Robust Classification for Imprecise Environments
Machine Learning
Data Mining and Knowledge Discovery
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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In an article in this issue, Webb and Ting criticize ROC analysis for its inability to handle certain changes in class distributions. They imply that the ability of ROC graphs to depict performance in the face of changing class distributions has been overstated. In this editorial response, we describe two general types of domains and argue that Webb and Ting's concerns apply primarily to only one of them. Furthermore, we show that there are interesting real-world domains of the second type, in which ROC analysis may be expected to hold in the face of changing class distributions.