confusion matrix concept
| Definition | The four counts a two-class classifier with a threshold produces on a test set, true and false positives and negatives, from which precision, recall, the false positive rate, and accuracy are read as conditional probabilities. Primer S, equation S.17. |
|---|---|
| Example | 30 true positives, 10 false positives, 20 false negatives, and 940 true negatives give precision 0.75, recall 0.6, and accuracy 0.97. |
| Book | Data Mining as Observation, draft 0.2, commit f3914f0; entry id confusion-matrix, kind concept. |
| Status | no ledger row names this entry. Corrections: none recorded. |
| Defining equation | Book equation S.17. |
| 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
Book equation S.17.
\[P=\Pr[\text{positive}\mid\text{predicted positive}]=\frac{TP}{TP+FP},\qquad R=\Pr[\text{predicted positive}\mid\text{positive}]=\frac{TP}{TP+FN}.\]
Conditions
none
Ledger
none
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, 6, 8.
Related
precision, recall; accuracy; false positive rate; threshold.
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.