| Predicted condition | Sources: [1][2][3][4][5][6][7][8] | ||||
| Total population = P + N |
Predicted positive (PP) | Predicted negative (PN) | Informedness, bookmaker informedness (BM) = TPR + TNR − 1 |
Prevalence threshold (PT) = √TPR × FPR - FPR/TPR - FPR | |
Actual condition
|
Positive (P) [a] | True positive (TP), hit[b] |
False negative (FN), 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 type II error [c] = FN/P = 1 − TPR |
| Negative (N)[d] | False positive (FP), false alarm, overestimation |
True negative (TN), correct rejection[e] |
False positive rate (FPR), probability of false alarm, fall-out type I error [f] = 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) = √PPV × TPR |
Matthews correlation coefficient (MCC) = √TPR × TNR × PPV × NPV - √FNR × FPR × FOR × FDR |
Threat score (TS), critical success index (CSI), Jaccard index = TP/TP + FN + FP | |
- ^ the number of real positive cases in the data
- ^ A test result that correctly indicates the presence of a condition or characteristic
- ^ Type II error: A test result which wrongly indicates that a particular condition or attribute is absent
- ^ the number of real negative cases in the data
- ^ A test result that correctly indicates the absence of a condition or characteristic
- ^ Type I error: A test result which wrongly indicates that a particular condition or attribute is present
References
- ^ 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.
- ^ Provost, Foster; Tom Fawcett (2013-08-01). "Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking". O'Reilly Media, Inc.
- ^ 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.
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