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NPTEL Introduction to Machine Learning Assignment 4 Answers 2022
- by QuizXp Team
- February 11, 2022 February 23, 2022
Are you looking for the Answers to NPTEL Introduction to Machine Learning Assignment 4 – IIT Madras? This article will help you with the answer to the Nation al Programme on Technology Enhanced Learning ( NPTEL ) Course “ NPTEL Introduction to Machine Learning Assignment 4 “
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.
Below you can find the answers for NPTEL Introduction to Machine Learning Assignment 4
NPTEL Introduction to Machine Learning Assignment 4 Answers:-
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 )=[1 x 2]
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.
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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.
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Introduction To Machine Learning
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