F1 Score

The F1 score, a harmonic mean of precision and recall, is a widely used metric for evaluating the performance of classification models, particularly in tasks like music transcription, anomaly detection, and relation extraction. Current research focuses on improving F1 scores by addressing issues like instrument leakage in music transcription, developing threshold-independent measures that better reflect practical application, and accounting for realistic data scenarios in relation extraction, often employing deep learning models such as BERT and variations thereof. The F1 score's significance lies in its ability to provide a balanced assessment of model performance, guiding the development of more accurate and robust systems across diverse applications.