This is the number of correct classifications divided by the total number of classifications. As already outlined for calculating the ROC it is straightforward to calculate the number of true positives. The number of true negatives was taken to be the number of edges from the list "chosen false edges" that were found in the set of edges with a likelihood ratio lower than the chosen threshold. It is possible to calculate the accuracy, for a range of thresholds, and compare which threshold returns the highest accuracy, assuming that everything above the threshold is classified as a positive and everything beneath it as a negative. In this work the impact on the accuracy of taking progressively larger parts of the results (working in decreasing order of likelihood ratio) as positives was tested.