What are the differences between evaluation metrics in machine learning?

What are the main differences between precision and recall?

Precision measures the accuracy of positive predictions, while recall measures the ability to find all relevant instances.

Can you explain F1 score and how it relates to precision and recall?

The F1 score is the harmonic mean of precision and recall, providing a balance between the two metrics.

What about accuracy? How does it differ from these metrics?

Accuracy measures the overall correctness of the model, but it can be misleading in imbalanced datasets.

When should I use ROC-AUC instead of precision and recall?

ROC-AUC is useful for evaluating models across different thresholds, especially in binary classification problems.