A Strategy on Selecting Performance Metrics for Classifier Evaluation

This paper attempts to provide practitioners with a strategy on selecting performance metrics for classifier evaluation by investigating seven widely used performance metrics, namely classification accuracy, F-measure, kappa statistic, root mean square error, mean absolute error, the area under the receiver operating curve, and the areas under the precision-recall curve.