# NPTEL Data Mining Assignment 5 Answers 2023

Hello NPTEL Learners, In this article, you will find NPTEL Data Mining Assignment 5 Week 5 Answers 2023. All the Answers are provided below to help the students as a reference donâ€™t straight away look for the solutions, first try to solve the questions by yourself. If you find any difficulty, then look for the solutions.

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## NPTEL Data Mining Assignment 5 Answers 2023:

#### Q.1. Support vector machine is:

• A. Maximum aprori classifier
• B. Maximum margin classifier
• C. Minimum apriori classifier
• D. Minimum margin classifier

#### Q.2. Support vectors in SVM are:

• A. Outliers
• B. Subset of testing data points
• C. Subset of training data points
• D. Random points in the data set

#### Q.3. In a hard margin support vector machine:

• A. No training instances lie inside the margin
• B. All the training instances lie inside the margin
• C. Only few training instances lie inside the margin
• D. None of the above

#### Q.4. The Lagrange multipliers corresponding to the support vectors have a value:

• A. equal to zero
• B. less than zero
• C. greater than zero
• D. can take on any value

#### Q.9. What would be the class if 7-NN is used?

• A. Genetic programming
• B. Neural programming
• C. Dynamic programming

#### Q.10. Slack variables are used in which of the below:

• A. Soft margin SVM
• B. Hard margin SVM
• C. Both in Soft margin SVM and Hard margin SVM
• D. Neither in Soft margin SVM nor in Hard margin SVM
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Disclaimer: This answer is provided by us only for discussion purpose if any answer will be getting wrong donâ€™t blame us. If any doubt or suggestions regarding any question kindly comment. The solution is provided byÂ Chase2learn. This tutorial is only for Discussion andÂ LearningÂ purpose.

#### About NPTEL Data Mining Course:

Data mining is study of algorithms for finding patterns in large data sets. It is an integral part of modern industry, where data from its operations and customers are mined for gaining business insight. It is also important in modern scientific endeavors. Data mining is an interdisciplinary topic involving, databases, machine learning and algorithms. The course will cover the fundamentals of data mining. It will explain the basic algorithms like data preprocessing, association rules, classification, clustering, sequence mining and visualization. It will also explain implementations in open source software. Finally, case studies on industrial problems will be demonstrated.

#### Course Layout:

• Week 1:Â Introduction, Data PreprocessingÂ
• Week 2:Â Association Rule Mining, Classification Basics
• Week 3:Â Decision Tree, Bayes Classifier, K nearest neighborÂ
• Week 4:Support Vector Machine, Kernel MachineÂ
• Week 5:Â Clustering, Outlier detectionÂ
• Week 6:Â Sequence miningÂ
• Week 7:Â Evaluation, Visualization.Â
• Week 8:Â Case studiesÂ
###### CRITERIA TO GET A CERTIFICATE:

Average assignment score = 25% of 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 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.

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