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        Further thoughts on precision

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        905746.pdf (PDF, 172Kb)
        Author
        Gray, D.
        Bowes, David
        Davey, Neil
        Sun, Yi
        Christianson, B.
        Attention
        2299/6362
        Abstract
        Background: There has been much discussion amongst automated software defect prediction researchers regarding use of the precision and false positive rate classifier performance metrics. Aim: To demonstrate and explain why failing to report precision when using data with highly imbalanced class distributions may provide an overly optimistic view of classifier performance. Method: Well documented examples of how dependent class distribution affects the suitability of performance measures. Conclusions: When using data where the minority class represents less than around 5 to 10 percent of data points in total, failing to report precision may be a critical mistake. Furthermore, deriving the precision values omitted from studies can reveal valuable insight into true classifier performance.
        Publication date
        2011
        Published in
        In: EASE 2011 the 15th International Conference on Evaluation and Assessment in Software Engineering
        Other links
        http://hdl.handle.net/2299/6362
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