What are validation metrics in machine learning?

What are validation metrics in machine learning?

Validation metrics are measures used to evaluate the performance of a machine learning model on a validation dataset.

Can you give me some examples of validation metrics?

Sure! Common validation metrics include accuracy, precision, recall, F1 score, and ROC-AUC.

How do I choose the right validation metric for my model?

Choosing the right metric depends on your specific problem. For example, use accuracy for balanced classes and F1 score for imbalanced classes.

What is the difference between precision and recall?

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