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C. Competency-based/Application-based questions. 21 st Century #Critical Thinking
Skills
1. A credit scoring model is used to predict whether an applicant is likely to default on a loan (1) or not (0). Out of 1000
loan applicants: [CBSE Handbook]
True Positives(TP): 90 applicants were correctly predicted to default on the loan.
False Positives(FP): 40 applicants were incorrectly predicted to default on the loan.
True Negatives(TN): 820 applicants were correctly predicted not to default on the loan.
False Negatives (FN): 50 applicants were incorrectly predicted not to default on the loan.
Calculate metrics such as accuracy, precision, recall, and F1-score.
Assertion and Reasoning questions.
Direction: Questions 2-4, consist of two statements – Assertion (A) and Reasoning (R). Answer these questions by selecting
the appropriate option given below:
a. Both A and R are correct, and R is the correct explanation of A.
b. Both A and R are correct, but R is NOT the correct explanation of A.
c. A is correct, but R is incorrect.
d. A is incorrect, but R is correct.
2. Assertion (A): A model with high accuracy always performs well on all types of classification problems.
Reasoning(R): Accuracy is a reliable metric for evaluating model performance in all scenarios.
3. Assertion (A): Bias in training data can lead to unfair predictions in AI models.
Reasoning (R): If the training dataset lacks diversity, the model may learn and reinforce existing biases.
4. Assertion (A): Accuracy is an evaluation metric that allows you to measure the total number of predictions a model
gets right.
Reasoning (R): The accuracy of the model and performance of the model is directly proportional, and hence better the
performance of the model, the more accurate are the predictions. [CBSE Handbook]
21 st Century #Technology Literacy
Skills
Lab
1. Explore the internet to find some scenarios related to natural disasters. Take any one out of them and
make prediction-reality comparison. Draw a confusion matrix for it to show prediction results.
2. For the scenario in the above question, make evaluation through its parameters_ Accuracy, Precision and
Recall.
3. Calculate the following measures for the given confusion matrix - Accuracy, Precision, Recall and F1 Score.
Confusion Matrix True Positive True Negative
Predictive Positive 100 45
Predicted Negative 65 320
Answers
Exercise (Section A)
A. 1. d 2. c 3. a 4. b 5. c 6. a 7. a 8. b 9. d 10. b
11. a 12. c
B. 1. train-test split 2. underfitting 3. precision, recall 4. false
5. correctness 6. complex 7. False Negative (FN) 8. positive, positive
9. No of correct Prediction, Total no. of predictions 10. F1 score
C. 1. False 2. True 3. False 4. False 5. True
D. 1. d 2. a 3. e 4. b 5. c
164 Artificial Intelligence Play (Ver 1.0)-X

