Page 315 - AI Ver 1.0 Class 10
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Here the reality is that there are no board exams to be conducted as they got cancelled due to the COVID-19
                 number of cases increasing drastically. But the machine has incorrectly predicted that there will be board exams
                 for the students of grade 12. This case is termed as False Positive.


                 Case 4: Is there a Board Exam?
























                                       Predicion:
                                          No                                              Reality: Yes
                                                                False Negative

                 Here, the board has decided to conduct examinations for grade 12 students because of which the Reality is Yes
                 but the machine has incorrectly predicted it as a No which means the machine predicts that there will be no board
                 exams. Therefore, this case becomes False Negative.



                         Confusion Matrix

                 Confusion matrix is a tabular structure which 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 in the form of 2X2 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 classes are being predicted correctly and incorrectly, and what type 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 help us in finding the 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 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)




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