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Abstract: This paper is about constructing and evaluating pointwise confidence bounds on an ROC curve. We describe four confidencebound methods, two from the medical field and two used previously in machine learning research. We evaluate whether the bounds indeed contain the relevant operating point on the "true" ROC curve with a confidence of 1-#. We then evaluate pointwise confidence bounds on the region where the future performance of a model is expected to lie. For evaluation we use a synthetic ... (Update)
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BibTeX entry: (Update)
Macskassy, S., Provost, F., & Rosset, S. (2005). Pointwise ROC Confidence Bounds: An Empirical Evaluation. Proceedings of the Workshop on ROC Analysis in Machine Learning (ROCML-2005) at ICML-2005. http://citeseer.ist.psu.edu/macskassy05pointwise.html More
@misc{ macskassy05pointwise,
author = "S. Macskassy and F. Provost and S. Rosset",
title = "Pointwise ROC Confidence Bounds: An Empirical Evaluation",
text = "Macskassy, S., Provost, F., & Rosset, S. (2005). Pointwise ROC Confidence
Bounds: An Empirical Evaluation. Proceedings of the Workshop on ROC Analysis
in Machine Learning (ROCML-2005) at ICML-2005.",
year = "2005",
url = "citeseer.ist.psu.edu/macskassy05pointwise.html" }
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