Performance Measures in Classification of Human Communications
نویسندگان
چکیده
This study emphasizes the importance of using appropriate measures in particular text classification settings. We focus on methods that evaluate how well a classifier performs. The effect of transformations on the confusion matrix are considered for eleven well-known and recently introduced classification measures. We analyze the measure’s ability to retain its value under changes in a confusion matrix. We discuss benefits from the use of the invariant and non-invariant measures with respect to characteristics of data classes.
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