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Case 4: Is There a Perfect Score?


                                                               Here, the reality is that there is a Perfect Score due to the
                                                               hard work  of students,  the  availability of online resources
                                                               and the right guidance at the coaching institutes. However,
                                                               the machine has incorrectly predicted that there will be no
                  Predicion: No                  Reality: Yes  Perfect Score for the students of grade 10. This case is termed
                                 False Negative                as False Positive.


              Confusion Matrix

              Confusion matrix is a tabular structure that helps in measuring the performance of an AI model using the test data.
              The table is made with 4 different combinations of predicted and actual values (reality) in the form of a 2×2 matrix.
              The comparison between the prediction and the reality can be used to analyse the rate of success. It also gives a
              clear picture of which cases are being predicted correctly and incorrectly, and what types of errors are being made.
              This matrix is also known as the Error Matrix and is used in situations where we need to evaluate the performance
              of the model, where it went wrong and helps us find ways to increase the efficiency of the model. It is useful for
              measuring recall, precision, accuracy and F1 score.

              The following confusion matrix table illustrates how the 4-classification metrics are calculated (TP, FP, FN, TN), and
              how our predicted value is compared to the actual value in a confusion matrix.

                                                                                        Reality
                                 Confusion Matrix
                                                                             Yes                       No

                                                    Yes                True Positive (TP)       False Positive (FP)
                        Prediction
                                                    No                False Negative (FN)      True Negative (TN)

              In the confusion matrix:

                 • The target variable has two values: Positive and Negative.
                 • The columns represent the actual values of the target variable.
                 • The rows represent the predicted values of the target variable.

              For example:
              In schools, a lot of times it happens that there is no transport facility available to commute. The unavailability of
              school transport is a very common and prominent problem. Hence, an AI model is designed to predict if there is
              going to be school transport available or not.

              The confusion matrix for the same is:

                                                                                        Reality
                                 Confusion Matrix
                                                                             Yes                       No
                                                    Yes                       22                       12
                        Prediction
                                                    No                        48                       18





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