from ( )we conclude that the accuracy of classification is equal to accuracy tp tn tp tn fp fn howeverwe can see that in this particular caseaccuracy is problematic metricsince the algorithm allowed non-authorized personnel to enter the facility one way to deal with this issue is to modify the loss function to give much higher loss to non-authorized access thusinstead of an (indicatorloss matrixwe could for example take the loss matrix an alternative approach is to keep the indicator loss function and consider additional classification metrics below we give list of commonly used metrics for simplicity we call an object whose actual class is -objectthe precision (also called positive predictive valueis the fraction of all objects classified as that are actually -objects specificallyprecision tp tp fp the recall (also called sensitivityis the fraction of all -objects that are correctly classified as such that istp recall tp fn the specificity measures the fraction of all nonj-objects that are correctly classified as such specificallytn specificity fp tn the fb score is combination of the precision and the recall and is used as single measurement for classifier' performance the fb score is given by fbj precision ( tp ( tp fn fp for we obtain the precision and for we obtain the recall the particular choice of metric is clearly application dependent for examplein the classification of authorized personnel in top-secret military facilitysuppose we have two classifiers the first (classifier has confusion matrix given in table and the second (classifier has confusion matrix given in table various metrics for these two classifiers are show in table in this case we prefer classifier which has much higher precision recall specificity fb score