模板:Diagnostic testing diagram

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Zeroclanzhang（讨论 | 贡献）2024年1月25日 (四) 12:51的版本
Predicted condition Sources: [1][2][3][4][5][6][7][8][9]
Total population
= P + N 		Predicted Positive (PP) Predicted Negative (PN) Informedness, bookmaker informedness (BM)
= TPR + TNR − 1 		Prevalence threshold (PT)
=解析失败 (SVG（MathML可通过浏览器插件启用）：从服务器“https://wikimedia.org/api/rest_v1/”返回无效的响应（“Math extension cannot connect to Restbase.”）：): {\displaystyle \mathsf\tfrac{\sqrt{\text{TPR}\times\text{FPR}}-\text{FPR}}{\text{TPR}-\text{FPR}}}
Actual condition
 Positive (P) True positive (TP),
hit 		False negative (FN),
type II error, miss,
underestimation 		True positive rate (TPR), recall, sensitivity (SEN), probability of detection, hit rate, power
= TP/P = 1 − FNR 		False negative rate (FNR),
miss rate
= FN/P = 1 − TPR
Negative (N) False positive (FP),
type I error, false alarm,
overestimation 		True negative (TN),
correct rejection 		False positive rate (FPR),
probability of false alarm, fall-out
= FP/N = 1 − TNR 		True negative rate (TNR),
specificity (SPC), selectivity
= TN/N = 1 − FPR
Prevalence
= P/P + N 		Positive predictive value (PPV), precision
= TP/PP = 1 − FDR 		False omission rate (FOR)
= FN/PN = 1 − NPV 		Positive likelihood ratio (LR+)
= TPR/FPR 		Negative likelihood ratio (LR−)
= FNR/TNR
Accuracy (ACC) = TP + TN/P + N False discovery rate (FDR)
= FP/PP = 1 − PPV 		Negative predictive value (NPV) = TN/PN = 1 − FOR Markedness (MK), deltaP (Δp)
= PPV + NPV − 1 		Diagnostic odds ratio (DOR) = LR+/LR−
Balanced accuracy (BA) = TPR + TNR/2 F1 score
= 2 PPV × TPR/PPV + TPR = 2 TP/2 TP + FP + FN 		Fowlkes–Mallows index (FM) = 解析失败 (SVG（MathML可通过浏览器插件启用）：从服务器“https://wikimedia.org/api/rest_v1/”返回无效的响应（“Math extension cannot connect to Restbase.”）：): {\displaystyle \scriptstyle\mathsf\sqrt{\text{PPV}\times\text{TPR}}} Matthews correlation coefficient (MCC)
=解析失败 (SVG（MathML可通过浏览器插件启用）：从服务器“https://wikimedia.org/api/rest_v1/”返回无效的响应（“Math extension cannot connect to Restbase.”）：): {\displaystyle \scriptstyle\mathsf\sqrt{\text{TPR}\times\text{TNR}\times\text{PPV}\times\text{NPV}}} 解析失败 (SVG（MathML可通过浏览器插件启用）：从服务器“https://wikimedia.org/api/rest_v1/”返回无效的响应（“Math extension cannot connect to Restbase.”）：): {\displaystyle \scriptstyle-\mathsf\sqrt{\text{FNR}\times\text{FPR}\times\text{FOR}\times\text{FDR}}} 		Threat score (TS), critical success index (CSI), Jaccard index = TP/TP + FN + FP
  1. ↑ Balayla, Jacques (2020). "Prevalence threshold (ϕe) and the geometry of screening curves". PLOS ONE. 15 (10): e0240215. doi:10.1371/journal.pone.0240215. PMID 33027310.
  2. ↑ Fawcett, Tom (2006). "An Introduction to ROC Analysis" (PDF). Pattern Recognition Letters. 27 (8): 861–874. doi:10.1016/j.patrec.2005.10.010. S2CID 2027090.
  3. ↑ Piryonesi S. Madeh; El-Diraby Tamer E. (2020-03-01). "Data Analytics in Asset Management: Cost-Effective Prediction of the Pavement Condition Index". Journal of Infrastructure Systems. 26 (1): 04019036. doi:10.1061/(ASCE)IS.1943-555X.0000512. S2CID 213782055.
  4. ↑ Powers, David M. W. (2011). "Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation". Journal of Machine Learning Technologies. 2 (1): 37–63.
  5. ↑ Ting, Kai Ming (2011). Sammut, Claude; Webb, Geoffrey I. (eds.). Encyclopedia of machine learning. Springer. doi:10.1007/978-0-387-30164-8. ISBN 978-0-387-30164-8.
  6. ↑ Brooks, Harold; Brown, Barb; Ebert, Beth; Ferro, Chris; Jolliffe, Ian; Koh, Tieh-Yong; Roebber, Paul; Stephenson, David (2015-01-26). "WWRP/WGNE Joint Working Group on Forecast Verification Research". Collaboration for Australian Weather and Climate Research. World Meteorological Organisation. Retrieved 2019-07-17.
  7. ↑ Chicco D, Jurman G (January 2020). "The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation". BMC Genomics. 21 (1): 6-1–6-13. doi:10.1186/s12864-019-6413-7. PMC 6941312. PMID 31898477.
  8. ↑ Chicco D, Toetsch N, Jurman G (February 2021). "The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation". BioData Mining. 14 (13): 13. doi:10.1186/s13040-021-00244-z. PMC 7863449. PMID 33541410.
  9. ↑ Tharwat A. (August 2018). "Classification assessment methods". Applied Computing and Informatics. 17: 168–192. doi:10.1016/j.aci.2018.08.003.
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