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 model's ability to identify all relevant instances. The F1 score provides a single score that balances these two aspects, making it particularly useful when dealing with imbalanced datasets. The F1 score ranges from 0 to 1, where a score of 1 indicates perfect precision and recall. It is commonly used in various fields, including machine learning and natural language processing, to assess the effectiveness of models in tasks like spam detection and sentiment analysis.