What is the F1 Score and why is it important?

What is the F1 Score and why is it important?

The F1 Score is a measure of a model's accuracy that considers both precision and recall. It's important because it provides a balance between the two, especially in cases where class distribution is uneven.

How is the F1 Score calculated?

The F1 Score is calculated using the formula: F1 = 2 * (precision * recall) / (precision + recall). It combines both metrics into a single score.

When should I use the F1 Score instead of accuracy?

You should use the F1 Score when you have imbalanced classes, as accuracy can be misleading in such cases. The F1 Score gives a better measure of the model's performance.

Can you give an example of when the F1 Score is particularly useful?

Sure! In medical diagnosis, where false negatives can be critical, the F1 Score helps ensure that both precision and recall are high, making it a better metric than accuracy alone.