What is the difference between precision and recall?

Precision and recall are two important metrics used to evaluate the performance of a classification model. Precision measures the proportion of true positives among all predicted positives, while recall measures the proportion of true positives among all actual positives. Precision is a measure of how accurate the positive predictions are, while recall is a measure of how well the positive cases are identified. A high precision means that the model has a low false positive rate, while a high recall means that the model has a low false negative rate.