In the realm of machine learning (ML), recall serves as a vital metric for gauging the sensitivity of a classifier or predictor. It is computed as the ratio of the number of actual positive cases to the number of true positive predictions made by the classifier. Put simply, recall represents the percentage of genuinely positive instances that the classifier accurately identifies.
Recall holds immense importance in machine learning as it evaluates how effectively the classifier can recognize positive cases. It is commonly used in conjunction with another metric called precision, which is defined as the proportion of true positive predictions to the total number of positive predictions made by the classifier.
There exists a trade - off between precision and recall. An increase in recall might lead to a decrease in precision, and vice versa. This trade-off can be managed by adjusting the threshold parameter, which determines the minimum probability required for a prediction to be considered accurate.
In summary, recall, a key indicator of a classifier's or predictor's sensitivity in machine learning, is calculated as the ratio of true positive predictions to all real-world positive cases. It is a crucial parameter that, when combined with precision, helps in assessing the effectiveness of a classifier.