Precision and recall are two important metrics used to evaluate the performance of classification models, especially in scenarios where the classes are imbalanced.

Precision measures the accuracy of the positive predictions made by the model. It is defined as the number of true positives (correctly predicted positive cases) divided by the total number of positive predictions (true positives plus false positives). A high precision indicates that the model has a low false positive rate, meaning that when it predicts a positive case, it is likely to be correct.

Recall, on the other hand, measures the model’s ability to identify all relevant positive cases. It is defined as the number of true positives divided by the total number of actual positive cases (true positives plus false negatives). A high recall indicates that the model successfully captures most of the positive cases, but it may also lead to a higher false positive rate.

There is often a trade-off between precision and recall; improving one may lead to a decrease in the other. The F1 score, which is the harmonic mean of precision and recall, is often used to balance these two metrics.