The F-Measure is a statistical metric used to evaluate the performance of a classification model. It combines two important measures: Precision and Recall. Precision indicates the accuracy of the positive predictions made by the model, while Recall measures the model's ability to identify all relevant instances. The F-Measure provides a single score that balances these two aspects, making it useful for assessing models, especially in cases with imbalanced datasets. The F-Measure is calculated using the formula: F = 2 \times \fracPrecision \times RecallPrecision + Recall . This score ranges from 0 to 1, where 1 indicates perfect precision and recall. It is commonly used in fields like machine learning and information retrieval to ensure that models perform well in identifying relevant data.