The F1 Score is a statistical measure used to evaluate the performance of a classification model. It combines two important metrics: precision and recall. Precision measures the accuracy of positive predictions, while recall assesses the ability to identify all relevant instances. The F1 Score provides a balance between these two metrics, making it especially useful when dealing with imbalanced datasets. The F1 Score ranges from 0 to 1, where 1 indicates perfect precision and recall. It is commonly used in various fields, including machine learning and natural language processing, to ensure that models perform well in identifying true positives while minimizing false positives and false negatives.