# Introduction to Machine Learning NPTEL Week 4 Solutions NPTEL 2023

This set of MCQ(multiple choice questions) focuses on the Introduction to Machine Learning NPTEL Week 4 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 4 Solutions

Q1. Consider a Boolean function in three variables, that returns True if two or more variables out of three are True, and False otherwise. Can this function be implemented using the perceptron algorithm?

Â a. no
b.Â yes

Q2. For a support vector machine model, letÂ xiÂ be an input instance with labelÂ yiÂ . IfÂ yi(Î²^0+xTiÎ²^)>1(Î²^0+Î²^)>1, whereÂ Î²0Î²0Â andÂ Î²^)Î²^)Â are the estimated parameters of the model, then

a. xiÂ is not a support vector
b. xi is a support vector
c. xiÂ is either an outlier or a support vectorÂ
d. Depending upon other data points, x i may or may not be a support vector.

Q3. CSuppose 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?

a. yes
b. no

Introduction to Machine Learning NPTEL Week 4 Solutions

Q4. 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:Â Î²0,Î²1,Î²2,Î²3Î²0,Î²1,Î²2,Î²3Â . (You can round-off the accuracy value to the nearest 2-decimal point number.)

a. -1.2, 2.1, 2.2, 1Â
b. 1, 1.2, 2.1, 2.2Â
c. -1, 1.2, 2.1, 2.2Â
d. 1, -1.2, 2.1, 2.2Â
e. 1, 1.2, -2.1, -2.2

Q5. 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:

a. increaseÂ
b. first increase then decreaseÂ
c. decreaseÂ
d. first decrease then increase

Introduction to Machine Learning NPTEL Week 4 Solutions

Q6. Train anÂ l22Â regularized logistic regression classifier on the modified iris dataset. We recommend using sklearn. Use only the first two features for your model. We encourage you to explore the impact of varying different hyperparameters of the model. Kindly note that theÂ C parameter mentioned below is the inverse of the regularization parameterÂ Î»Î». As part of the assignment train a model with the following hyperparameters:
Model: logistic regression with one-vs-rest classifier,Â C=1e4=14
For the above set of hyperparameters, report the best classification accuracy

a. 0.88Â
b. 0.86Â
c. 0.98Â
d. 0.68

Introduction to Machine Learning NPTEL Week 4 Solutions

Q7. Train an SVM classifier on the modified iris dataset. We recommend using sklearn. Use only the first two 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,Â gamma=0.5=0.5, one-vs-rest classifier, no-feature-normalization. TryÂ C=0.01,1,10=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.

a.Â 0.92, 69Â
b. 0.88, 40Â
c. 0.88, 69Â
d. 0.98, 41

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