Introduction To Machine Learning IIT-KGP Nptel Week 5 Assignment Answers

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Introduction To Machine Learning IIT-KGP Nptel Week 5 Assignment Answers

Introduction To Machine Learning IIT-KGP Week 5 Answers (July-Dec 2024)

Q1.What would be the ideal complexity of the curve which can be used for separating the two classes shown in the image below?

A) Linear
B) Quadratic
C) Cubic
D) insufficient data to draw conclusion

Answer: A) Linear

Q2.Suppose you have a dataset with n=10 features and m=1000 examples. After training a logistic regression classifier with gradient descent, you find that it has high training error and does not achieve the desired performance on training and validation sets. Which of
the following might be promising steps to take?

Use SVM with a non-linear kernel function
Reduce the number of training examples
Create or add new polynomial features

A) 1,2

B) 1,3

c)1,2,3

D) None

Answer: B) 1,3

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These are Introduction To Machine Learning IIT-KGP Week 5 Answers

Q3.In logistic regression, we learn the conditional distribution p(y|x), where y is the class label and x is a data point. If h(x) is the output of the logistic regression classifier for an input x, then p(y|x) equals:

A.
B.
C.
D.

Answer A.

Q4.The output of binary class logistic regression lies in the range:
A. [-1.0]
B. [0.1]
C. [-1.2]
D. [1.10]

Answer: B. [0.1]

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Q5.State whether True or False.
“After training an SVM, we can discard all examples which are not support
vectors and can still classify new examples.”
A) TRUE
B) FALSE

Answer: A) TRUE

Q6 Suppose you are dealing with a 3-class classification problem and you want to train a SVM model on the data. For that you are using the One-vs-all method. How many times do we need to train our SVM model in such a case?

A) 1

B) 2

c)3

D) 4

Answer: c)3

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Q7What is/are true about kernels in SVM?

1.Kernel function can map low dimensional data to high dimensional space

2.It’s a similarity function
A)1
B)2
C)1and2
D) None of these.

Answer: C)1and2

Q8.If g(z) is the sigmoid function, then its derivative with respect to z may be written in term of g(z) as

A) g(z)(g(z)-1)

B) g(2)(1+g(z))

C)-g(2)(1+g(z))

D)g(2)(1-g(z))

Answer: D)g(2)(1-g(z))

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Q9.Below are the labelled instances of 2 classes and hand drawn decision boundaries for logistic regression. Which of the following figures demonstrates overfitting of the training data?
A) A
B) B
C) C
D) None of these

Answer: C) C

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Q10.What do you conclude after seeing the visualization in the previous question (Question9)?
C1. The training error in the first plot is higher as compared to the second and third plot.
C2. The best model for this regression problem is the last (third) plot because it
has minimum training error (zero).
C3. Out of the 3 models, the second model is expected to perform best on
unseen data.
C4. All will perform similarly because we have not seen the test data.
A)C1and C2
B)C1and C3
C)C2and C3
D)C4

Answer: B)C1and C3

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