What is the F1 Score and can you provide examples?

What is the F1 Score and why is it important in machine learning?

The F1 Score is a metric that combines precision and recall, providing a balance between the two. It's crucial for evaluating models, especially in cases of imbalanced datasets.

Can you give me an example of how to calculate the F1 Score?

Sure! If you have 70 true positives, 10 false positives, and 20 false negatives, the precision is 0.875 and recall is 0.777. The F1 Score is then calculated as 2 * (precision * recall) / (precision + recall), which equals approximately 0.823.

What are some scenarios where the F1 Score is more useful than accuracy?

The F1 Score is particularly useful in scenarios like fraud detection or disease diagnosis, where false negatives are more critical than false positives.

Are there any limitations to using the F1 Score?

Yes, the F1 Score doesn't account for true negatives, so it may not provide a complete picture of model performance in all situations.