There are various statistical parameters, especially metrics, that are rather confounding. Here is a list as a quick reference.

Evaluation metrics of a binary predictive model

  • Sensitivity, a.k.a., Recall, True positive rate (TPR)
\[\mathrm{Sensitivity} = \frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}}\]
  • Specificity
\[\mathrm{Specificity} = \frac{\mathrm{TN}}{\mathrm{TN}+\mathrm{FP}}\]
  • False positive rate, FPR
\[\mathrm{FPR} = \frac{\mathrm{FP}}{\mathrm{TN}+\mathrm{FP}} = 1 - \mathrm{Specificity}\]
  • Positive predict value, PPV; a.k.a., Precision
\[\mathrm{PPV} = \frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}}\]
  • Negative predict value, NPV
\[\mathrm{NPV} = \frac{\mathrm{TN}}{\mathrm{TN}+\mathrm{FN}}\]
  • Likelihood radios, LR+, LR-
\[\mathrm{LR}^+ = \frac{\mathrm{Sensitivity}}{1-\mathrm{Specificity}} = \frac{\mathrm{TP}(\mathrm{TN}+\mathrm{FP})}{\mathrm{FP}(\mathrm{TP}+\mathrm{FN})}\] \[\mathrm{LR}^- = \frac{1-\mathrm{Sensitivity}}{\mathrm{Specificity}} = \frac{\mathrm{FN}(\mathrm{TN}+\mathrm{FP})}{\mathrm{TN}(\mathrm{TP}+\mathrm{FN})}\]
  • Accuracy
\[\mathrm{Accuracy} = \frac{\mathrm{TP}+\mathrm{TN}}{\mathrm{P}+\mathrm{N}}\]
  • Prevalence
\[\mathrm{Prevalence} = \frac{\mathrm{TP}+\mathrm{FN}}{\mathrm{P}+\mathrm{N}}\]
  • Precision ==> PPV
\[\mathrm{Precision} = \frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}}\]
  • Recall ==> Sensitivity
\[\mathrm{Recall} = \frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}}\]
  • $F_1$ score, the harmonic mean of Precision and Recall.
\[\begin{aligned} F_1 &= 2\times\frac{\mathrm{Precision}\times\mathrm{Recall}}{\mathrm{Precision}+\mathrm{Recall}}\\ & = \frac{2\times\mathrm{TP}}{2\times\mathrm{TP}+\mathrm{FP}+\mathrm{FN}} \end{aligned}\]