# Table 4 Performance metrics used to evaluate classifiers

Performance metric Calculation and description
Sensitivity (SENS) TP/(TP + FN)
Measures the ability of a QSAR tool to detect Ames positives compounds correctly.
Specificity (SPEC) TN/(TN + FN)
Measures the ability for a QSAR tool to detect negatives compounds.
Accuracy (ACC) (TP + TN)/(TP + TN + FP + FN)
Assesses a QSAR tool’s overall performance by returning the fraction of compounds which were correctly predicted.
Balanced Accuracy (BA) (SENS + SPEC)/2
Assesses the overall model performance, giving each class equal weight.
Positive Prediction Value (PPV) (TP)/(TP + FP)
Indicates how frequently positive predictions are correct.
Negative Prediction Value (NPV) TN/(TN + FN)
Indicates how often negative predictions are correct.
Mathews Correlation Coefficient (MCC) $$\frac{\left(\left( TP\ast TN\right)-\left( FP\ast FN\right)\right)}{\sqrt{\left( TP+ FP\left)\left( TP+ FN\right)\left( TN+ FP\right)\right( TN+ FN\right)}}$$
Assesses the overall performance of the model. Values can range from −1 to 1, which is in contrast to the other metrics in this table which range form 0 to 1.
Coverage (COV) (TP + TN + FP + FN)/(TP + TN + FP + FN + OOD)
Evaluates the proportion of compounds for which the model can make a positive or negative prediction. 