# NPTEL Introduction to Machine Learning Assignment 4 Answers 2023

## 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.

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of the average of best 8 assignments out of the total 12 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF THE AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

## NPTEL Introduction To Machine Learning Week 4 Assignment Answer 2023

Q1. Consider the data set given below.

Claim: PLA (perceptron learning algorithm) can learn a classifier that achieves zero misclassification error on the training data. This claim is:

True
False
Depends on the initial weights
True, only if we normalize the feature vectors before applying PLA.

`Answer:- For Answer Click Here`

Q2. Which of the following loss functions are convex? (Multiple options may be correct)

• 0-1 loss (sometimes referred as mis-classification loss)
• Hinge loss
• Logistic loss
• Squared error loss
`Answer:- `

Q3. Which of the following are valid kernel functions?

• (1+ < x, x’ >)d
• tanℎ(K1<x,x’>+K2)
• exp(−γ||x−x’||2)
`Answer:- For Answer Click Here`

Q4. Consider the 1 dimensional dataset:

(Note: x is the feature, and y is the output)

State true or false: The dataset becomes linearly separable after using basis expansion with the following basis function ϕ(x)=[1x3]

• True
• False
`Answer:- `

Q5. State True or False:
SVM cannot classify data that is not linearly separable even if we transform it to a higherdimensional space.

• True
• False
`Answer:- For Answer Click Here`

Q6. State True or False:
The decision boundary obtained using the perceptron algorithm does not depend on the initial values of the weights.

• True
• False
`Answer:- `

Q7. Consider a linear SVM trained with n labeled points in R2 without slack penalties and resulting in k=2 support vectors, where n>100. By removing one labeled training point and retraining the SVM classifier, what is the maximum possible number of support vectors in the resulting solution?

• 1
• 2
• 3
• n − 1
• n
`Answer:- For Answer Click Here`

Q8. Consider an SVM with a second order polynomial kernel. Kernel 1 maps each input data point x to K1(x)=[x x2]. Kernel 2 maps each input data point x to K2(x)=[3x 3×2]. Assume the hyper-parameters are fixed. Which of the following option is true?

• The margin obtained using K2(x) will be larger than the margin obtained using K1(x).
• The margin obtained using K2(x) will be smaller than the margin obtained using K1(x).
• The margin obtained using K2(x) will be the same as the margin obtained using K1(x).
`Answer:- `

## NPTEL Introduction to Machine Learning Assignment 4 Answers July 2022

1. Consider the 1-dimensional dataset:

State true or false: The dataset becomes linearly separable after using basis expansion with the following basis function ϕ(x)=[1×3]ϕ(x)=[1×3]

• a. True
• b.False
`Answer:- b`

2. Consider a linear SVM trained with nn labeled points in R2R2 without slack penalties and resulting in k=2k=2 support vectors, where n>100n>100. By removing one labeled training point and retraining the SVM classifier, what is the maximum possible number of support vectors in the resulting solution?

a. 1
b. 2
c. 3
d. n − 1
e. n

`Answer:- d`

3. Which of the following are valid kernel functions?

a. (1+<x,x’>)d(1+<x,x′>)d
b. tanh(K1<x,x’>+K2)
c. exp(−γ||x−x’||2)

`Answer:- a, b, c`

4. Consider the following dataset:

Which of these is not a support vector when using a Support Vector Classifier with a polynomial kernel with degree =3,C=1,=3,C=1, and gamma =0.1?=0.1?
(We recommend using sklearn to solve this question.)

a. 3
b.1
c. 9
d. 10

`Answer:- b`

5. Consider an SVM with a second order polynomial kernel. Kernel 1 maps each input data point xx to K1(x)=[x x2]. Kernel 2 maps each input data point xx to K2(x)=[3x 3×2]K2(x). Assume the hyper-parameters are fixed. Which of the following option is true?

a. The margin obtained using K2(x)K2(x) will be larger than the margin obtained using K1(x)K1(x).
b. The margin obtained using K2(x)K2(x) will be smaller than the margin obtained using K1(x)K1(x).
c. The margin obtained using K2(x)K2(x) will be the same as the margin obtained using K1(x)K1(x).

`Answer:- a`

6. Train a Linear perceptron classifier on the modified iris dataset. Report the best classification accuracy for l1 and elasticnet penalty terms.

(We recommend using sklearn.)

a. 0.82, 0.64
b. 0.90, 0.71
c. 0.84, 0.82
d. 0.78, 0.64

`Answer:- c`

7. Train an SVM classifier on the modified iris dataset. We encourage you to explore the impact of varying different hyperparameters of the model. Specifically, try different kernels and the associated hyperparameters. As part of the assignment, train models with the following set of hyperparameters
poly, gamma=0.4gamma=0.4, one-vs-rest classifier, no-feature-normalization.

a. 0.98
b. 0.96
c. 0.92
d. 0.94

`Answer:- b`

NPTEL Introduction to Machine Learning Assignment 4 Answers 2022:- In This article, we have provided the answers of Introduction to Machine Learning Assignment 4

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.

## NPTEL Introduction to Machine Learning Assignment 4 Answers Jan 2022

Q1. Consider the 1 dimensional dataset:

State true or false: The dataset becomes linearly separable after using basis expansion with the following basis function ϕ(x)=[1x2]

Q2. Consider the data set given below.

Claim: PLA (perceptron learning algorithm) can learn a classifier that achieves zero misclassification error on the training data. This claim is:

Q3. Which of the following is the correct definition for the neural network kernel?

Q4. State True or False
SVM cannot classify data that is not linearly separable even if we transform it to a higher- dimensional space.

Q5. Consider the following dataset:

Which of these is not a support vector when using a Support Vector Classifier with a polynomial kernel with degree = 3, C = 1, and gamma = 0.1?

Q6. State True or False:

The decision boundary obtained using the perceptron algorithm does not depend on the initial values of the weights.

Q7. Train a Linear perceptron classifier on the modified iris dataset. We recommend using sklearn. Use only the first two features for
your model and report the best classification accuracy for l1 and l2 penalty terms.

Q8. Train a SVM classifier on the modified iris dataset. We recommend using sklearn. Use only the first three features. We encourage
you to explore the impact of varying different hyperparameters of the model. Specifically try different kernels and the associated
hyperparameters. As part of the assignment train models with the following set of hyperparameters RBF-kernel, gamma = 0.5, one-vs-rest classifier,
no-feature-normalization.

Try C = 0.01, 1, 10. For the above set of hyperparameters, report the best classification accuracy.

NPTEL Introduction to Machine Learning Assignment 4 Answers 2022:- In This article, we have provided the answers of Introduction to Machine Learning Assignment 4

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.