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}}} | |
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 |
- ↑ 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.
- ↑ 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.
- ↑ 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.
- ↑ 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.
- ↑ 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.
- ↑ 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.
- ↑ 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.
- ↑ 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.
- ↑ Tharwat A. (August 2018). "Classification assessment methods". Applied Computing and Informatics. 17: 168–192. doi:10.1016/j.aci.2018.08.003.