CORRELATION ANALYSIS OF PERFORMANCE METRICS FOR CLASSIFIER

The correct selection of performance metrics is one of the most key issues in evaluating classifier's performance. Although many performance metrics have been proposed and used in machine learning community, there is not any common conclusions among practitioners regarding which metric to choose for evaluating a classifier's performance. In this paper, we attempt to investigate the potential relationship among some common used performance metrics. Based on definitions, We first classify seven most widely performance metrics into three groups, namely threshold metrics, rank metrics, and probability metrics. Then, we focus on using Pearson linear correlation and Spearman rank correlation to investigate the relationship among these metrics. Experimental results show the reasonableness of classifying seven common used metrics into three groups. This can be useful for helping practitioners enhance understanding about the different relationships and groupings among the performance metrics.