Precision and recall are identical metrics used interchangeably in all classification problems.

Precision measures the accuracy of the positive predictions, while recall measures the model's ability to detect all positive instances.

Precision is used in scenarios where the cost of false positives is high, and recall is used when the cost of false negatives is high.

Both precision and recall are used only in multi-class classification problems.