模型评估

2021-12-12 r语言

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

精度

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

预测为positve的占实际positive的比例。

准确率

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?	F1 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?	F1 score =2 / 1 / Recall +1 / Precision

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