introduction to machine learning nptel assignment 3 answers

Introduction to Machine Learning NPTEL Week 3 Solutions NPTEL 2023

This set of MCQ(multiple choice questions) focuses on the Introduction to Machine Learning NPTEL Week 3 Solutions NPTEL 2023 .

With the increased availability of data from varied sources there has been increasing attention paid to the various data driven disciplines such as analytics and machine learning. In this course we intend to introduce some of the basic concepts of machine learning from a mathematically well motivated perspective. We will cover the different learning paradigms and some of the more popular algorithms and architectures used in each of these paradigms.

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Introduction to Machine learning NPTEL 2023 Week 3 Solutions

Q1. Fill in the blanks: K-Nearest Neighbor is a _______, ________ algorithm

a) Non-parametric, eager b) Parametric, eager c) Non-parametric, lazy d) Parametric, lazy

Q2. You have been given the following 2 statements. Find out which of these options is/are true in the case of k-NN. (i) In case of very large value of k, we may include points from other classes in to the neighborhood. (ii) In case of too small value of k, the algorithm is very sensitive to noise.

a) (i) is True and (ii) is False b) (i) is False and (ii) is True c) Both are True d) Both are False

Q3. State whether the statement is True/False: k-NN algorithm does more computation on test time rather than train time.

a) True b) False

Introduction to Machine Learning NPTEL Week 3 Solutions

Q4. Suppose you are given the following images(1 represents the left image, 2 represents the middle and 3 represents the right). Now you task is to find out the value of k in k-NN in each of the images shown below. Here k1 is for 1st, k2 is for 2nd and k3 is for 3rd figure.

a) k1 > k2 > k3 b) k1 < k2 > k3 c) k1 < k2 < k3 d) None of these

Q5. Which of the following necessitates feature reduction in machine learning?

a) Irrelevant and redundant features b) Limited training data c) Limited computational resources d) All of the above

Q6. Suppose, you have given the following data where x and y are the 2 input variables and Class is the dependent variable.

a) + Class b) – Class c) Can’t say d) None of these

Q7. What is the optimum number of principal components in the below figure?

a) 10 b) 20 c) 30 d) 40

Q8. Suppose we are using dimensionality reduction as pre-processing technique, i.e, instead of using all the features, we reduce the data to k dimensions with PCA. And then use these PCA projections as our features. Which of the following statements is correct? Choose which of the options is correct?

a) Higher value of ‘k’ means more regularization b) Higher value of ‘k’ means less regularization

Q9. In 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

Q10. The major limitation of collaborative filtering is:

a) Cold start b) Overspecialization c) None of the above

Q11. Consider the figures below. Which figure shows the most probable PCA component directions for the data points?

a) A b) B c) C d) D

Q12. Suppose that you wish to reduce the number of dimensions of a given data to dimensions using PCA. Which of the following statement is correct?

a) Higher means more regularization b) Higher means less regularization c) Can’t say

Q13. Suppose you are given 7 plots 1-7 (left to right) and you want to compare Pearson correlation coefficients between variables of each plot. Which fo the following is true? 1. 1 < 2<3<4 2. 1>2>3>4 3. 7<6<5<4 4. 7>6>5>4

a) 1 and 3 b) 2 and 3 c) 1 and 4 d) 2 and 4

Q14. Imagine you are dealing with 20 class classification problem. What is the maximum number of discriminant vectors that can be produced by LDA?

a) 20 b) 19 c) 21 d) 10

Q15. In which of the following situations collaborative filtering algorithm is appropriate?

a) You manage an online bookstore and you have the book ratins from many users. For each user, you want to recommend other books he/she will like based on her previous ratins and other users’ ratings. b) You manage an online bookstore and you have the book raings from many users. You want to predict the expected sales volume(No of books sold) as a function of average rating of a book. c) Both A and B d) None of the above

Q1. Which of the following is false about a logistic regression based classifier?

a)  The logistic function is non-linear in the weights b) The logistic function is linear in the weights c) he decision boundary is non-linear in the weights d) The decision boundary is linear in the weights

Answer: a,c

Q2. Consider the case where two classes follow Gaussian distribution which are centered at (3, 9) and (−3, 3) and have identity covariance matrix. Which of the following is the separating decision boundary using LDA assuming the priors to be equal?

a) y−x=3 b) x+y=3 c) x+y=6 d) both (b) and (c) e) None of the above f) Can not be found from the given information

Q3. Consider the following relation between a dependent variable and an independent variable identified by doing simple linear regression. Which among the following relations between the two variables does the graph indicate?

a)  as the independent variable increases, so does the dependent variable b) as the independent variable increases, the dependent variable decreases c) if an increase in the value of the dependent variable is observed, then the independent variable will show a corresponding increase d) if an increase in the value of the dependent variable is observed, then the independent variable will show a corresponding decrease e)  the dependent variable in this graph does not actually depend on the independent variable f) none of the above

Q4. Given the following distribution of data points:

What method would you choose to perform Dimensionality Reduction?

a) Linear Discriminant Analysis b) Principal Component Analysis

Q5. In general, which of the following classification methods is the most resistant to gross outliers?

a) Quadratic Discriminant Analysis (QDA) b) Linear Regression c) Logistic regression d) Linear Discriminant Analysis (LDA)

Q6. Suppose that we have two variables, X and Y (the dependent variable). We wish to find the relation between them. An expert tells us that relation between the two has the form Y=m+X2+c=+2+. Available to us are samples of the variables X and Y. Is it possible to apply linear regression to this data to estimate the values of m and c?

a) no b) yes c) insufficient information

Q7. In a binary classification scenario where x is the independent variable and y is the dependent variable, logistic regression assumes that the conditional distribution y|x| follows a

a) Bernoulli distribution  b) binomial distribution  c) normal distribution  d) exponential distribution

Q8. Consider the following data:

Assuming that you apply LDA to this data, what is the estimated covariance matrix?

a) [1.8750.31250.31250.9375][1.8750.31250.31250.9375] b) [2.50.41670.41671.25] c) [1.8750.31250.31251.2188] d) [2.50.41670.41671.625] e) [3.251.16671.16672.375] f) [2.43750.8750.8751.7812] g) None of these

Q9. Given the following 3D input data, identify the principal component.

(Steps: center the data, calculate the sample covariance matrix, calculate the eigenvectors and eigenvalues, identify the principal component)

a) ⎢−0.10220.00180.9948⎤⎦⎥ b) ⎡⎣⎢0.5742−0.81640.0605⎤⎦⎥  c) ⎢0.57420.81640.0605⎤⎦⎥ d) ⎡⎣⎢−0.57420.81640.0605⎤⎦ e) ⎡⎣⎢0.81230.57740.0824⎤⎦⎥ f) None of the above

Q10. For the data given in the previous question, find the transformed input along the first two principal components.

a) ⎡⎣⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢0.6100−0.4487−1.26511.33450.5474−1.0250−1.26721.5142−0.0196−0.1181−0.11630.5702−0.72570.27270.1724−0.0355⎤⎦⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥ b) ⎡⎣⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢−0.1817−1.2404−2.05680.5428−0.2443−1.8167−2.05890.72250.89440.79590.79771.48420.18841.18681.08640.8785⎤⎦⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥ c) ⎡⎣⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢−6.2814−4.3143−3.7368−1.79502.29173.52894.91865.38830.6100−0.4487−1.26511.33450.5474−1.0250−1.26721.5142⎤⎦⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥  d) ⎡⎣⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢1.47213.43924.01665.958410.045111.282312.672013.1418−0.1817−1.2404−2.05680.5428−0.2443−1.8167−2.05890.7225⎤⎦⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥ e) None of the above

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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.

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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

Answer: C. 9

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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.

Q5. Which of the following necessitates feature reduction in machine learning?

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

Answer : A. Increase the value of k

Q7.Find the value of the Pearson’s correlation coefficient of X and Y from the data in the following table.

Α. 0.47 B. 0.68 C. 1 D. 0.33

Answer: B. 0.68

Q8. Which of the following statements is/are true about PCA?

• PCA is a supervised method
• It identifies the directions that data have the largest variance
• Maximum number of principal components <= number of features
• All principal components are orthogonal to each other

A. Only 2 B. 1, 3 and 4 C. 1, 2 and 3 D. 2, 3 and 4

Answer: D. 2, 3 and 4

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

Q10 .Identify whether the following statement is true or false? “Linear Discriminant Analysis (LDA) is a supervised method” A. TRUE B. FALSE

Answer: A. TRUE

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Introduction To Machine Learning IIT-KGP Week 3 Answers (July-Dec 202 2)

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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

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.

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

Answer: 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

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

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

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NPTEL Introduction to Machine Learning Week 3 Assignment Answers 2024

1. For a two-class problem using discriminant functions (δk – discriminant function for class k), where is the separating hyperplane located?

• Where δ1>δ2
• Where δ1<δ2
• Where δ1=δ2
• Where δ1+δ2=1

2. Given the following dataset consisting of two classes, A and B, calculate the prior probability of each class.

What are the prior probabilities of class A and class B?

• P(A)=0.5,P(B)=0.5
• P(A)=0.625,P(B)=0.375
• P(A)=0.375,P(B)=0.625
• P(A)=0.6,P(B)=0.4

3. In a 3-class classification problem using linear regression, the output vectors for three data points are [0.8, 0.3, -0.1], [0.2, 0.6, 0.2], and [-0.1, 0.4, 0.7]. To which classes would these points be assigned?

4. If you have a 5-class classification problem and want to avoid masking using polynomial regression, what is the minimum degree of the polynomial you should use?

5. Consider a logistic regression model where the predicted probability for a given data point is 0.4. If the actual label for this data point is 1, what is the contribution of this data point to the log-likelihood?

6. What additional assumption does LDA make about the covariance matrix in comparison to the basic assumption of Gaussian class conditional density?

• The covariance matrix is diagonal
• The covariance matrix is identity
• The covariance matrix is the same for all classes
• The covariance matrix is different for each class

7. What is the shape of the decision boundary in LDA?

• Can not be determined

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Contributors 3

• Computer Science and Engineering
• NOC:Introduction to Machine Learning (Video) 
• Co-ordinated by : IIT Kharagpur
• Available from : 2016-09-08
• Intro Video
• Lecture 01: Introduction
• Lecture 02: Different Types of Learning
• Lecture 03: Hypothesis Space and Inductive Bias
• Lecture 04: Evaluation and Cross-Validation
• Lecture 05 : Linear Regression
• Lecture 06 : Introduction to Decision Trees
• Lecture 07 : Learning Decision Tree
• Lecture 08 : Overfitting
• Lecture 9: Python Exercise on Decision Tree and Linear Regression
• Tutorial II
• Lecture 12: k-Nearest Neighbour
• Lecture 13: Feature Selection
• Lecture 14: Feature Extraction
• Lecture 15: Collaborative Filtering
• Lecture 16: Python Exercise on kNN and PCA
• Lecture 17: Tutorial III
• Lecture 18: Bayesian Learning
• Lecture 19: Naive Bayes
• Lecture 20 : Bayesian Network
• Lecture 21: Python Exercise on Naive Bayes
• Lecture 22: Tutorial IV
• Lecture 23 : Logistic Regression
• Lecture 24: Introduction Support Vector Machine
• Lecture 25: SVM : The Dual Formulation
• Lecture 26: SVM : Maximum Margin with Noise
• Lecture 27: Nonlinear SVM and Kernel Function
• Lecture 28: SVM : Solution to the Dual Problem
• Lecture 29: Python Exercise on SVM
• Lecture 30: Introduction
• Lecture 31: Multilayer Neural Network
• Lecture 32 : Neural Network and Backpropagation Algorithm
• Lecture 33: Deep Neural Network
• Lecture 34: Python Exercise on Neural Network
• Lecture 35: Tutorial VI
• Lecture 36: Introduction to Computational Learning Theory
• Lecture 37: Sample Complexity : Finite Hypothesis Space
• Lecture 38: VC Dimension
• Lecture 39 : Introduction to Ensembles
• Lecture 40 : Bagging and Boosting
• Lecture 41 : Introduction to Clustering
• Lecture 42 : Kmeans Clustering
• Lecture 43: Agglomerative Hierarchical Clustering
• Lecture 44: Python Exercise on kmeans clustering
• Watch on YouTube
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• Transcripts

Module Name	Download
noc19_cs52_assignment_Week_1
noc19_cs52_assignment_Week_2
noc19_cs52_assignment_Week_3
noc19_cs52_assignment_Week_4
noc19_cs52_assignment_Week_5
noc19_cs52_assignment_Week_6
noc19_cs52_assignment_Week_7
noc19_cs52_assignment_Week_8

Sl.No	Chapter Name	MP4 Download
1	Lecture 01: Introduction
2	Lecture 02: Different Types of Learning
3	Lecture 03: Hypothesis Space and Inductive Bias
4	Lecture 04: Evaluation and Cross-Validation
5	Tutorial I
6	Lecture 05 : Linear Regression
7	Lecture 06 : Introduction to Decision Trees
8	Lecture 07 : Learning Decision Tree
9	Lecture 08 : Overfitting
10	Lecture 9: Python Exercise on Decision Tree and Linear Regression
11	Tutorial II
12	Lecture 12: k-Nearest Neighbour
13	Lecture 13: Feature Selection
14	Lecture 14: Feature Extraction
15	Lecture 15: Collaborative Filtering
16	Lecture 16: Python Exercise on kNN and PCA
17	Lecture 17: Tutorial III
18	Lecture 18: Bayesian Learning
19	Lecture 19: Naive Bayes
20	Lecture 20 : Bayesian Network
21	Lecture 21: Python Exercise on Naive Bayes
22	Lecture 22: Tutorial IV
23	Lecture 23 : Logistic Regression
24	Lecture 24: Introduction Support Vector Machine
25	Lecture 25: SVM : The Dual Formulation
26	Lecture 26: SVM : Maximum Margin with Noise
27	Lecture 27: Nonlinear SVM and Kernel Function
28	Lecture 28: SVM : Solution to the Dual Problem
29	Lecture 29: Python Exercise on SVM
30	Lecture 30: Introduction
31	Lecture 31: Multilayer Neural Network
32	Lecture 32 : Neural Network and Backpropagation Algorithm
33	Lecture 33: Deep Neural Network
34	Lecture 34: Python Exercise on Neural Network
35	Lecture 35: Tutorial VI
36	Lecture 36: Introduction to Computational Learning Theory
37	Lecture 37: Sample Complexity : Finite Hypothesis Space
38	Lecture 38: VC Dimension
39	Lecture 39 : Introduction to Ensembles
40	Lecture 40 : Bagging and Boosting
41	Lecture 41 : Introduction to Clustering
42	Lecture 42 : Kmeans Clustering
43	Lecture 43: Agglomerative Hierarchical Clustering
44	Lecture 44: Python Exercise on kmeans clustering

Sl.No	Chapter Name	English
1	Lecture 01: Introduction
2	Lecture 02: Different Types of Learning
3	Lecture 03: Hypothesis Space and Inductive Bias
4	Lecture 04: Evaluation and Cross-Validation
5	Tutorial I
6	Lecture 05 : Linear Regression
7	Lecture 06 : Introduction to Decision Trees
8	Lecture 07 : Learning Decision Tree
9	Lecture 08 : Overfitting
10	Lecture 9: Python Exercise on Decision Tree and Linear Regression
11	Tutorial II
12	Lecture 12: k-Nearest Neighbour
13	Lecture 13: Feature Selection
14	Lecture 14: Feature Extraction
15	Lecture 15: Collaborative Filtering
16	Lecture 16: Python Exercise on kNN and PCA
17	Lecture 17: Tutorial III
18	Lecture 18: Bayesian Learning
19	Lecture 19: Naive Bayes
20	Lecture 20 : Bayesian Network
21	Lecture 21: Python Exercise on Naive Bayes
22	Lecture 22: Tutorial IV
23	Lecture 23 : Logistic Regression
24	Lecture 24: Introduction Support Vector Machine
25	Lecture 25: SVM : The Dual Formulation
26	Lecture 26: SVM : Maximum Margin with Noise
27	Lecture 27: Nonlinear SVM and Kernel Function
28	Lecture 28: SVM : Solution to the Dual Problem
29	Lecture 29: Python Exercise on SVM
30	Lecture 30: Introduction
31	Lecture 31: Multilayer Neural Network
32	Lecture 32 : Neural Network and Backpropagation Algorithm
33	Lecture 33: Deep Neural Network
34	Lecture 34: Python Exercise on Neural Network
35	Lecture 35: Tutorial VI
36	Lecture 36: Introduction to Computational Learning Theory
37	Lecture 37: Sample Complexity : Finite Hypothesis Space
38	Lecture 38: VC Dimension
39	Lecture 39 : Introduction to Ensembles
40	Lecture 40 : Bagging and Boosting
41	Lecture 41 : Introduction to Clustering
42	Lecture 42 : Kmeans Clustering
43	Lecture 43: Agglomerative Hierarchical Clustering
44	Lecture 44: Python Exercise on kmeans clustering

Sl.No	Language	Book link
1	English	Not Available
2	Bengali	Not Available
3	Gujarati	Not Available
4	Hindi	Not Available
5	Kannada	Not Available
6	Malayalam	Not Available
7	Marathi	Not Available
8	Tamil	Not Available
9	Telugu	Not Available

NPTEL Introduction to Machine Learning Assignment 3 Answers 2023

NPTEL Introduction to Machine Learning Assignment 3 Answers 2023:- In This article, we have provided the answers of Introduction to Machine Learning Assignment 3 You must submit your assignment to your own knowledge.

NPTEL Introduction To Machine Learning Week 3 Assignment Answer 2023

1. Which of the following are differences between LDA and Logistic Regression?

• Logistic Regression is typically suited for binary classification, whereas LDA is directly applicable to multi-class problems
• Logistic Regression is robust to outliers whereas LDA is sensiti v e to outliers
• both (a) and (b)
• None of these

2. We have two classes in our dataset. The two classes have the same mean but different variance.

LDA can classify them perfectly. LDA can NOT classify them perf e ctly. LDA is not applicable in data with these properties Insufficient information

3. We have two classes in our dataset. The two classes have the same variance but different mean.

LDA can classify them perfectly. LDA can NOT classify them perfectly. LDA is not applicable in data with these prop e rties Insufficient information

4. Given the following distribution of data points:

What method would you choose to perform Dimensionality Red u ction? Linear Discriminant Analysis Principal Component Analysis Both LDA and/or PCA. None of the above.

5. If log(1−p(x)/1+p(x))=β0+βx Wha t is p(x) ?

p(x)=1+eβ0+βx / eβ0+βx p(x)=1+eβ0+βx / 1−eβ0+βx p(x)=eβ0+βx / 1+eβ0+βx p(x)=1−eβ0+βx / 1+eβ0+βx

Red Orange Blue Green

7. Which of these techniques do we use to optimise Logistic Regres s ion:

Least S q uare Error Maximum Likelihood (a) or (b) are equally good (a) and (b) perform very poorly, so we generally avoid using Logistic Regression None of these

8. LDA assumes that the class data is distributed as:

Poisson Unif o rm Gaussian LDA makes no such assumption.

9. Suppose we have two variables, X and Y (the dependent variable), and we wish to find their relation. An expert tells us that relation between the two has the form Y=meX+c. Suppose the samples of the variables X and Y are available to us. Is it possible to apply linear regression to this data to estimate the values of m and c ?

No. Yes. Insufficient information. None of the above.

10. What might happen to our logistic regression model if the number of features is more th a n the number of samples in our dataset?

It will remain unaffected It will not find a hyperplane as the decision bound a ry It will over fit None of the above

NPTEL Introduction to Machine Learning Assignment 3 Answers [July 2022]

1. For linear classification we use: a. A linear function to separate the classes. b . A linear function to model the data. c. A linear loss. d. Non-linear function to fit the data.

2. Logit transformation for Pr(X=1) for given data is S=[0,1,1 , 0,1,0,1] a. 3/4 b. 4/3 c. 4/7 d . 3/7

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3. The output of binary class logistic regression lies in this range. a. [−∞,∞] b. [−1,1] c. [0,1] d. [−∞ , 0]

4. If log(1−p(x)1+p(x))=β0+βxlog What is p(x)p(x)?

5. Logistic regression is robust to outliers. Why? a . The squashing of output values between [0, 1] dampens the affect of outliers. b. Linear models are robust to outliers. c. The parameters in logistic regression tend to take small values due to the nature of the problem setting and hence outliers get translated to the same range as other samples. d. The given statement is false.

6. Aim of LDA is (multiple options may apply) a. Minimize intra-class variability. b. Maximize intra-class variability. c . Minimize the distance between the mean of classes d. Maximize the distance between the mean of classes

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7. We have two classes in our dataset with mean 0 and 1, and variance 2 and 3. a. LDA may be able to classify them perfectly. b. LDA will definitely be able to classify them perfectly. c. LDA will definitely NOT be able to classify them perfectly . d. None of the above.

8. We have two classes in our dataset with mean 0 and 5 , and variance 1 and 2. a. LDA may be able to classify them perfectly. b. LDA will definitely be able to classify them perfectly. c. LDA will definitely NOT be able to classify them perfectly. d. None of the above.

9. For the two classes ’+ ’ and ’-’ shown below. While performing LDA on it, which line is the most appropriate for projecting data points? a. Red b. Orange c. Blue d. Green

10. LDA assumes that the class data is distributed as: a. Poisson b. Uniform c. Gaussian d. LDA makes no such assumption .

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What is Introduction to Machine Learning?

With the increased availability of data from varied sources there has been increasing attention paid to the various data driven disciplines such as analytics and machine learning. In this course we intend to introduce some of the basic concepts of machine learning from a mathematically well motivated perspective. We will cover the different learning paradigms and some of the more popular algorithms and architectures used in each of these paradigms.

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NPTEL Introduction to Machine Learning Assignment 3 Answers [Jan 2022]

Q1. consider the case where two classes follow Gaussian distribution which are centered at (6, 8) and (−6, −4) and have identity covariance matrix. Which of the following is the separating decision boundary using LDA assuming the priors to be equal?

(A) x+y=2 (B) y−x=2 (C) x=y (D) both (a) and (b) (E) None of the above (F) Can not be found from the given information

Answer:- (A) x+y=2

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Q2. Which of the following are differences between PCR and LDA?

(A) PCR is unsupervised whereas LDA is supervised (B) PCR maximizes the variance in the data whereas LDA maximizes the separation between the classes (C) both (a) and (b) (D) None of these

Answer:- (A) PCR is unsupervised whereas LDA is supervised

Q3. Which of the following are differences between LDA and Logistic Regression?

(A) Logistic Regression is typically suited for binary classification, whereas LDA is directly applicable to multi-class problems (B) Logistic Regression is robust to outliers whereas LDA is sensitive to outliers (C) both (a) and (b) (D) None of these

Answer:- (C) both (a) and (b)

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Q4. We have two classes in our dataset. The two classes have the same mean but different variance.

• LDA can classify them perfectly.
• LDA can NOT classify them perfectly.
• LDA is not applicable in data with these properties
• Insufficient information

Answer:- 2. LDA can NOT classify them perfectly.

Q5. We have two classes in our dataset. The two classes have the same variance but different mean.

Answer:- 1. LDA can classify them perfectly.

Q6. Which of these techniques do we use to optimise Logistic Regression:

• Least Square Error
• Maximum Likelihood
• (a) or (b) are equally good
• (a) and (b) perform very poorly, so we generally avoid using Logistic Regression

Answer:- 2.Maximum Likelihood

Q7. Suppose we have two variables, X and Y (the dependent variable), and we wish to find their relation. An expert tells us that relation between the two has the form Y = meX + c . Suppose the samples of the variables X and Y are available to us. Is it possible to apply linear regression to this data to estimate the values of m and c ?

• insufficient information

Answer:- 2.yes

Q8. What might happen to our logistic regression model if the number of features is more than the number of samples in our dataset?

• It will remain unaffected
• It will not find a hyperplane as the decision boundary
• It will overfit
• None of the above

Answer:- 3. It will overfit

Q9. Logistic regression also has an application in

• Regression problems
• Sensitivity analysis
• Both (a) and (b)

Answer:- 3. Both (a) and (b)

Q10. Consider the following datasets:

Which of these datasets can you achieve zero training error using Logistic Regression (without any additional feature transformations)?

• Both the datasets
• Only on dataset 1
• Only on dataset 2
• None of the datasets

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NPTEL Introduction to Machine Learning Assignment 3 Answers 2022:- In This article, we have provided the answers of Introduction to Machine Learning Assignment 3

Disclaimer :- We do not claim 100% surety of solutions, these solutions are based on our sole expertise, and by using posting these answers we are simply looking to help students as a reference, so we urge do your assignment on your own.

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Nptel introduction to machine learning assignment answers week 3 2022 iitkgp.

Are you looking for help in Machine Learning NPTEL Week 3 Assignment Answers? So, here in this article, we have provided Machine Learning week 3 Assignment Answer’s hint.

NPTEL Introduction to Machine Learning Assignment Answers Week 3

Q1. Suppose, you have given the following data where x and y are the 2 input variables and Class is the dependent variable.

X	Y	Class
-1	1	–
0	1	+
0	2	–
1	-1	–
1	0	+
1	2	+
2	2	–
2	3	+

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: a. + Class

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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?

Answer : c. 9

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.

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

Answer: d. All of the above

Q6. When there is noise in data, which of the following options would improve the performance 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

Q7. Find the value of the Pearson’s correlation coefficient of X and Y from the data in the following table.

AGE (X)	GLUCOSE (Y)
43	99
21	65
25	79
42	75

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 orthogonal 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

Q10. Identify whether the following statement is true or false? “PCA can be used for projecting and visualizing data in lower dimensions.”

Answer: a. TRUE

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• Machine Learning. Tom Mitchell. First Edition, McGraw- Hill, 1997.
• Introduction to Machine Learning Edition 2, by Ethem Alpaydin

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Course Status :	Completed
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Start Date :	26 Jul 2021
End Date :	17 Sep 2021
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Exam Date :	26 Sep 2021 IST