where MW is the true molecular mass, and the symbols 𝑇𝑃, 𝐹𝑃, 𝐹𝑁, and 𝑇𝑁 indicate the number of true positives, false positives, false negatives and true negatives, respectively, observed in the test. From the error matrix, several common performance metrics can be calculated. The metrics considered in this manuscript are summarized in Table . Of most importance is the measure of precision, which estimates the probability that a prediction with index greater than the threshold is correct. A summary of classification performance metrics on the outlined tests sets is provided as Table , with the threshold value, denoted 𝜏_ for generality, chosen to maximize the computed accuracy of each method. In general, these results demonstrate that all three methods can be used independently with the prescribed threshold values to predict molecular mass of a molecule from EI mass spectra even without parameter optimization as discussed in section 2.2. Figure shows the standard receiver operating characteristic (ROC) curves for all three algorithms using test set 1 (19078 predictions), with threshold values assessed between 0.001 and 0.999 by increments of 0.001. The ROC curve for an ideal classifier would have a sharp point near the top left corner indicating the method had high specificity and recall--the plots in Figure make clear that all three classifiers show room for improvement. It is worth noting that if spectra of the analytes were included in the reference libraries, both the SS-HM and iHS-HM should show substantial improvements that mirror the effectiveness of compound identification with a library search when a reference spectrum of the unknown is in the library. Accordingly, expanding reference libraries will benefit the library-search based methods for predicting molecular mass. Of the three methods, the overall worst performing method in the large-scale studies was the iHS-HM. However, as shown in the examples of Section 3.1, there are scenarios where the iHS-HM correctly predicts molecular weight, and the other two methods fail. Accordingly, considering all three predictions simultaneously is a useful and recommended strategy.