Performance comparison of supervised machine learning (ML) models are widely done in terms of different confusion matrix based scores obtained on test datasets. However, a dataset comprises several instances having different difficulty levels. Therefore, it is more logical to compare effectiveness of ML models on individual instances instead of comparing scores obtained for the entire dataset. In this paper, an alternative approach is proposed for direct comparison of supervised ML models in terms of individual instances within the dataset. A direct comparison matrix called \emph{Prayatul Matrix} is introduced, which accounts for comparative outcome of two ML algorithms on different instances of a dataset. Five different performance measures are designed based on prayatul matrix. Efficacy of the proposed approach as well as designed measures is analyzed with four classification techniques on three datasets. Also analyzed on four large-scale complex image datasets with four deep learning models namely ResNet50V2, MobileNetV2, EfficientNet, and XceptionNet. Results are evident that the newly designed measure are capable of giving more insight about the comparing ML algorithms, which were impossible with existing confusion matrix based scores like accuracy, precision and recall.

该论文通过引入一种直接比较个别数据集实例的监督机器学习模型方法，即Prayatul矩阵，提出了5种性能度量，以比较四种分类技术和四种常见的深度学习模型在多个数据集上的性能表现，结果表明新设计的度量能够在比较ML算法方面提供更多的洞察力，这是目前的混淆矩阵和准确性等现有指标所无法实现的。

Prayatul Matrix：直接对比评估监督机器学习模型性能的方法