Recall is another essential metric used to evaluate the performance of a classification model. It measures the model’s ability to identify all relevant instances of the positive class. In simpler terms, recall answers the question:
- Out of all the actual positive cases, how many did the model correctly identify as positive?
The formula for recall is:
Where:
- TP (True Positives): Cases where the model correctly predicts the positive class.
- FN (False Negatives): Cases where the model incorrectly predicts the negative class when it is actually positive.
Importance of Recall
A high recall score indicates that the model successfully identifies most of the positive cases, which is crucial in scenarios where missing positives has serious consequences. For example:
- Medical Diagnosis: Failing to diagnose a patient with a critical condition could lead to severe outcomes.
- Spam Detection: Missing spam emails can clutter a user’s inbox with unwanted messages.
However, recall does not take into account the number of false positives, which is where Precision (Metric) plays a role. In practice, recall is often considered alongside precision to evaluate the model’s overall performance. The balance between precision and recall can be measured using the F1-score.