模板:Diagnostic testing diagram：修订间差异

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<noinclude>{| class="wikitable" align="center" style="border:none; background:transparent; text-align:center;"
{| class="wikitable" align="center" style="border:none; background:transparent; text-align:center;"
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| style="background:#bbeeee;" colspan="2" | '''预测条件'''
| style="background:#bbeeee;" colspan="2" | '''Predicted condition'''
| style="border:none; text-align:right;" colspan="2" | <sup>来源: </sup><ref>
| style="border:none; text-align:right;" colspan="2" | <sup>Sources: </sup><ref>
{{cite journal |last=Balayla |first=Jacques |title=流行阈值 (ϕe) 及筛查曲线的几何性质 |journal=PLOS ONE |date=2020 |volume=15 |issue=10 |pages=e0240215 |doi=10.1371/journal.pone.0240215 |pmid=33027310 |doi-access=free }}</ref><ref>
{{cite journal |last=Balayla |first=Jacques |title=Prevalence threshold (ϕe) and the geometry of screening curves |journal=PLOS ONE |date=2020 |volume=15 |issue=10 |pages=e0240215 |doi=10.1371/journal.pone.0240215 |pmid=33027310 |doi-access=free }}</ref><ref>  
{{cite journal |last=Fawcett |first=Tom |title=ROC 分析简介 |journal=Pattern Recognition Letters |date=2006 |volume=27 |issue=8 |pages=861–874 |doi=10.1016/j.patrec.2005.10.010 |s2cid=2027090 |url=http://people.inf.elte.hu/kiss/11dwhdm/roc.pdf}}</ref><ref>
{{cite journal |last=Fawcett |first=Tom |title=An Introduction to ROC Analysis |journal=Pattern Recognition Letters |date=2006 |volume=27 |issue=8 |pages=861–874 |doi=10.1016/j.patrec.2005.10.010 |s2cid=2027090 |url=http://people.inf.elte.hu/kiss/11dwhdm/roc.pdf}}</ref><ref>
{{Cite journal|last1=Piryonesi S. Madeh|last2=El-Diraby Tamer E.|date=2020-03-01|title=资产管理中数据分析：路面状况指数的成本效益预测|journal=Journal of Infrastructure Systems|volume=26|issue=1|pages=04019036|doi=10.1061/(ASCE)IS.1943-555X.0000512|s2cid=213782055 }}</ref><ref>
{{Cite journal|last1=Piryonesi S. Madeh|last2=El-Diraby Tamer E.|date=2020-03-01|title=Data Analytics in Asset Management: Cost-Effective Prediction of the Pavement Condition Index|journal=Journal of Infrastructure Systems|volume=26|issue=1|pages=04019036|doi=10.1061/(ASCE)IS.1943-555X.0000512|s2cid=213782055 }}</ref><ref>
{{cite journal |first=David M. W. |last=Powers |date=2011 |title=评价：从精确度、召回率和F-度量到ROC、信息度和标记度与相关性 |journal=Journal of Machine Learning Technologies |volume=2 |issue=1 |pages=37–63 |url=https://www.researchgate.net/publication/228529307}}</ref><ref>
{{cite journal |first=David M. W. |last=Powers |date=2011 |title=Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation |journal=Journal of Machine Learning Technologies |volume=2 |issue=1 |pages=37–63 |url=https://www.researchgate.net/publication/228529307}}</ref><ref>
{{cite book |last=Ting |first=Kai Ming |editor2-first=Geoffrey I. |editor2-last=Webb |editor1-first=Claude |editor1-last=Sammut |title=机器学习百科全书 |date=2011 |publisher=Springer |doi=10.1007/978-0-387-30164-8 |isbn=978-0-387-30164-8 }}</ref><ref>
{{cite book |last=Ting |first=Kai Ming |editor2-first=Geoffrey I. |editor2-last=Webb |editor1-first=Claude |editor1-last=Sammut |title=Encyclopedia of machine learning |date=2011 |publisher=Springer |doi=10.1007/978-0-387-30164-8 |isbn=978-0-387-30164-8 }}</ref><ref>
{{cite web |url=https://www.cawcr.gov.au/projects/verification/ |title=WWRP/WGNE 联合预测验证研究工作组 |last1=Brooks |first1=Harold |last2=Brown |first2=Barb |last3=Ebert |first3=Beth |last4=Ferro |first4=Chris |last5=Jolliffe |first5=Ian |last6=Koh |first6=Tieh-Yong |last7=Roebber |first7=Paul |last8=Stephenson |first8=David |date=2015-01-26|website=澳大利亚天气与气候研究合作|publisher=世界气象组织|access-date=2019-07-17}}</ref><ref>
{{cite web |url=https://www.cawcr.gov.au/projects/verification/ |title=WWRP/WGNE Joint Working Group on Forecast Verification Research |last1=Brooks |first1=Harold |last2=Brown |first2=Barb |last3=Ebert |first3=Beth |last4=Ferro |first4=Chris |last5=Jolliffe |first5=Ian |last6=Koh |first6=Tieh-Yong |last7=Roebber |first7=Paul |last8=Stephenson |first8=David |date=2015-01-26|website=Collaboration for Australian Weather and Climate Research|publisher=World Meteorological Organisation|access-date=2019-07-17}}</ref><ref>
{{cite journal |vauthors = Chicco D, Jurman G |title = 马修斯相关系数 (MCC) 在二元分类评估中优于 F1 分数和准确度的优势 |journal = BMC Genomics |volume = 21 |issue = 1 |date = January 2020 |page = 6-1–6-13 |pmid = 31898477 |doi = 10.1186/s12864-019-6413-7 |pmc = 6941312 |doi-access = free }}</ref><ref>
{{cite journal |vauthors = Chicco D, Jurman G |title = The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation |journal = BMC Genomics |volume = 21 |issue = 1 |date = January 2020 |page = 6-1–6-13 |pmid = 31898477 |doi = 10.1186/s12864-019-6413-7 |pmc = 6941312 |doi-access = free }}</ref><ref>
{{cite journal |vauthors = Chicco D, Toetsch N, Jurman G |title = 马修斯相关系数 (MCC) 在两类混淆矩阵评估中比平衡精度、博彩信息度和标记度更可靠 |journal = BioData Mining |volume = 14 |issue = 13 |date = February 2021 |page = 13pmid = 33541410 | pmc = 7863449 |doi = 10.1186/s13040-021-00244-z |doi-access = free }}</ref><ref>
{{cite journal |vauthors = Chicco D, Toetsch N, Jurman G |title = The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation |journal = BioData Mining |volume = 14 |issue = 13 |date = February 2021 |page = 13 |pmid = 33541410 | pmc = 7863449 |doi = 10.1186/s13040-021-00244-z |doi-access = free }}</ref><ref>
{{cite journal |author = Tharwat A. |title = 分类评估方法 |journal = Applied Computing and Informatics |date = August 2018 |volume = 17 |pages = 168–192 |doi = 10.1016/j.aci.2018.08.003 |doi-access = free }}</ref> <sup>{{navbar|Diagnostic testing diagram|plain=y}}</sup>
{{cite journal |author = Tharwat A. |title = Classification assessment methods |journal = Applied Computing and Informatics |date = August 2018 |volume = 17 |pages = 168–192 |doi = 10.1016/j.aci.2018.08.003 |doi-access = free }}</ref> <sup>{{navbar|Diagnostic testing diagram|plain=y}}</sup>
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| style="background:#eeeeee;" | [[Statistical population|总体人群]] <br/><span style="white-space:nowrap;">= P + N</span>
| style="background:#eeeeee;" | [[Statistical population|Total population]] <br/><span style="white-space:nowrap;">= P + N</span>
| style="background:#ccffff;" | '''预测阳性 (PP)'''
| style="background:#ccffff;" | '''Predicted Positive (PP)'''
| style="background:#aadddd;" | '''预测阴性 (PN)'''
| style="background:#aadddd;" | '''Predicted Negative (PN)'''
| style="border-left:double silver;" | [[Youden's_J_statistic|信息度]], {{small|博彩信息度 (BM)}} <br/><span style="white-space:nowrap;">= TPR + TNR − 1</span>
| style="border-left:double silver;" | [[Youden's_J_statistic|Informedness]], {{small|bookmaker informedness (BM)}} <br/><span style="white-space:nowrap;">= TPR + TNR − 1</span>
| [[Prevalence threshold]] (PT) <br/><span style="white-space:nowrap;">=[math]\mathsf\tfrac{\sqrt{\text{TPR}\times\text{FPR}}-\text{FPR}}{\text{TPR}-\text{FPR}}[/math]</span>
| [[Prevalence threshold]] (PT) <br/><span style="white-space:nowrap;">=<math>\mathsf\tfrac{\sqrt{\text{TPR}\times\text{FPR}}-\text{FPR}}{\text{TPR}-\text{FPR}}</math></span>
|-
|-
| rowspan="2" {{verth|va=middle|cellstyle=background:#eeeebb;|'''实际条件'''}}
| rowspan="2" {{verth|va=middle|cellstyle=background:#eeeebb;|'''Actual condition'''}}
| style="background:#ffffcc;" | '''阳性 (P)'''
| style="background:#ffffcc;" | '''Positive (P)'''
| style="background:#ccffcc;" | '''[[True positive]] (TP), <br />{{small|命中}}'''
| style="background:#ccffcc;" | '''[[True positive]] (TP), <br />{{small|hit}}'''
| style="background:#ffdddd;" | '''[[False negative]] (FN), <br/>{{small|[[type&nbsp;II&nbsp;error]], 错失, <br/>低估}}'''
| style="background:#ffdddd;" | '''[[False negative]] (FN), <br/>{{small|[[type&nbsp;II&nbsp;error]], miss, <br/>underestimation}}'''
| style="background:#eeffee;" | [[True positive rate]] (TPR), [[recall (information retrieval)|召回率]], [[Sensitivity (tests)|敏感性]] (SEN), {{small|检测概率, 命中率, [[statistical power|功效]]}} <br/><span style="white-space:nowrap;">= {{sfrac|TP|P}}</span> <span style="white-space:nowrap;">= 1 − FNR</span>
| style="background:#eeffee;" | [[True positive rate]] (TPR), [[recall (information retrieval)|recall]], [[Sensitivity (tests)|sensitivity]] (SEN), {{small|probability&nbsp;of&nbsp;detection, hit&nbsp;rate, [[statistical power|power]]}} <br/><span style="white-space:nowrap;">= {{sfrac|TP|P}}</span> <span style="white-space:nowrap;">= 1 − FNR</span>
| style="background:#ffeeee;" | [[False negative rate]] (FNR), <br/>{{small|错失率}} <br/><span style="white-space:nowrap;">= {{sfrac|FN|P}}</span> <span style="white-space:nowrap;">= 1 − TPR</span>
| style="background:#ffeeee;" | [[False negative rate]] (FNR), <br/>{{small|miss&nbsp;rate}} <br/><span style="white-space:nowrap;">= {{sfrac|FN|P}}</span> <span style="white-space:nowrap;">= 1 − TPR</span>
|-
|-
| style="background:#ddddaa;" | '''阴性 (N)'''
| style="background:#ddddaa;" | '''Negative (N)'''
| style="background:#ffcccc;" | '''[[False positive]] (FP), <br/>{{small|[[type&nbsp;I&nbsp;error]], 虚警, <br/>高估}}'''
| style="background:#ffcccc;" | '''[[False positive]] (FP), <br/>{{small|[[type&nbsp;I&nbsp;error]], false alarm, <br/>overestimation}}'''
| style="background:#bbeebb;" | '''[[True negative]] (TN), <br />{{small|正确拒绝}}'''
| style="background:#bbeebb;" | '''[[True negative]] (TN), <br />{{small|correct rejection}}'''
| style="background:#eedddd;" | [[False positive rate]] (FPR), <br/>{{small|虚警概率, [[evaluation measures (information retrieval)#Fall-out|{{nowrap|降雨量}}]]}} <br/><span style="white-space:nowrap;">= {{sfrac|FP|N}}</span> <span style="white-space:nowrap;">= 1 − TNR</span>
| style="background:#eedddd;" | [[False positive rate]] (FPR), <br/>{{small|probability&nbsp;of&nbsp;false&nbsp;alarm, [[evaluation measures (information retrieval)#Fall-out|{{nowrap|fall-out}}]]}} <br/><span style="white-space:nowrap;">= {{sfrac|FP|N}}</span> <span style="white-space:nowrap;">= 1 − TNR</span>
| style="background:#ddeedd;"| [[True negative rate]] (TNR), <br/>{{small|[[specificity (tests)|特异性]] (SPC), 选择性}} <br/><span style="white-space:nowrap;">= {{sfrac|TN|N}}</span> <span style="white-space:nowrap;">= 1 − FPR</span>
| style="background:#ddeedd;"| [[True negative rate]] (TNR), <br/>{{small|[[specificity (tests)|specificity]] (SPC), selectivity}} <br/><span style="white-space:nowrap;">= {{sfrac|TN|N}}</span> <span style="white-space:nowrap;">= 1 − FPR</span>
 
|-
|-
| style="border:none;" rowspan="3"|
| style="border:none;" rowspan="3"|
| style="border-top:double silver; border-right:double silver;"|[[患病率]] <br/><span style="white-space:nowrap;">= {{sfrac|P|P + N}}</span>
| style="border-top:double silver; border-right:double silver;"|[[Prevalence]] <br/><span style="white-space:nowrap;">= {{sfrac|P|P + N}}</span>
| style="background:#eeffee;" | {{nowrap|[[阳性预测值]] (PPV),}} {{small|[[信息检索中的精确度|精确度]]}} <br/><span style="white-space:nowrap;">= {{sfrac|TP|PP}}</span> <span style="white-space:nowrap;">= 1 − FDR</span>
| style="background:#eeffee;" | {{nowrap|[[Positive predictive value]] (PPV),}} {{small|[[precision (information retrieval)|precision]]}} <br/><span style="white-space:nowrap;">= {{sfrac|TP|PP}}</span> <span style="white-space:nowrap;">= 1 − FDR</span>
| style="background:#ffeeee;border-right:double silver;"|[[漏报率]] (FOR) <br/><span style="white-space:nowrap;">= {{sfrac|FN|PN}}</span> <span style="white-space:nowrap;">= 1 − NPV</span>
| style="background:#ffeeee;border-right:double silver;"|[[False omission rate]] (FOR) <br/><span style="white-space:nowrap;">= {{sfrac|FN|PN}}</span> <span style="white-space:nowrap;">= 1 − NPV</span>
| style="background:#eeeeff;" | [[阳性似然比]] (LR+) <br/><span style="white-space:nowrap;">= {{sfrac|TPR|FPR}}</span>
| style="background:#eeeeff;" | [[Positive likelihood ratio]] (LR+) <br/><span style="white-space:nowrap;">= {{sfrac|TPR|FPR}}</span>
| style="background:#eeeeff;" | [[阴性似然比]] (LR−) <br/><span style="white-space:nowrap;">= {{sfrac|FNR|TNR}}</span>
| style="background:#eeeeff;" | [[Negative likelihood ratio]] (LR−) <br/><span style="white-space:nowrap;">= {{sfrac|FNR|TNR}}</span>
|-
|-
| style="border-right:double silver;"|[[准确度与精确度#在二元分类中|准确度]] (ACC) <span style="white-space:nowrap;">= {{sfrac|TP + TN|P + N}}</span>
| style="border-right:double silver;"|[[Accuracy and precision#In binary classification|Accuracy]] (ACC) <span style="white-space:nowrap;">= {{sfrac|TP + TN|P + N}}</span>
| style="background:#eedddd;"|[[假发现率]] (FDR) <br/><span style="white-space:nowrap;">= {{sfrac|FP|PP}}</span> <span style="white-space:nowrap;">= 1 − PPV</span>
| style="background:#eedddd;"|[[False discovery rate]] (FDR) <br/><span style="white-space:nowrap;">= {{sfrac|FP|PP}}</span> <span style="white-space:nowrap;">= 1 − PPV</span>
| style="background:#ddeedd;"|[[阴性预测值]] (NPV) <span style="white-space:nowrap;">= {{sfrac|TN|PN}}</span> <span style="white-space:nowrap;">= 1 − FOR</span>
| style="background:#ddeedd;"|[[Negative predictive value]] (NPV) <span style="white-space:nowrap;">= {{sfrac|TN|PN}}</span> <span style="white-space:nowrap;">= 1 − FOR</span>
| style="border-top:double silver;border-right:double silver;" | [[标记度]] (MK), {{small|deltaP (&Delta;p)}} <br/><span style="white-space:nowrap;">= PPV + NPV − 1</span>
| style="border-top:double silver;border-right:double silver;" | [[Markedness]] (MK), {{small|deltaP (&Delta;p)}} <br/><span style="white-space:nowrap;">= PPV + NPV − 1</span>
| style="background:#eeeeff;" | [[诊断比值比|诊断{{nowrap|比值比}}]] (DOR) <span style="white-space:nowrap;">= {{sfrac|LR+|LR−}}</span>
| style="background:#eeeeff;" | [[Diagnostic odds ratio|Diagnostic {{nowrap|odds ratio}}]] (DOR) <span style="white-space:nowrap;">= {{sfrac|LR+|LR−}}</span>
|-
|-
| 平衡准确度 (BA) <span style="white-space:nowrap;">= {{sfrac|TPR + TNR|2}}</span>
| Balanced accuracy (BA) <span style="white-space:nowrap;">= {{sfrac|TPR + TNR|2}}</span>
| style="border-top:double silver;"|[[F1 分数|F<sub>1</sub> 分数]] <br/><span style="white-space:nowrap;">= {{sfrac|2&hairsp;PPV&hairsp;×&hairsp;TPR|PPV + TPR}}</span> <span white-space:nowrap;">= {{sfrac|2&hairsp;TP|2&hairsp;TP + FP + FN}}</span>
| style="border-top:double silver;"|[[F1 score|F<sub>1</sub> score]] <br/><span style="white-space:nowrap;">= {{sfrac|2&hairsp;PPV&hairsp;×&hairsp;TPR|PPV + TPR}}</span> <span white-space:nowrap;">= {{sfrac|2&hairsp;TP|2&hairsp;TP + FP + FN}}</span>
| style="border-top:double silver;"|[[Fowlkes–Mallows 指数]] (FM) <span style="white-space:nowrap;">= [math]\scriptstyle\mathsf\sqrt{\text{PPV}\times\text{TPR}}[/math]</span>
| style="border-top:double silver;"|[[Fowlkes–Mallows index]] (FM) <span style="white-space:nowrap;">= <math>\scriptstyle\mathsf\sqrt{\text{PPV}\times\text{TPR}}</math></span>
| style="border-top:double silver;"|[[马修斯相关系数]] (MCC) <br/>=[math]\scriptstyle\mathsf\sqrt{\text{TPR}\times\text{TNR}\times\text{PPV}\times\text{NPV}}[/math][math]\scriptstyle-\mathsf\sqrt{\text{FNR}\times\text{FPR}\times\text{FOR}\times\text{FDR}}[/math]
| style="border-top:double silver;"|[[Matthews correlation coefficient]] (MCC) <br/>=<math>\scriptstyle\mathsf\sqrt{\text{TPR}\times\text{TNR}\times\text{PPV}\times\text{NPV}}</math><math>\scriptstyle-\mathsf\sqrt{\text{FNR}\times\text{FPR}\times\text{FOR}\times\text{FDR}}</math>
| style="border-top:double silver;" colspan="2"|威胁分数 (TS), 关键成功指数 (CSI), [[Jaccard 指数#在二元分类混淆矩阵中的Jaccard指数|Jaccard 指数]] <span style="white-space:nowrap;">= {{sfrac|TP|TP + FN + FP}}</span>
| style="border-top:double silver;" colspan="2"|Threat score (TS), critical success index (CSI), [[Jaccard_index#Jaccard_index_in_binary_classification_confusion_matrices|Jaccard index]] <span style="white-space:nowrap;">= {{sfrac|TP|TP + FN + FP}}</span>
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[[Category:Medicine procedure templates]]
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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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