정말로 negative인 것 중에서 model이 positive라고 (잘못) 예측한 것의 비율.
FPR = FP / (FP + TN)
via
https://www.ibm.com/docs/en/cloud-paks/cp-data/4.6.x?topic=overview-false-positive-rate-fpr
False Positive Rate
= (False Positives) / (Negatives)
= 1 − (True Negatives) / (Negatives)
= 1 - Specificity
(Kwak, Slide 1, p77)
FPR = 1 − TNR = 1 − Specificity = FP / (FP + TN)
(chk, via
https://driip.me/3ef36050-f5a3-41ea-9f23-874afe665342
)
MKLINK:
false_positive
는
type_1_error
. - curr see
오류,error
거짓양성,false_positive,FP
false_positive
{
ex.
object_detection에서, 물체가 아닌데 물체로 판단
}
참양성,true_positive,TP
참음성,true_negative,TN
거짓양성,false_positive,FP
거짓음성,false_negative,FN
참양성율,true_positive_rate,TPR
=
민감도,sensitivity
=
재현율,recall
참음성율,true_negative_rate,TNR
=
특이도,specificity
거짓양성율,false_positive_rate,FPR
거짓음성율,false_negative_rate,FNR
See also
ROC곡선,ROC_curve
혼동행렬,confusion_matrix
Twins:
False_positive_rate
...
false positive rate
false positive rate
비율,rate
Retrieved from http://tomoyo.ivyro.net/123/wiki.php/거짓양성율,false_positive_rate,FPR
last modified 2023-06-03 17:03:16