorithm? A. Increase the value of k B. Decrease the value of k C. Changing value of k will not change the effect of the noise D. None of these
These are Introduction To Machine Learning IIT-KGP Week 3 Answers
Q9.In user-based collaborative filtering based recommendation, the items are recommended based on: A. Similar users B. Similar items C. Both of the above D. None of the above
Q1. Suppose, you have given the following data where x and y are the 2 input variables and Class is the dependent variable. Suppose, you want to predict the class of new data point x=1 and y=1 using euclidean distance in 3-NN. To which class the new data point belongs to?
a. +Class b. -Class c. Can’t say d. None of these
Answer: b. – Class
Q2. Imagine you are dealing with a 10 class classification problem. What is the maximum number of discriminant vectors that can be produced by LDA?
a. 20 b. 14 c. 9 d. 10
Answer: c. 9
These are Introduction To Machine Learning IIT-KGP Week 3 Answers
Q3. Fill in the blanks: K-Nearest Neighbor is a_ algorithm a. Non-parametric, eager b. Parametric, eager c. Non-parametric, lazy d. Parametric, lazy
Answer: c. Non-parametric, lazy
Q4. Which of the following statements is True about the KNN algorithm? a. KNN algorithm does more computation on test time rather than train time. b. KNN algorithm does lesser computation on test time rather than train time. c. KNN algorithm does an equal amount of computation on test time and train time. d. None of these.
Answer: a. KNN algorithm does more computation on test time rather than train time.
These are Introduction To Machine Learning IIT-KGP Week 3 Answers
Q5. Which of the following necessitates feature reduction in machine learning? A. Irrelevant and redundant features B. Curse of dimensionality C. Limited computational resources. D. All of the above
Q6. When there is noise in data, which of the following options would improve the perfomance of the KNN algorithm? a. Increase the value of k b. Decrease the value of k c. Changing value of k will not change the effect of the noise d. None of these
Answer: a. Increase the value of k
These are Introduction To Machine Learning IIT-KGP Week 3 Answers
Q7. Find the value of the Pearson’s correlation coefficient of X and Y from the data in the following table. a. 0.47 b. 0.68 c. 1 d. 0.33
Answer: b. 0.68
Q8. Which of the following is false about PCA? a. PCA is a supervised method b. It identifies the directions that data have the largest variance c. Maximum number of principal components = number of features d. All principal components are othogonal to each other
Answer: a. PCA is a supervised method
Q9. In user-based collaborative filtering based recommendation, the items are recommended based on : a. Similar users b. Similar items c. Both of the above d. None of the above
Answer: a. Similar users
These are Introduction To Machine Learning IIT-KGP Week 3 Answers
Q10. Identify whether the following statement is true or false? “PCA can be used for projecting and visualizing data in lower dimensions.” a. True b. False
Answer: a. True
These are Introduction To Machine Learning IIT-KGP Week 3 Answers
