What is the F1 Score and how is it used in practice?

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 explain how to calculate the F1 Score?

Sure! The F1 Score is calculated using the formula: F1 = 2 * (precision * recall) / (precision + recall). Precision is the ratio of true positives to the sum of true positives and false positives, while recall is the ratio of true positives to the sum of true positives and false negatives.

In what scenarios is the F1 Score more useful than accuracy?

The F1 Score is more useful than accuracy in scenarios where the class distribution is imbalanced, such as fraud detection or disease diagnosis, where false negatives can be more critical.

Can you give an example of a situation where the F1 Score would be preferred?

Absolutely! In a medical diagnosis scenario, if a model predicts whether a patient has a disease, a high F1 Score ensures that both false positives and false negatives are minimized, which is vital for patient safety.