Template:Diagnostic testing diagram

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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
power

= TP/P = 1 − FNR
type II error [c]

= FN/P = 1 − TPR
Negative (N)[d]
False positive (FP),
false alarm, overestimation
True negative (TN),
correct rejection[e]
type I error [f]

= FP/N = 1 − TNR
specificity
(SPC), selectivity
= TN/N = 1 − FPR
Prevalence
= P/P + N
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
  1. ^ the number of real positive cases in the data
  2. ^ A test result that correctly indicates the presence of a condition or characteristic
  3. ^ Type II error: A test result which wrongly indicates that a particular condition or attribute is absent
  4. ^ the number of real negative cases in the data
  5. ^ A test result that correctly indicates the absence of a condition or characteristic
  6. ^ Type I error: A test result which wrongly indicates that a particular condition or attribute is present

References

These references will appear in the article, but this list appears only on this page.
  1. S2CID 2027090
    .
  2. ^ 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.
  3. ^ 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.
  4. ^ Ting, Kai Ming (2011). Sammut, Claude; Webb, Geoffrey I. (eds.). Encyclopedia of machine learning. Springer.
    ISBN 978-0-387-30164-8
    .
  5. ^ 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.
  6. ^ 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.
    PMID 31898477
    .
  7. ^ 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.
    PMID 33541410
    .
  8. ^ 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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