## NPTEL Introduction to Machine Learning Week 7 Assignmeent Answers 2024

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

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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

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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

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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

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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

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

- Accuracy
- Sensitivity
- Specificity
- MSE

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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

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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.

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