Recall, also known as sensitivity or true positive rate, is a key performance metric used to evaluate the effectiveness of a classification model in the field of artificial intelligence and machine learning. It quantifies the ability of a model to correctly identify all relevant instances within a dataset.
Mathematically, recall is defined as the ratio of true positive outcomes to the total number of actual positive instances. The formula for recall is:
Recall = True Positives / (True Positives + False Negatives)
In this formula:
- True Positives (TP) refer to the instances that are correctly classified as positive.
- False Negatives (FN) are the instances that are incorrectly classified as negative, despite being positive.
Recall is particularly important in scenarios where the cost of missing a positive instance is high, such as in medical diagnoses or fraud detection. A high recall score indicates that the model is effective at capturing most of the relevant instances, while a low score suggests that many positive instances are being overlooked.
However, it is essential to consider recall in conjunction with other metrics, such as precision (the ratio of true positives to the total predicted positives) and F1 score (the harmonic mean of precision and recall), to get a comprehensive understanding of a model’s performance. Balancing recall and precision is crucial, as focusing solely on maximizing recall may lead to a high number of false positives.