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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.


                       Terminologies of Confusion Matrix


              To understand the confusion matrix let’s understand the following terms:
                 • Positive: The prediction is positive for the scenario. For example, there will be board exams.

                 • Negative: The prediction is negative for the scenario. For example, there will be no board exams conducted this
                year.

                 • True Positive: The predicted value matches the actual value i.e.; the actual value was positive and the model
                predicted a positive value.
                 • True Negative: The predicted value matches the actual value i.e.; the actual value was negative and the model
                predicted a negative value.

                 • False Positive (Type 1 error): The predicted value was falsely predicted i.e.; the actual value was negative but
                the model predicted a positive value.
                 • False Negative (Type 2 error): The predicted value was falsely predicted i.e.; the actual value was positive but
                the model predicted a negative value.


                       Evaluation Matrix for AI Model


              There are different evaluation matrices available to check the performance of the AI model. Let us study about the
              commonly used Evaluation matrices.

              Accuracy
              Accuracy is the percentage of correct prediction out of the total observations made in an AI model. It gives you a
              clear picture on how accurate is the prediction for the given AI Model. High Accuracy means good performance of
              the AI model as accuracy counts all of the true predicted values. Its mathematical formula is:


                                                            Correct prediction
                                                 Accuracy =                    × 100
                                                                Total cases

                                                                 (TP + TN)
                                                  Accuracy =                    × 100%
                                                            (TP + TN + FP + FN)
              Where,

              Correct prediction = Total Positive (TP) + Total Negative (TN)
              Total cases = Total Positive (TP) + Total Negative (TN) + False Positive (FP) + False Negative (FN)
              A prediction is said to be correct if it matches reality. Here we have two conditions in which the Prediction matches
              with the Reality, i.e., True Positive and True Negative.







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