Abstract
Different evaluation measures assess different characteristics of machine learning algorithms. The empirical evaluation of algorithms and classifiers is a matter of on-going debate among researchers. Most measures in use today focus on a classifier’s ability to identify classes correctly. We note other useful properties, such as failure avoidance or class discrimination, and we suggest measures to evaluate such properties. These measures – Youden’s index, likelihood, Discriminant power – are used in medical diagnosis. We show that they are interrelated, and we apply them to a case study from the field of electronic negotiations. We also list other learning problems which may benefit from the application of these measures.
We did this work while the first author was at the University of Ottawa. Partial support came from the Natural Sciences and Engineering Research Council of Canada.
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Sokolova, M., Japkowicz, N., Szpakowicz, S. (2006). Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_114
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DOI: https://doi.org/10.1007/11941439_114
Publisher Name: Springer, Berlin, Heidelberg
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