In other words, specificity measures how the test is effective when used on negative individuals. ![]() Specificity (also called True Negative Rate): proportion of negative cases that are well detected by the test.The mathematical definition is given by: Sensitivity = TP/(TP + FN). If it is below 0.5, the test is counter-performing and it would be useful to reverse the rule so that sensitivity is higher than 0.5 (provided that this does not affect the specificity). The test is perfect for positive individuals when sensitivity is 1, equivalent to a random draw when sensitivity is 0.5. In other words, the sensitivity measures how the test is effective when used on positive individuals. Sensitivity (equivalent to the True Positive Rate): Proportion of positive cases that are well detected by the test.The ROC curve corresponds to the graphical representation of the couple (1 – specificity, sensitivity) for the various possible threshold values. ![]() The ROC curve generated by XLSTAT allows to represent the evolution of the proportion of true positive cases (also called sensitivity) as a function of the proportion of false positives cases (corresponding to 1 minus specificity), and to evaluate a binary classifier such as a test to diagnose a disease, or to control the presence of defects on a manufactured product.
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