What is the F1 score and how is it used for model evaluation?

What is the F1 score and how is it used for model evaluation?

The F1 score is a metric that combines precision and recall to evaluate a model's performance, especially in classification tasks.

Why is the F1 score important compared to accuracy?

The F1 score is crucial when dealing with imbalanced datasets, as it provides a better measure of a model's performance on minority classes.

How do you calculate the F1 score?

The F1 score is calculated using the formula: F1 = 2 * (precision * recall) / (precision + recall).

Can you give an example of when to use the F1 score?

Sure! The F1 score is particularly useful in scenarios like fraud detection, where false negatives can be costly.