Threshold Calibration

Threshold calibration focuses on optimizing the decision boundary of a model to improve its performance, particularly in scenarios with imbalanced data or unseen classes. Recent research emphasizes developing adaptive methods, such as those employing graph neural networks or active learning strategies, to refine thresholds based on limited labeled data or even entirely unlabeled test sets. This is crucial for enhancing the reliability and accuracy of various machine learning tasks, including semi-supervised learning, open-world recognition, and knowledge graph completion, leading to more robust and effective models in diverse applications.