Dataset Evaluation

Dataset evaluation focuses on assessing the quality and suitability of datasets used to train and evaluate machine learning models, aiming to ensure reliable and unbiased results. Current research emphasizes statistical measures of dataset reliability, difficulty, and validity, often employing model-agnostic frameworks and exploring the relationships between dataset characteristics and model performance. This work is crucial for improving the reproducibility and generalizability of machine learning research, impacting various fields by identifying and mitigating biases in datasets used for applications ranging from medical image analysis to natural language processing.