Difference between r1.11 and the current
@@ -21,6 +21,11 @@
}----
[[참양성,true_positive,TP]]
[[참음성,true_negative,TN]]
[[거짓양성,false_positive,FP]]
[[거짓음성,false_negative,FN]]
[[참음성율,true_negative_rate,TNR]] = [[특이도,specificity]]
'''거짓양성율,false_positive_rate,FPR'''
@@ -36,5 +41,4 @@
----
[[비율,rate]]
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)= 1 − (True Negatives) / (Negatives)
= 1 - Specificity
FPR = 1 − TNR = 1 − Specificity = FP / (FP + TN)(chk, via https://driip.me/3ef36050-f5a3-41ea-9f23-874afe665342)
MKLINK:
false_positive는 type_1_error. - curr see 오류,error
거짓양성,false_positive,FP false_positive
{
ex.
object_detection에서, 물체가 아닌데 물체로 판단
}
false_positive는 type_1_error. - curr see 오류,error
거짓양성,false_positive,FP false_positive
{
ex.
object_detection에서, 물체가 아닌데 물체로 판단
}
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
참음성율,true_negative_rate,TNR = 특이도,specificity
거짓양성율,false_positive_rate,FPR
거짓음성율,false_negative_rate,FNR