2018
DOI: 10.5093/ejpalc2018a5
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Transforming the Area under the Normal Curve (AUC) into Cohen’s d, Pearson’s r pb , Odds-Ratio, and Natural Log Odds-Ratio: Two Conversion Tables

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Cited by 108 publications
(79 citation statements)
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“…and the typical metrics used in ML (classification accuracy, F1, AUC). Salgado (2018) addressed the problem of translating performance indicators from ML metrics and statistical metrics. It has been shown that the most used ML evaluation metrics can be mapped into effect size; for example, it has been shown that an AUC = 0.8 corresponds to a Cohen's d = 1.19.…”
Section: Comparing Statistical Inferences With Machine Learning Resultsmentioning
confidence: 99%
“…and the typical metrics used in ML (classification accuracy, F1, AUC). Salgado (2018) addressed the problem of translating performance indicators from ML metrics and statistical metrics. It has been shown that the most used ML evaluation metrics can be mapped into effect size; for example, it has been shown that an AUC = 0.8 corresponds to a Cohen's d = 1.19.…”
Section: Comparing Statistical Inferences With Machine Learning Resultsmentioning
confidence: 99%
“…Taking a quantitative perspective, there was a difference in the total score of risk factors in the CPVR, which was significantly (p = .000) higher in the judicial sample (M = 25.86, SD = 8.51) than in the clinical one (M = 14.25, SD = 7.94). According to AUC value, total score distinguished the type of group (AUC = .830,95% CI [.733,.926], p = .000), and the presence of injuries to mother (AUC = .764, 95% CI [.637, .891], p = .001) with a large effect size (Salgado, 2018). The best cutoff score for group classification and injuries to mothers was between scores of 22 and 23 as can be seen in Table 4.…”
Section: Quantitative Risk Assessmentmentioning
confidence: 99%
“…Given that the interpretation of effect sizes (i.e., small, medium, large) is not uniform within or between contexts, we calculated the associated percentile and the percentage of smaller effect sizes out of the total possible effect sizes (see Vilariño, Amado, Vázquez, & Arce, 2018). For the conversion of the sizes, we referred to the tables in Salgado (2018).…”
Section: •Data Analysismentioning
confidence: 99%