Multiclass Classification Performance Curve
Jesús S. Aguilar–Ruiz, Marcin Michalak
Abstract
Quality of predictive models is a critical factor. Many evaluation measures have been proposed for binary and multi–class datasets. However, less attention has been paid to graphical representation of the classification performance, where the ROC curve is extensively used for binary datasets but there is no standard method accepted by the scientific community for multi–class datasets. In this work, a multi–class classification performance (MCP) curve based on the Hellinger distance between true and prediction probabilities of the classifier is introduced. The MCP curve shows the classification performance, contributes to highlight the low or high confidence on correct predictions, and quantifies the quality by means of the area under the curve.