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		<summary type="html">&lt;p&gt;1 revision imported from &lt;a href=&quot;https://alpha.indicwiki.in/index.php?title=Template:Confusion_matrix_terms&quot; class=&quot;extiw&quot; title=&quot;alpha:Template:Confusion matrix terms&quot;&gt;alpha:Template:Confusion_matrix_terms&lt;/a&gt;&lt;/p&gt;
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				&lt;td colspan=&quot;1&quot; style=&quot;background-color: #fff; color: #202122; text-align: center;&quot;&gt;Revision as of 10:07, 6 April 2023&lt;/td&gt;
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		<author><name>Indicwiki</name></author>
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		<id>https://www.vigyanwiki.in/index.php?title=Template:Confusion_matrix_terms&amp;diff=122157&amp;oldid=prev</id>
		<title>alpha&gt;Indicwiki: Created page with &quot;{| class=&quot;wikitable floatright&quot; width=35% style=&quot;margin-left:0.5em; padding:0.25em; background:#f1f5fc; font-size:98%;&quot; |+ Terminology and derivations&lt;br/&gt;from a confusion m...&quot;</title>
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		<updated>2023-03-21T05:15:08Z</updated>

		<summary type="html">&lt;p&gt;Created page with &amp;quot;{| class=&amp;quot;wikitable floatright&amp;quot; width=35% style=&amp;quot;margin-left:0.5em; padding:0.25em; background:#f1f5fc; font-size:98%;&amp;quot; |+ Terminology and derivations&amp;lt;br/&amp;gt;from a confusion m...&amp;quot;&lt;/p&gt;
&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;{| class=&amp;quot;wikitable floatright&amp;quot; width=35% style=&amp;quot;margin-left:0.5em; padding:0.25em; background:#f1f5fc; font-size:98%;&amp;quot;&lt;br /&gt;
|+ Terminology and derivations&amp;lt;br/&amp;gt;from a [[confusion matrix]]&lt;br /&gt;
|- style=&amp;quot;vertical-align:top;&amp;quot;&lt;br /&gt;
|&lt;br /&gt;
; condition positive (P): the number of real positive cases in the data&lt;br /&gt;
; condition negative (N): the number of real negative cases in the data&lt;br /&gt;
----&lt;br /&gt;
; true positive (TP): A test result that correctly indicates the presence of a condition or characteristic&lt;br /&gt;
; true negative (TN): A test result that correctly indicates the absence of a condition or characteristic&lt;br /&gt;
; false positive (FP): A test result which wrongly indicates that a particular condition or attribute is present&lt;br /&gt;
; false negative (FN): A test result which wrongly indicates that a particular condition or attribute is absent&lt;br /&gt;
----&lt;br /&gt;
; [[sensitivity (test)|sensitivity]], [[Precision_and_recall#Recall|recall]], [[hit rate]], or [[sensitivity (test)|true positive rate]] (TPR): &amp;lt;math&amp;gt;\mathrm{TPR} = \frac {\mathrm{TP}} {\mathrm{P}} = \frac {\mathrm{TP}} {\mathrm{TP}+\mathrm{FN}}= 1 - \mathrm{FNR}&amp;lt;/math&amp;gt;&lt;br /&gt;
; [[Specificity (tests)|specificity]], [[Specificity (tests)|selectivity]] or [[Specificity (tests)|true negative rate]] (TNR): &amp;lt;math&amp;gt;\mathrm{TNR} = \frac {\mathrm{TN}} {\mathrm{N}} = \frac {\mathrm{TN}} {\mathrm{TN} + \mathrm{FP}} = 1 - \mathrm{FPR}&amp;lt;/math&amp;gt;&lt;br /&gt;
; [[Precision_and_recall#Precision|precision]] or [[positive predictive value]] (PPV): &amp;lt;math&amp;gt;\mathrm{PPV} = \frac {\mathrm{TP}} {\mathrm{TP} + \mathrm{FP}} = 1 - \mathrm{FDR}&amp;lt;/math&amp;gt;&lt;br /&gt;
; [[negative predictive value]] (NPV): &amp;lt;math&amp;gt;\mathrm{NPV} = \frac {\mathrm{TN}} {\mathrm{TN} + \mathrm{FN}} = 1 - \mathrm{FOR}&amp;lt;/math&amp;gt;&lt;br /&gt;
; miss rate or [[Type I and type II errors#False positive and false negative rates|false negative rate]] (FNR): &amp;lt;math&amp;gt;\mathrm{FNR} = \frac {\mathrm{FN}} {\mathrm{P}} = \frac {\mathrm{FN}} {\mathrm{FN} + \mathrm{TP}} = 1 - \mathrm{TPR} &amp;lt;/math&amp;gt;&lt;br /&gt;
; [[False positive rate|fall-out]] or [[False positive rate|false positive rate]] (FPR): &amp;lt;math&amp;gt;\mathrm{FPR} = \frac {\mathrm{FP}} {\mathrm{N}} = \frac {\mathrm{FP}} {\mathrm{FP} + \mathrm{TN}} = 1 - \mathrm{TNR}&amp;lt;/math&amp;gt;&lt;br /&gt;
; [[false discovery rate]] (FDR): &amp;lt;math&amp;gt;\mathrm{FDR} = \frac {\mathrm{FP}} {\mathrm{FP} + \mathrm{TP}} = 1 - \mathrm{PPV} &amp;lt;/math&amp;gt;&lt;br /&gt;
; [[Positive and negative predictive values|false omission rate]] (FOR): &amp;lt;math&amp;gt;\mathrm{FOR} = \frac {\mathrm{FN}} {\mathrm{FN} + \mathrm{TN}} = 1 - \mathrm{NPV} &amp;lt;/math&amp;gt;&lt;br /&gt;
; [[Positive likelihood ratio]] (LR+): &amp;lt;math&amp;gt;\mathrm{LR+} = \frac {\mathrm{TPR}} {\mathrm{FPR}} &amp;lt;/math&amp;gt;&lt;br /&gt;
; [[Negative likelihood ratio]] (LR-): &amp;lt;math&amp;gt;\mathrm{LR-} = \frac {\mathrm{FNR}} {\mathrm{TNR}} &amp;lt;/math&amp;gt;&lt;br /&gt;
; [[prevalence threshold]] (PT): &amp;lt;math&amp;gt;\mathrm{PT}= \frac{\sqrt{\mathrm{FPR}}}{\sqrt{\mathrm{TPR}} + \sqrt{\mathrm{FPR}}} &amp;lt;/math&amp;gt;&lt;br /&gt;
; threat score (TS) or critical success index (CSI): &amp;lt;math&amp;gt;\mathrm{TS} = \frac{\mathrm{TP}}{\mathrm{TP} + \mathrm{FN} + \mathrm{FP}}&amp;lt;/math&amp;gt;&lt;br /&gt;
----&lt;br /&gt;
; [[Prevalence]]: &amp;lt;math&amp;gt; \frac {\mathrm{P}} {\mathrm{P} + \mathrm{N}} &amp;lt;/math&amp;gt;&lt;br /&gt;
; [[accuracy]] (ACC): &amp;lt;math&amp;gt;\mathrm{ACC} = \frac {\mathrm{TP} + \mathrm{TN}} {\mathrm{P} + \mathrm{N}} = \frac {\mathrm{TP} + \mathrm{TN}} {\mathrm{TP} + \mathrm{TN} + \mathrm{FP} + \mathrm{FN}} &amp;lt;/math&amp;gt;&lt;br /&gt;
; balanced accuracy (BA): &amp;lt;math&amp;gt;\mathrm{BA} = \frac {TPR + TNR}{2} &amp;lt;/math&amp;gt;&lt;br /&gt;
; [[F1 score]]: is the [[Harmonic mean#Harmonic mean of two numbers|harmonic mean]] of [[Information retrieval#Precision|precision]] and [[sensitivity (test)|sensitivity]]: &amp;lt;math&amp;gt;\mathrm{F}_1 = 2 \times \frac {\mathrm{PPV} \times \mathrm{TPR}} {\mathrm{PPV} + \mathrm{TPR}} = \frac {2 \mathrm{TP}} {2 \mathrm{TP} + \mathrm{FP} + \mathrm{FN}}&amp;lt;/math&amp;gt;&lt;br /&gt;
; [[phi coefficient]] (φ or r&amp;lt;sub&amp;gt;φ&amp;lt;/sub&amp;gt;) or [[Matthews correlation coefficient]] (MCC): &amp;lt;math&amp;gt; \mathrm{MCC} = \frac{ \mathrm{TP} \times \mathrm{TN} - \mathrm{FP} \times \mathrm{FN} } {\sqrt{ (\mathrm{TP}+\mathrm{FP}) ( \mathrm{TP} + \mathrm{FN} ) ( \mathrm{TN} + \mathrm{FP} ) ( \mathrm{TN} + \mathrm{FN} ) } }&amp;lt;/math&amp;gt;&lt;br /&gt;
; [[Fowlkes–Mallows index]] (FM): &amp;lt;math&amp;gt; \mathrm{FM} = \sqrt{\frac {TP}{TP+FP} \times \frac{TP}{TP+FN}} = \sqrt{ PPV \times TPR }&amp;lt;/math&amp;gt;&lt;br /&gt;
; [[informedness]] or bookmaker informedness (BM): &amp;lt;math&amp;gt; \mathrm{BM} = \mathrm{TPR} + \mathrm{TNR} - 1&amp;lt;/math&amp;gt;&lt;br /&gt;
; [[markedness]] (MK) or deltaP (Δp): &amp;lt;math&amp;gt; \mathrm{MK} = \mathrm{PPV} + \mathrm{NPV} - 1&amp;lt;/math&amp;gt;&lt;br /&gt;
; [[Diagnostic odds ratio]] (DOR): &amp;lt;math&amp;gt; \mathrm{DOR} = \frac {\mathrm{LR+}} {\mathrm{LR-}} &amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;span style=&amp;quot;font-size:90%;&amp;quot;&amp;gt;''Sources:'' Fawcett (2006),&amp;lt;ref&amp;gt;{{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 |url=http://people.inf.elte.hu/kiss/11dwhdm/roc.pdf}}&amp;lt;/ref&amp;gt; Piryonesi and El-Diraby (2020),&amp;lt;ref&amp;gt;{{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}}&amp;lt;/ref&amp;gt;&lt;br /&gt;
Powers (2011),&amp;lt;ref&amp;gt;{{cite journal |first=David M. W. |last=Powers |date=2011 |title=Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness &amp;amp; Correlation |journal=Journal of Machine Learning Technologies |volume=2 |issue=1 |pages=37–63 |url=https://www.researchgate.net/publication/228529307}}&amp;lt;/ref&amp;gt; Ting (2011),&amp;lt;ref&amp;gt;{{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 }}&amp;lt;/ref&amp;gt; CAWCR,&amp;lt;ref&amp;gt;{{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}}&amp;lt;/ref&amp;gt; {{Nowrap|D. Chicco &amp;amp; G. Jurman (2020, 2021, 2023)}},&amp;lt;ref&amp;gt;{{cite journal |author=Chicco D. |author2=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}}&amp;lt;/ref&amp;gt;&amp;lt;ref&amp;gt;{{cite journal |author=Chicco D. |author2=Toetsch N. |author3=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 = 1-22 |pmid = 33541410 | pmc = 7863449 |doi = 10.1186/s13040-021-00244-z}}&amp;lt;/ref&amp;gt;&amp;lt;ref&amp;gt;{{cite journal |author = Chicco D. |author2 = Jurman G. | title = The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification | journal = BioData Mining | date = 2023 | volume = 16 | issue = 1 | doi = 10.1186/s13040-023-00322-4  | pmc = 9938573 | url = http://doi.org/10.1186/s13040-023-00322-4  |doi-access = free}}&amp;lt;/ref&amp;gt;  Tharwat (2018).&amp;lt;ref&amp;gt;{{cite journal |author = Tharwat A. |title = Classification assessment methods |journal = Applied Computing and Informatics |date = August 2018 |doi = 10.1016/j.aci.2018.08.003 |doi-access = free }}&amp;lt;/ref&amp;gt; Balayla (2020)&amp;lt;ref&amp;gt;{{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 |doi=10.1371/journal.pone.0240215 |url=https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0240215}}&amp;lt;/ref&amp;gt;&amp;lt;/span&amp;gt;&lt;br /&gt;
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