ROC Curves for Continuous Data

By Wojtek Janusz Krzanowski, David J. Hand. 2009

660 citations recorded by the source corpus.

Subfields: Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/cab9848f517e2b328f4e338120423260909579d4

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In Environmental Engineering

1. A. Jiménez‐Valverde
2. Jesús Salgado
3. Raimundo Real
4. Bart Baesens
5. P. Acevedo

Citations in the corpus

• Discrimination capacity in species distribution models depends on the representativeness of the environmental domain (2013, methodology): “The area under the receiver operating characteristic (ROC) curve (AUC) has been a widely adopted statistic in measuring discrimination power (Hilden, 1991; Swets et al., 2000; Lobo et al., 2008; for extensive details on the ROC analysis see Krzanowski & Hand, 2009).”
• New insights into churn prediction in the telecommunication sector: A profit driven data mining approach (2012, methodology): “Then the AUC is defined as (Krzanowski and Hand, 2009): AUC ¼ Z 1 1 F0ðsÞf1ðsÞds: ð6Þ The AUC provides a simple figure-of-merit for the performance of the constructed classifier.”
• Transforming the Area under the Normal Curve (AUC) into Cohen’s d, Pearson’s rpb, Odds-Ratio, and Natural Log Odds-Ratio: Two Conversion Tables (2018, background): “…vigilance studies, forensic studies, law and human behavior studies, and, also, it is the basis for the estimates of the ROC curve when a normal distribution is assumed (Balaswany & Vardhan, 2015; Hasselblad & Hedges, 1995; Krzanowski & Hand, 2009; Marzban, 2004; Rice & Harris, 1995; Swets, 1973).”