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A Strategy on Selecting Performance Metrics for Classifier Evaluation

机译：选择绩效指标进行分类器评估的策略

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摘要

The evaluation of classifiers 'performances plays a critical role in construction and selection of classification model. Although many performance metrics have been proposed in machine learning community, no general guidelines are available among practitioners regarding which metric to be selected for evaluating a classifier s performance. In this paper, we attempt to provide practitioners with a strategy on selecting performance metrics for classifier evaluation. Firstly, the authors investigate 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 area under the precision-recall curve. Secondly, the authors resort to using Pearson linear correlation and Spearman rank correlation to analyses the potential relationship among these seven metrics. Experimental results show that these commonly used metrics can be divided into three groups, and all metrics within a given group are highly correlated but less correlated with metrics from different groups.
机译：分类器性能的评估在分类模型的构建和选择中起着至关重要的作用。尽管在机器学习社区中已经提出了许多性能指标，但是从业者之间没有通用的准则可用于选择哪个指标来评估分类器的性能。在本文中，我们尝试为从业人员提供选择绩效指标进行分类器评估的策略。首先，作者研究了七个广泛使用的性能指标，即分类准确性，F量度，kappa统计量，均方根误差，平均绝对误差，接收器工作曲线下的面积以及精确召回曲线下的面积。其次，作者诉诸于使用Pearson线性相关和Spearman秩相关来分析这七个指标之间的潜在关系。实验结果表明，这些常用指标可分为三组，并且给定组中的所有指标与不同组的指标高度相关，但相关性较低。

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