Recall, also known as sensitivity or true positive rate, is a performance metric used in classification tasks within the field of data science and machine learning. It measures the ability of a model to correctly identify all relevant instances, or in other words, it calculates the ratio of true positive predictions to the total number of actual positive instances.

The formula for recall is given by:

Recall = True Positives / (True Positives + False Negatives)

• True Positives (TP): Instances that are actually positive and are correctly identified as positive by the model.

• False Negatives (FN): Instances that are actually positive but are incorrectly identified as negative by the model. Recall is particularly important in scenarios where the cost of missing positive instances (false negatives) is high. For example, in medical diagnoses, it is crucial to have high recall for identifying patients with a certain condition. A low recall means that the model is missing a significant number of positive cases, which could have serious consequences in applications like disease detection.

In general terms:

• High Recall: The model is effective at capturing most of the positive instances, minimizing false negatives.
• Low Recall: The model is missing a significant number of positive instances, leading to a high number of false negatives.

It's important to note that there is often a trade-off between precision and recall. Increasing one may come at the cost of the other, and finding the right balance depends on the specific goals and requirements of the task at hand. F1 score, which is the harmonic mean of precision and recall, is another metric that combines both aspects and is often used when there's a need to balance precision and recall in a single measure.