[statistic] Confusion matrix

condition positive (P)

the number of real positive cases in the data

condition negatives (N)

the number of real negative cases in the data

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true positive (TP)

eqv. with hit

true negative (TN)

eqv. with correct rejection

false positive (FP)

eqv. with false alarm, Type I error

false negative (FN)

eqv. with miss, Type II error

sensitivity, recall, hit rate, or true positive rate (TPR)

${\displaystyle \mathrm {TPR} ={\frac {\mathrm {TP} }{P}}={\frac {\mathrm {TP} }{\mathrm {TP} +\mathrm {FN} }}}$

specificity or true negative rate (TNR)

${\displaystyle \mathrm {TNR} ={\frac {\mathrm {TN} }{N}}={\frac {\mathrm {TN} }{\mathrm {FP} +\mathrm {TN} }}}$

precision or positive predictive value (PPV)

${\displaystyle \mathrm {PPV} ={\frac {\mathrm {TP} }{\mathrm {TP} +\mathrm {FP} }}}$

negative predictive value (NPV)

${\displaystyle \mathrm {NPV} ={\frac {\mathrm {TN} }{\mathrm {TN} +\mathrm {FN} }}}$

accuracy (ACC)

${\displaystyle \mathrm {ACC} ={\frac {\mathrm {TP} +\mathrm {TN} }{P+N}}}$

장비의 분류 성능은 이정도만 제공해줘도 충분하다.