What does "F-measure" mean?

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F-measure is a way to evaluate how well a model is performing when it comes to finding the right answers, especially in fields like image analysis and medical diagnosis. It combines two important aspects: precision and recall.

• Precision looks at how many of the results from the model were correct. If the model says something is true, precision checks if that is actually true.

• Recall checks how many of the actual correct answers the model found. It’s about seeing if the model missed anything important.

By combining these two measures, F-measure gives a single score that helps to understand the overall effectiveness of the model. A higher F-measure means the model is doing a good job in identifying the right objects or conditions in images.