Are you looking for NPTEL Introduction to Machine Learning Assignment Week 4 Answers? If yes, you will find the answers to the questions asked in the NPTEL Introduction to Machine Learning quiz exam here. If you are preparing for this exam this article will help you in finding the latest and updated answers.

There is a total of 6 questions related to Perceptron, Support Vector Machines. The correct answers are marked in Green Color with a tick sign.

Note: If the questions in the exam is not same/changed please share them with us, so that we update with the latest questions & answers

NPTEL Introduction to Machine Learning Assignment Week 4 Answers

1. Consider the data set given below. Can we use perceptron learning algorithm to build a model using only the given features that achieves zero misclassification error on the training data?

Can we use perceptron learning algonithm to build a model using only the given features that achieves zero misclassification error on the training data?
  1. Yes
  2. No
  3. Depends on the initial weights

2. Suppose we use a linear kernel SVM to build a classifier for a 2-class problem where the training data points are linearly separable. In general, will the classifier trained in this manner be always the same as the classifier trained using the perceptron training algorithm on the same training data?

  1. Yes
  2. No

3. Train a linear regression model (without regularization) on the above dataset. Report the coefficients of the best fit model. Report the coefficients in the following format β0, β1, β2, β3 (You can round off the accuracy value to the nearest 2-decimal point number.)

  1. -1.2, 2.1, 2.2, 1
  2. 1, 1.2, 2.1, 2.2
  3. -1, 1.2, 2.1, 2.2
  4. 1, -1.2, 2.1, 2.2
  5. 1, 1.2, -2.1, -2.2

4. Train an l2 regularized linear regression model on the above dataset. Vary the regularization parameter from 1 to 10. As you increase the regularization parameter, absolute value of the coefficients (excluding the intercept) of the model:

  1. increase
  2. first increase then decrease
  3. decrease
  4. first decrease then increase
  5. does not change

5. Train an l2 regularized logistic regression classifier on the modified iris dataset. We recommend using sklearn. Use only the first three features for your model. We encourage you to explore the impact of varying different hyperparameters of the model. Kindly note that the C parameter mentioned below is the inverse of the regularization parameter λ. As part of the assignment train a model with the following hyperparameters:

Model: logistic regression with one-vs-rest classifier, C = 1e4
For the above set of hyperparameters, report the best classification accuracy

  1. 0.88
  2. 0.86
  3. 0.98
  4. 0.68

6 Train an SVM classifier on the modified iris dataset. We recommend using sklearn. Use only the first three features for your model. 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 along with total number of support vectors on the test data.

  1. 092, 69
  2. 0.88, 40
  3. 0.88, 69
  4. 0.98, 41
NPTEL Introduction to Machine Learning Assignment Answers

FAQ

What is NPTEL Introduction to Machine Learning?

NPTEL Introduction to Machine Learning Course is an online free course by IIT Madras that has been developed by Prof. Balaraman Ravindran. The main aim of this course is to provide the basic concepts of machine learning from a mathematically well-motivated perspective.

Are These Answers Correct?

Yes, all these answers are 100% correct.

Are These Answers Updated?

Yes, these answers are up to date with the latest questions.

Will I Get a Certificate?

Yes, you will get a certificate but it costs ₹1000 exam fee.

Wrap Up

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Disclaimer: These Answers are only for educational purposes so please don’t use them for any cheating purposes. We urge you to do assignments on your own.

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