Page 150 - Ai_C10_Flipbook
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Predictions for 1 that were actually 1                                     Predictions for 0 that were actually 1
               appear in the cell. Implying prediction                                    appear in the cell. Implying prediction
              that the loan will be approved and the                                       that the loan will not be approved
                  loan was approved actually.                           Predicted         and the loan was approved actually.
                                                Confusion Matrix
                                                                      Yes       No

                                                            Yes       20        10
                                                Actual
                                                            No        15         5
               Predictions for 1 that were actually 0                                     Predictions for 0 that were actually 0
               appear in the cell. Implying prediction                                   appear in the cell. Implying prediction
               that the loan will be approved and the                                    that the loan will not be approved and
                 loan was not approved actually.                                          the loan was not approved actually.




                              Brainy Fact


                   Mammography is the most effective way to detect breast cancer, but it can miss it 20% of the time. AI can help
                   improve the accuracy of breast cancer detection and prediction. An AI model developed by researchers at MIT’s
                   Computer Science and Artificial Intelligence Laboratory (CSAIL) and Jameel Clinic for Machine Learning. Mirai is
                   a complex neural network that can detect breast cancer up to five years before it occurs.



              Build the Confusion Matrix


              To build the confusion matrix, we manually compare the predicted labels with the actual labels and categorise the
              results into True Positives, True Negatives, False Positives, and False Negatives.
              For example, predicting the possibility of snowfall. Here, Yes would mean there will be snowfall, and No would
              mean that there will be no snowfall. So, the AI model will have output as Yes or No.
              The following table shows the actual values and the predicted values. Let’s fill the given matrix based on the table
              given here.

                           Predicted Value        Actual Value

                                 Yes                  Yes
                                 No                   Yes

                                 Yes                   No

                                 No                    No
                                 Yes                  Yes

                                 Yes                   No

                                 No                   Yes                                     Predicted
                                                                       Confusion Matrix
                                 Yes                  Yes                                    Yes       No

                                 No                    No                          Yes
                                                                       Actual
                                 No                    No                          No


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