What is the difference between AUC and F1 Score?

What is the difference between AUC and F1 Score?

AUC (Area Under the Curve) measures the performance of a classification model at all classification thresholds, while F1 Score is the harmonic mean of precision and recall, focusing on the balance between them.

When should I use AUC instead of F1 Score?

Use AUC when you want to evaluate the model's ability to distinguish between classes across all thresholds. F1 Score is better when you need to balance precision and recall, especially in imbalanced datasets.

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

Sure! In a medical diagnosis scenario where false negatives are critical, F1 Score helps ensure that both precision and recall are high, minimizing missed positive cases.

What are the limitations of using AUC?

AUC can be misleading in imbalanced datasets, as it may give a high score even if the model performs poorly on the minority class.