若TP为真阳性,FN为假阴性,TN为真阴性,FP为假阳性,阴性预测值计算公式是()
A: TP/(TP+FN)×100%
B: TN/(TN+FP)×100%
C: (TP+FN)/(TP+FP+TN+FN)×100%
D: TP/(TP+FP)×100%
E: TN/(TN+FN)×100%
A: TP/(TP+FN)×100%
B: TN/(TN+FP)×100%
C: (TP+FN)/(TP+FP+TN+FN)×100%
D: TP/(TP+FP)×100%
E: TN/(TN+FN)×100%
举一反三
- 若TP为真阳性,FN为假阴性,TN为真阴性,FP为假阳性,阳性预测值计算公式是() A: TN/(TN+FN)×100% B: TP/(TP+FN)×100% C: TN/(TN+FP)×100% D: TP/(TP+FP)×100% E: (TP+FN)/(TP+FP+TN+FN)×100%
- 式中TP为真阳性,FN为假阴性,TN为真阴性,FP为假阳性,式中符号意义同上,诊断敏感性计算公式为: A: TP/TP+FP×100% B: TP/TP+FN×100% C: TN/TN+FP×100% D: TN/TN+FN×100% E: 以上都不对
- 有关机器学习分类算法的Precision和Recall,以下定义中正确的是(假定tp = true positive, tn = true negative, fp = false positive, fn = false negative)( ) A: Precision = tp / (tn + fn), Recall = tp /(tp + fp) B: Precision= tp / (tp + fp), Recall = tp / (tp + fn) C: Precision = tp / (tn + fp), Recall = tp /(tp + fn) D: Precision = tp / (tp + fp), Recall = tp /(tn + fn)
- 式中TP为真阳性,FN为假阴性,TN为真阴性,FP为假阳性,阴性预测量计算公式为
- 式中TP为真阳性,FN为假阴性,TN为真阴性,FP为假阳性,阳性预示值计算公式为: