A response to Webb and Ting's on the application of ROC analysis to predict classification performance under varying class distributions

DOI10.1007/s10994-005-5256-4zbMath1469.68082DBLPjournals/ml/FawcettF05OpenAlexW2092509431WikidataQ58642783 ScholiaQ58642783MaRDI QIDQ1777416

Tom Fawcett, Peter A. Flach

Publication date: 13 May 2005

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-005-5256-4

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (8)

Drift mining in data: a framework for addressing drift in classification ⋮ Quantification-oriented learning based on reliable classifiers ⋮ An experimental comparison of cross-validation techniques for estimating the area under the ROC curve ⋮ Challenges in benchmarking stream learning algorithms with real-world data ⋮ Pointwise exact bootstrap distributions of ROC curves ⋮ On the study of nearest neighbor algorithms for prevalence estimation in binary problems ⋮ Unnamed Item ⋮ Unnamed Item

This page was built for publication: A response to Webb and Ting's on the application of ROC analysis to predict classification performance under varying class distributions