Further thoughts on precision

D. Gray, David Bowes, N. Davey, Yi Sun, B. Christianson

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
101 Downloads (Pure)

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
Original languageEnglish
Pages (from-to)129-133
JournalIET Seminar Digest
Issue number1
DOIs
Publication statusPublished - 2011
EventProceedings of the 15th International Conference on Evaluation and Assessment in Software Engineering - Durham, United Kingdom
Duration: 11 Apr 201112 Apr 2011

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