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○   Model that predicts whether a mail is spam or not. If the model always predicts that the mail is spam, people
                    would not look at it and eventually might lose important information. Missing some important mail might
                    create a problem.
              Based on different examples we can say, that both Recall and Precision are important. In some cases, you might
              have a High Precision but Low Recall or Low Precision but High Recall. So, we need an important measure that
              considers  both  Recall  and  Precision  for  the  good  and accurate performance of  an  AI  model.  This  problem  is
              resolved by considering F1 Score.

                       F1 Score


              F1 score, also called F-score or F-measure, is a metric used to evaluate the accuracy of a test. It can be defined
              as the measure of balance between precision and recall. The F1 score ranges between 0 and 1, where 1 indicates
              perfect Precision and Recall, and 0 indicates a complete failure.
              A high F1 score means the model has low False Positives (FP) and low False Negatives (FN)—meaning it correctly
              identifies real cases and minimizes false alarms. It is particularly useful in real-life classification problems, especially
              when dealing with imbalanced datasets (where one class is much more frequent than the other).
                                                                 Precision × Recall
                                                  F1 Score = 2 ×
                                                                 Precision + Recall

              A model is said to have a good performance if the F1 Score for that model is high.
              An ideal situation occurs when both Precision and Recall have value as 1 i.e., 100%, then F1 score would also be an
              ideal 1 (100%). It is also known as the perfect value for F1 Score.
              A model is considered to be a total failure when the F1 score is 0.
              As the values of both Precision and Recall range from 0 to 1, the F1 score also ranges from 0 to 1.

              The different possibility of the F1 Score is:
                 • If Precision = Low and Recall = Low then F1 Score will be Low.
                 • If Precision = Low and Recall = High then F1 Score will be Low.
                 • If Precision = High and Recall = Low then F1 Score will be Low.

                 • If Precision = High and Recall = High then F1 Score will be High.
              CASE STUDY: Availability of School Transport


              In schools, a lot of times it happens that there is no transport facility available to commute. Such 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:


                                                                                      Prediction
                                 Confusion Matrix
                                                                             Yes                       No

                                                    Yes                       22                       47
                          Actual
                                                    No                        12                       18









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