Sensitivity is recall under another name: of all the real positives, the share your model catches. Specificity is its mirror on the other class: of all the real negatives, the share you correctly leave alone. Medical and diagnostic work usually uses these two names.
A receiver operating characteristic (ROC) curve is built from exactly this pair. The vertical axis is sensitivity. The horizontal axis is one minus specificity, also called the false positive rate. Each point on the curve is one decision cutoff.
Knowing the pair keeps you honest. Raising sensitivity almost always lowers specificity, since catching more positives means flagging more negatives too. Quoting one alone hides that cost, which is how a screening test with 99% sensitivity can still drown a clinic in false alarms.
Rewriting in plainer words…
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