Introduction to Machine Learning Week 7 NPTEL Assignment Answers 2025

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✅ Subject: Introduction to Machine Learning (nptel ml Answers)
📅 Week: 7
🎯 Session: NPTEL 2025 July-October
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NPTEL Introduction to Machine Learning Week 7 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 : See Answers

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 : See Answers

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 :

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 :

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 :

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

• Accuracy
• Sensitivity
• Specificity
• MSE

Answer :

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 :

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 : See Answers

NPTEL Introduction to Machine Learning Week 7 Assignment Answers 2024

1. Which of the following statement(s) regarding the evaluation of Machine Learning models is/are true?

• A model with a lower training loss will perform better on a test dataset.
• The train and test datasets should represent the underlying distribution of the data.
• To determine the variation in the performance of a learning algorithm, we generally use one training set and one test set.
• A learning algorithm can learn different parameter values if given different samples from the same distribution.

Answer :- b, d

2. Suppose we have a classification dataset comprising of 2 classes A and B with 100 and 50 samples respectively. Suppose we use stratified sampling to split the data into train and test sets. Which of the following train-test splits would be appropriate?

• Train- {A:80samples,B:30samples}, Test- {A:20samples,B:20samples}
• Train- {A:20samples,B:20samples}, Test- {A:80samples,B:30samples}
• Train- {A:80samples,B:40samples}, Test- {A:20samples,B:10samples}
• Train- {A:20samples,B:10samples}, Test- {A:80samples,B:40samples}

Answer :- c

3. Suppose we are performing cross-validation on a multiclass classification dataset with N data points. Which of the following statement(s) is/are correct?

• In k-fold cross validation, each fold should have a class-wise proportion similar to the given dataset.
• In k-fold cross-validation, we train one model and evaluate it on the k different test sets.
• In LOOCV, we train N different models, using (N-1) data points for training each model.
• In LOOCV, we can use the same test data to evaluate all the trained models.

Answer :- a, c, 

7. Consider the following statements.
Statement P : Boosting takes multiple weak classifiers and combines them into a strong classifier.
Statement Q : Boosting assigns equal weights to the predictions of all the weak classifiers, resulting in a high overall performance.

• P is True. Q is True. Q is the correct explanation for A.
• P is True. Q is True. Q is not the correct explanation for A.
• P is True. Q is False.
• Both P and Q are False.

Answer :- c

8. Which of the following statement(s) about ensemble methods is/are correct?

• The individual classifiers in bagging cannot be trained parallelly.
• The individual classifiers in boosting cannot be trained parallelly.
• A committee machine can consist of different kinds of classifiers like SVM, decision trees and logistic regression.
• Bagging further increases the variance of an unstable classifier.

Answer :- a, c