Introduction to Machine Learning Week 7 NPTEL Assignment Answers 2025

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📌 Subject: Machine Learning
📌 Week: 7
📌 Session: NPTEL 2025
📌 Course Link: NPTEL Machine Learning
📌 Reliability: Expert-reviewed answers

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Machine Learning Week 1 Answers
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Machine Learning Week 6 Answers
Machine Learning Week 7 Answers
Machine Learning Week 8 Answers
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Machine Learning Week 10 Answers
Machine Learning Week 11 Answers
Machine Learning Week 12 Answers

Introduction to Machine Learning Week 7 NPTEL Assignment Answers 2025

1. Define active learning:

• A learning approach where the algorithm passively receives all training data at once
• A technique where the model learns from its own predictions without human intervention
• An iterative learning process where the model selects the most informative data points for labeling
• A method where the model randomly selects data points for training to reduce bias

Answer :- c

2. Given 100 distinct data points, if you sample 100 times with replacement, what is the expected number of distinct points you will obtain?

• Approximately 50
• Approximately 63
• Exactly 100
• Approximately 37

Answer :- b

3. What is the key difference between bootstrapping and cross-validation?

• Bootstrapping uses the entire dataset for training, while cross-validation splits the data into subsets
• Cross-validation allows replacement, while bootstrapping does not
• Bootstrapping creates multiple samples with replacement, while cross-validation creates subsets without replacement
• Cross-validation is used for model selection, while bootstrapping is only used for uncertainty estimation

Answer :- c

For the Week 8 answers: Introduction to Machine Learning Week 8 NPTEL Assignment Answers

4. Consider the following confusion matrix for a binary classification problem:

What are the precision, recall, and accuracy of this classifier?

• Precision: 0.81, Recall: 0.85, Accuracy: 0.83
• Precision: 0.85, Recall: 0.81, Accuracy: 0.85
• Precision: 0.80, Recall: 0.85, Accuracy: 0.82
• Precision: 0.85, Recall: 0.85, Accuracy: 0.80

Answer :- a

5. AUC for your newly trained model is 0.5. Is your model prediction completely random?

• Yes
• No
• ROC curve is needed to derive this conclusion
• Cannot be determined even with ROC

Answer :- c

6. You are building a model to detect cancer. Which metric will you prefer for evaluating your model?

• Accuracy
• Sensitivity
• Specificity
• MSE

Answer :- b

7. You have 2 binary classifiers A and B. A has accuracy=0% and B has accuracy=50%. Which classifier is more useful?

• A
• B
• Both are good
• Cannot say

Answer :- a

8. You have a special case where your data has 10 classes and is sorted according to target labels. You attempt 5-fold cross validation by selecting the folds sequentially. What can you say about your resulting model?

• It will have 100% accuracy.
• It will have 0% accuracy.
• It will have close to perfect accuracy.
• Accuracy will depend on the compute power available for training.

Answer :- b

Conclusion:

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