Die F-Messung, auch bekannt als F1-Score, is a statistical measure used to assess the performance of a classification model, particularly in scenarios with unausgewogene Datensätze. It combines two critical Bewertungsmetriken: precision and recall.
Präzision refers to the number of true positive results divided by the sum of true positive and falsch positive results. It indicates how many of the predicted positive instances are actually positive. Rückruf, 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.
Die F-Messung wird mit der Formel berechnet:
F1 = 2 * (Präzision * Recall) / (Präzision + 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.
Die F-Messung ist besonders nützlich in Anwendungen wie dem Informationsretrieval, 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.