True negative rate (TNR), also called specificity, is the proportion of actual negative cases correctly identified by a binary classifier or diagnostic test. It is calculated as TNR = TN / (TN + FP), where TN is true negatives and FP is false positives. The concept was formalized in statistical hypothesis testing to quantify a test's ability to correctly reject false hypotheses. Its persistence mechanism is mathematical definition embedded in teaching, medical diagnostics, and machine learning evaluation frameworks. [formal: specificitas | substrate: mind | horizon: a moment | explicit: yes | epoch: 0.01]
Accepted ontology entry
true negative rate
True negative rate (TNR), also called specificity, is the proportion of actual negative cases correctly identified by a binary classifier or diagnostic test. It is calculated as TNR = TN / (TN + FP), where TN is true negatives and FP is fa…
Definition
Why it is in scope
Human-made statistical concept for evaluating binary classifiers: the proportion of actual negatives correctly identified by a test or model, measured as TN/(TN+FP). Built to persist through teaching, medical diagnostics, and machine learning benchmarking as the formal counterpart to recall (sensitivity).
Names and aliases
- true negative rateen · CANONICAL
Relations from this entry
- cmrwiv1rn00a8soacg5vdpiogINSTANCE_OF →
True negative rate IS a specific kind of metric: TN/(TN+FP), a numerical measure of classifier performance. A competent speaker would call TNR 'a metric'.
- cmsd1etqn03ei3vv36o2bf0rfDEPENDS_ON →
True negative rate is computed as TN/(TN+FP) from a confusion matrix. Remove the matrix concept and you lose the computational framework needed to define this rate.
Relations to this entry
No accepted relations in this direction.
Record identity
- Created
- Aug 5, 2026, 2:07 AM UTC
- Content hash
- 2917bd972f950f997d95569b87bdc67d730ace3f05263a71a48d54bce60f5299