AUC of ROC是机器学习的社群最常使用来比较不同模型优劣的方法[2] 。然而近来这个做法开始受到质疑,因为有些机器学习的研究指出,AUC的杂讯太多,并且很常求不出可信又有效的AUC值(此时便不能保证AUC传达本节开头所述之意义),使得AUC在模型比较时产生的问题比解释的问题更多[3][4][5] 。
Lobo, Jorge M.; Jiménez-Valverde, Alberto; and Real, Raimundo (2008), AUC: a misleading measure of the performance of predictive distribution models, Global Ecology and Biogeography, 17: 145–151
Hand, David J. (2009); Measuring classifier performance: A coherent alternative to the area under the ROC curve, Machine Learning, 77: 103–123
来源
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Balakrishnan, N., (1991) Handbook of the Logistic Distribution, Marcel Dekker, Inc., ISBN 978-0824785871.
Gonen M., (2007) Analyzing Receiver Operating Characteristic Curves Using SAS, SAS Press, ISBN 978-1-59994-298-1.
Heagerty, P.J., Lumley, T., Pepe, M. S. (2000) Time-dependent ROC Curves for Censored Survival Data and a Diagnostic Marker Biometrics, 56:337–344
Hosmer, D.W. and Lemeshow, S., (2000) Applied Logistic Regression, 2nd ed., New York; Chichester, Wiley, ISBN 0-471-35632-8.
Brown, C.D., and Davis, H.T. (2006) Receiver operating characteristic curves and related decision measures: a tutorial, Chemometrics and Intelligent Laboratory Systems, 80:24–38
Mason, S.J. and Graham, N.E. (2002) Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation. Q.J.R. Meteorol. Soc., 128:2145–2166.
Pepe, M.S. (2003). The statistical evaluation of medical tests for classification and prediction. Oxford. ISBN 0198565828.
Carsten, S. Wesseling, S., Schink, T., and Jung, K. (2003) Comparison of Eight Computer Programs for Receiver-Operating Characteristic Analysis. Clinical Chemistry, 49:433–439
Swets, J.A. (1995). Signal detection theory and ROC analysis in psychology and diagnostics: Collected papers. Lawrence Erlbaum Associates.
Swets, J.A., Dawes, R., and Monahan, J. (2000) Better Decisions through Science. Scientific American, October, pages 82–87.
The magnificent ROC(页面存档备份,存于互联网档案馆) — An explanation and interactive demonstration of the connection of ROCs to archetypal bi-normal test result plots
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