Evaluation metrics differ on the basis of the nature of the problem . Evaluation metrics assess the performance of classification models, revealing their ability to distinguish between classes. Accuracy refers to the proportion of correctly classified instances to the total instances. Precision measures the proportion of correctly predicted positive instances among all predicted positives. Recall gauges the proportion of correctly predicted actual positive instances.