Recall

What is Recall?

Recall is a metric used to evaluate a machine learning model. It can be seen as a way to quantify the quantity of predictions the model makes.

Recall is the proportion of actual positives (or True Positives) that were identified correctly. In other words, a model with high recall means that the predictions the model makes will include both relevant and irrelevant predictions. A model with low recall will only make a small number of predictions.

Recall = \frac{TP}{TP + FN}

Related Terms

• True Positive
• False Positive
• Precision
• Accuracy