Label Free Model Evaluation

Label-free model evaluation aims to assess a model's performance on unseen data without requiring ground truth labels, addressing the significant cost and time associated with data annotation. Current research focuses on developing novel algorithms, such as those employing clustering techniques (e.g., k-means) and active learning strategies, to estimate model accuracy from unlabeled data by leveraging features like distribution shapes and cluster analysis or by strategically selecting a small subset for labeling. This field is crucial for improving the efficiency and scalability of model development and deployment across various domains, particularly where labeled data is scarce or expensive to obtain.