文件列表:

roc\auroc.m (2283, 2008-08-20)
roc\roc.m (2456, 2008-08-20)
roc\rocch.m (3027, 2008-08-20)
roc\rocdemo.m (2993, 2008-08-20)
roc (0, 2008-09-29)

Receiver Operating Characteristic (ROC) Curve Tools An ROC (Receiver Operating Characteristic) curve is a plot of the true positive rate as a function of the false positive rate of a classifier system as the score defining the decision threshold is varied. This toolkit implements some of the basic methods for constructing and processing ROC curves discussed by Fawcett [1] and Provost and Fawcett [2]. The area under the ROC curve is a reasonable performance statistic for classifier systems assuming no knowledge of the true ratio of misclassification costs. Downloads: m-file to compute ROC curve (roc.m) m-file to compute ROC convex hull (rocch.m) m-file to compute area under an ROC/ROCCH curve (auroc.m) demo/minimal test harness (rocdemo.m) References: [1] Fawcett, T., "ROC graphs : Notes and practical considerations for researchers", Technical report, HP Laboratories, MS 1143, 1501 Page Mill Road, Palo Alto CA 94304, USA, April 2004. [2] Provost, F. and Fawcett, T., "Robust classification for imprecise environments", Machine Learning, vol. 42, no. 3, pp. 203-231, 2001.

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