NPTEL Learning Analytics Tools Week 9 Assignment Answers 2024

NPTEL Learning Analytics Tools Week 9 Assignment Answers 2024

1. A student takes an exam in two subjects. Given that he has passed one of the subjects, what is the probability that he has passed both subjects?

• 0.75
• 0.50
• 0.25
• 0.33

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2. Which of the following statements about decision trees are correct?

• It requires the normalization of data
• It does not require the normalization of data
• Missing Values are not important
• A decision tree does not need a root node always

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3. “ The Decision tree is a non-linear classifier.”

• True
• False

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4. Overfitting and increase in the tree complexity can be overcome through the process called ___________.

• Normalization
• Branching
• Pruning
• Classification

Answer :- 

5. Consider the following statements-
A) Naive Bayes assumes independence among predictors.
B) Naive Bayes can perform multi-class prediction.

Which of the following statements is correct?

• Both a and b
• Only a
• Only b
• Neither a nor b

Answer :- 

Consider the data provided in the table below and using the Naive Bayes classifier formula, answer the following questions: Q.6 & Q.7

6. What is the probability that a student with 41-60% attendance will pass the exam?

• 2/5
• 4/5
• 3/5
• 1/5

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7. What is the probability that a 71-80% attendance student will fail the exam?

• 0
• 1/2
• 2/3
• 1/3

Answer :- 

Answer the questions 8, and 9 from the information given below:

A researcher is designing a decision tree classifier to classify students based on their exam performance, where scores greater than or equal to 50% are considered a pass, and scores less than 50% are considered a fail. The data is given in the table below:

8. Find the entropy of the target column.

• 0.92
• 0.82
• 0.72
• 0.6

Answer :- 

9. Calculate the ‘information gain’ for the parameter ‘Attendance in %’.

• 1
• 0.75
• 0.5
• 0.25

Answer :- 

10. Why is the Naive Bayes classifier called ‘Naive’?

• The classifier can solve only a very limited number of problems under multiple conditions
• Its use is limited to the domains of Natural Language Processing and Learning Analytics
• It assumes that the features of input space are strongly independent
• It assumes that the features of input space are strongly dependent.

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