An Alternative to ROC and AUC Analysis of Classifiers

Performance evaluation of classifiers is a crucial step for selecting the best classifier or the best set of parameters for a classifier. The misclassification rate of a classifier is often too simple because it does not take into account that misclassification for different classes might have more or less serious consequences. On the other hand, it is often difficult to specify exactly the consequences or costs of misclassifications. ROC and AUC analysis try to overcome these problems, but have their own disadvantages and even inconsistencies. We propose a visualisation technique for classifier performance evaluation and comparison that avoids the problems of ROC and AUC analysis.