false positive rate concept
| Definition | The fraction of negatives a classifier labels positive, which like recall does not depend on the base rate, so that classifiers are compared across datasets by those two and not by precision. Primer S section S.9. |
|---|---|
| Example | 10 false positives among 950 negatives is a false positive rate of 0.011. |
| Book | Data Mining as Observation, draft 0.2, commit f3914f0; entry id false-positive-rate, kind concept. |
| Status | no ledger row names this entry. Corrections: none recorded. |
| Defining equation | none |
| Assumptions and scope | none |
| Prior art | none recorded |
| Evidence | none |
| Reviewed | semantic review 2026-09-09; generated 2026-09-10 from records at the commits on the provenance page. |
Equation
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Conditions
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Ledger
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First stated
Primer S section S.9 of Data Mining as Observation, added in draft 0.3 (2026-09-09) for the ECE 514 readers whose first courses are far behind. The idea is standard and TSK Appendix C covers it at length.
Measurements
none
Failures and corrections
none
Invariance envelope
none declared
Machine checked
none
Used in
Data Mining as Observation primer S, chapters 0, 5.
Related
confusion matrix; precision, recall; AUROC; ROC curve.
See also
none
Status
Generated 2026-09-10 by encyclopedia/generate.py; book
at observation-data-mining f3914f0; the commit of every record is listed
in the encyclopedia’s provenance.