Smooth Receiver Operating Characteristics (smROC) CurvesWilliam Klement, Peter A. Flach, Nathalie Japkowicz, Stan Matwin, Smooth Receiver Operating Characteristics (smROC) Curves. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD'11). ISBN 978-3-642-23782-9, pp. 193–208. September 2011. No electronic version available. External information
Supervised learning algorithms perform common tasks including classification, ranking, scoring, and probability estimation. We investigate how scoring information, often produced by these models, is utilized by an evaluation measure. The ROC curve represents a visualization of the ranking performance of classifiers. However, they ignore the scores which can be quite informative. While this ignored information is less precise than that given by probabilities, it is much more detailed than that conveyed by ranking. This paper presents a novel method to weight the ROC curve by these scores. We call it the Smooth ROC (smROC) curve, and we demonstrate how it can be used to visualize the performance of learning models. We report experimental results to show that the smROC is appropriate for measuring performance similarities and differences between learning models, and is more sensitive to performance characteristics than the standard ROC curve.