What Recall Meaning, Applications & Example
Metric measuring proportion of actual positives identified.
What is Recall?
Recall, also known as sensitivity or true positive rate, is a metric used to evaluate the performance of a classification model . It measures the proportion of actual positive cases that were correctly identified by the model. High recall indicates that the model successfully identifies most of the positive instances.
Formula for Recall
\[ \text{Recall} = \frac{\text{True Positives}}{\text{True Positives} + \text{False Negatives}} \]Applications of Recall
- Medical Diagnostics: Recall is important in applications like cancer detection, where identifying as many true positives (diseased patients) as possible is critical, even at the cost of some false positives.
- Fraud Detection: In financial systems, recall helps identify as many fraudulent transactions as possible.
- Spam Detection: Recall ensures that most spam emails are correctly classified, reducing the number of false negatives.
Example of Recall
In a disease detection model, if there are 100 patients with the disease, and the model identifies 90 of them correctly, the recall would be 90%. This means the model correctly detected 90% of the actual positive cases.