Accuracy Evaluation of Neural Network Models on Real-World Data

Neural networks have achieved remarkable performance across a wide range of machine learning tasks; however, their accuracy often decreases significantly when deployed in real-world environments. This discrepancy arises due to noise, distribution shifts, heterogeneity, and temporal dynamics inherent in operational data. This study provides a structured analysis of methodologies for evaluating neural network accuracy on real-world datasets. We examine common pitfalls, discuss relevant metrics, review existing research, and propose a comprehensive evaluation framework that incorporates out-of-distribution analysis, robustness testing, calibration assessment, and continuous monitoring. Our findings demonstrate that traditional testing approaches are insufficient for assessing real-world model reliability and highlight the importance of multifaceted evaluation strategies to ensure trustworthy AI deployment.

Перевод пока недоступен