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    Estimating the uncertainty of average F1 scores

    Zhang, Dell and Wang, J. and Zhao, X. (2015) Estimating the uncertainty of average F1 scores. In: UNSPECIFIED (ed.) ICTIR '15: Proceedings of the 2015 International Conference on The Theory of Information Retrieval. New York, U.S.: ACM, pp. 317-320. ISBN 9781450338332.

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    In multi-class text classification, the performance (effectiveness) of a classifier is usually measured by micro-averaged and macro-averaged F1 scores. However, the scores themselves do not tell us how reliable they are in terms of forecasting the classifier's future performance on unseen data. In this paper, we propose a novel approach to explicitly modelling the uncertainty of average F1 scores through Bayesian reasoning.


    Item Type: Book Section
    Keyword(s) / Subject(s): Text Classification, Performance Evaluation, Bayesian Inference
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Research Centres and Institutes: Birkbeck Knowledge Lab, Data Analytics, Birkbeck Institute for
    Depositing User: Dr Dell Zhang
    Date Deposited: 11 Dec 2015 15:10
    Last Modified: 09 Aug 2023 12:37


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