The F-Measure, also known as F1 Score, is a statistical measure used to assess the performance of a classification model, particularly in scenarios with imbalanced datasets. It combines two critical evaluation metrics: precision and recall.
Precision refers to the number of true positive results divided by the sum of true positive and false positive results. It indicates how many of the predicted positive instances are actually positive. Recall, on the other hand, is the number of true positive results divided by the sum of true positive and false negative results, measuring how many of the actual positive instances were correctly identified by the model.
The F-Measure is calculated using the formula:
F1 = 2 * (Precision * Recall) / (Precision + Recall)
This formula creates a single score that balances both precision and recall, providing a more comprehensive measure of a model’s accuracy than either metric alone. The F-Measure ranges from 0 to 1, where 1 indicates perfect precision and recall, and 0 indicates the worst performance.
F-Measure is particularly useful in applications such as information retrieval, medical diagnosis, and any other field where the cost of false positives and false negatives varies significantly. By focusing on both precision and recall, the F-Measure helps practitioners select models that are better suited to their specific needs and priorities.