CDQI: a new dataset evaluation index system

With the wide application of data-driven machine learning in various fields, the impact of data quality on model performance, reliability, and generalization ability has become increasingly prominent. High-quality datasets not only enhance the stability of model training and the accuracy of predictions but also support more effective feature utilization and modeling of complex tasks. However, existing dataset evaluation methods primarily focus on a single metric, such as prediction accuracy or annotation quality, lacking a comprehensive, multi-dimensional assessment of the dataset's overall quality. In response to this issue, this study proposes the Comprehensive Data Quality Index (CDQI), which uniformly quantifies the four dimensions of prediction performance (P), model complexity (C), mean feature information (MFE), and model robustness (R). To comprehensively evaluate the contribution of the dataset to model training. This paper first introduces the model performance indicators, feature information quantity measurement, and cross-validation methods, laying a theoretical foundation for the construction of CDQI. Subsequently, an empirical analysis was conducted on four types of classic datasets, and a visual comparison of CDQI and each index was carried out, accompanied by tables and radar charts. The experimental results demonstrate that CDQI can effectively distinguish between simple and complex datasets, accurately reflect the comprehensive performance of the model on various data, and provide a reliable basis for dataset selection, model optimization, and experimental design. The CDQI framework proposed in this study has good universality and can provide a reference for data quality assessment.