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Class Activity



                        1.  Activity                                                              [CBSE Handbook]
                             Examine the following case studies. Draw the confusion matrix and calculate metrics such as accuracy,
                           precision, recall, and F1-score for each one of them.
                           Case Study 1
                             A spam email detection system is used to classify emails as either spam (1) or not spam (0). Out of 1000
                           emails:
                           • True Positives (TP): 150 emails were correctly classified as spam.
                           • False Positives (FP): 50 emails were incorrectly classified as spam.
                           • True Negatives (TN): 750 emails were correctly classified as not spam.
                           • False Negatives (FN): 50 emails were incorrectly classified as not spam.
                           Case Study 2
                             A fraud detection system is used to identify fraudulent transactions (1) from legitimate ones (0). Out of
                           1000 transactions:
                           •  True Positives (TP): 80 transactions were correctly identified as fraudulent.
                           •  False Positives (FP): 30 transactions were incorrectly identified as fraudulent.
                           •  True Negatives (TN): 850 transactions were correctly identified as legitimate.
                           •  False Negatives (FN): 40 transactions were incorrectly identified as legitimate.
                           Case Study 3
                             A medical diagnosis system is used to classify patients as having a certain disease (1) or not having it (0).
                           Out of 1000 patients:
                           •  True Positives (TP): 120 patients were correctly diagnosed with the disease.
                           •  False Positives (FP): 20 patients were incorrectly diagnosed with the disease.
                           •  True Negatives (TN): 800 patients were correctly diagnosed as not having the disease.
                           •  False Negatives (FN): 60 patients were incorrectly diagnosed as not having the disease.
                           Case Study 4
                           An inventory management system is used to predict whether a product will be out of stock
                           (1) or not (0) in the next month. Out of 1000 products:
                           •  True Positives (TP): 100 products were correctly predicted to be out of stock.
                           •  False Positives (FP): 50 products were incorrectly predicted to be out of stock.
                           •  True Negatives (TN): 800 products were correctly predicted not to be out of stock.
                           •  False Negatives (FN): 50 products were incorrectly predicted not to be out of stock.





              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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