F1 Score

The F1 Score is a statistical measure used to evaluate the performance of a classification model. It combines precision and recall into a single metric by calculating their harmonic mean. This score is particularly useful when the class distribution is imbalanced, as it provides a more comprehensive view of a model's accuracy than accuracy alone. The F1 Score ranges from 0 to 1, where 1 indicates perfect precision and recall. Common use cases include binary classification tasks in fields such as medical diagnosis, fraud detection, and sentiment analysis.