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
$\mathrm{NPV} = \frac{\mathrm{TN}}{\mathrm{TN}+\mathrm{FN}}$
$\mathrm{LR}^+ = \frac{\mathrm{Sensitivity}}{1-\mathrm{Specificity}} = \frac{\mathrm{TP}(\mathrm{TN}+\mathrm{FP})}{\mathrm{FP}(\mathrm{TP}+\mathrm{FN})}$ $\mathrm{LR}^- = \frac{1-\mathrm{Sensitivity}}{\mathrm{Specificity}} = \frac{\mathrm{FN}(\mathrm{TN}+\mathrm{FP})}{\mathrm{TN}(\mathrm{TP}+\mathrm{FN})}$
$\mathrm{Accuracy} = \frac{\mathrm{TP}+\mathrm{TN}}{\mathrm{P}+\mathrm{N}}$
$\mathrm{Prevalence} = \frac{\mathrm{TP}+\mathrm{FN}}{\mathrm{P}+\mathrm{N}}$
$\mathrm{Precision} = \frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}}$
$\mathrm{Recall} = \frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}}$
• $F_1$ 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}