模型评估

最新推荐文章于 2023-06-10 10:07:56 发布

原创最新推荐文章于 2023-06-10 10:07:56 发布 · 925 阅读

0 ·

CC 4.0 BY-SA版权

R语言专栏收录该内容

12 篇文章

订阅专栏

本文详细解释了在信息检索及机器学习中精确率(Precision)与召回率(Recall)的概念及其计算方法。精确率定义为预测为positive的样本中真正为positive的比例，而召回率则衡量了实际为positive的样本被正确预测的比例。此外，还介绍了准确率(Accuracy)、真阳性率(True Positive Rate, TPR)等关键指标。

https://en.wikipedia.org/wiki/Precision_and_recall

精度 ${\text{Precision}}={\frac {tp}{tp+fp}}\,$

预测为positve的占所有预测为positive的比例。

Recall = t p t p + f n {\displaystyle {\text{Recall}}={\frac {tp}{tp+fn}}\,}

召回率 ${\text{Recall}}={\frac {tp}{tp+fn}}\,$

预测为positve的占实际positive的比例。

准确率 ${\text{Accuracy}}={\frac {tp+tn}{tp+tn+fp+fn}}\,$

预测positive和negetive都正确的占所有样本的比例。

		True condition
	Total population	Condition positive	Condition negative	Prevalence = Σ Condition positive / Σ Total population	Accuracy (ACC) = Σ True positive + Σ True negative / Σ Total population
Predicted condition	Predicted condition positive	True positive, Power	False positive, Type I error	Positive predictive value (PPV), Precision = Σ True positive / Σ Predicted condition positive	False discovery rate (FDR) = Σ False positive / Σ Predicted condition positive
Predicted condition	Predicted condition negative	False negative, Type II error	True negative	False omission rate (FOR) = Σ False negative / Σ Predicted condition negative	Negative predictive value (NPV) = Σ True negative / Σ Predicted condition negative
		True positive rate (TPR), Recall, Sensitivity, probability of detection = Σ True positive / Σ Condition positive	False positive rate (FPR), Fall-out, probability of false alarm = Σ False positive / Σ Condition negative	Positive likelihood ratio (LR+) = TPR / FPR	Diagnostic odds ratio (DOR) = LR+ / LR−	F 1 score = 2 / 1 / Recall + 1 / Precision
		False negative rate (FNR), Miss rate = Σ False negative / Σ Condition positive	True negative rate (TNR), Specificity (SPC) = Σ True negative / Σ Condition negative	Negative likelihood ratio (LR−) = FNR / TNR	Diagnostic odds ratio (DOR) = LR+ / LR−	F 1 score = 2 / 1 / Recall + 1 / Precision