Aggregating performance metrics for classifier evaluation

Abstract

There are several performance metrics that have been proposed for evaluating a classification model, e.g., accuracy, error rates, precision, recall, etc. While it is known that evaluating a classifier on only one performance metric is not advisable, the use of multiple performance metrics poses unique comparative challenges for the analyst. Since different performance metrics provide different perspectives into the classifier performance space, it is common for a learner to be relatively better on one performance metric and not better on another performance metric. We present a novel approach to aggregating several individual performance metrics into one metric, called the Relative Performance Metric (RPM). A large case study consisting of 35 real-world classification datasets, 12 classification algorithms, and 10 commonly used performance metrics illustrates the practical appeal of RPM. The empirical results clearly demonstrate the benefits of using RPM when classifier evaluation requires the consideration of a large number of individual performance metrics.