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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

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