Abstract: The accuracy evaluation of multi-classification tasks has important theoretical significance and application value to the classification effect of the evaluation model. Aiming at multi-classification tasks in the field of machine learning, based on the current existing methods, this paper proposes a new method by expanding and migrating applications. In order to accurately evaluate the classification effect of the multi-classification task model, this paper introduces the remote sensing image classification effectiveness evaluation method (R′)  into the multi-classification tasks. In view of the actual characteristics of the multi-classification tasks, the method improves and popularizes the R′ method to better evaluate the performance of classifiers. The experimental results on the recognition task of MNIST handwritten character set and the classification task of the CIFAR-10 dataset show that, although the calculation is also based on the confusion matrix, compared with the existing evaluation indicators, the method can simultaneously give the overall classification performance of the classifiers and the classification efficiency of the individual categories, which can be beneficial to the training process. On the other hand, the method can be extended to the classification performance evaluation of any classification tasks, which has a good application prospect.

Key words: multi-classification, accuracy assessment, confusion matrix